Simple Linear Regression vs. Multiple Linear Regression vs. MANOVA: A Data Scientist’s Guide

As a data scientist, it's important to understand the difference between simple linear regression, multiple linear regression, and MANOVA. This will come in handy when you're working with different datasets and trying to figure out which one to use. Here's a quick overview of each method:

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Simple Linear Regression vs. Multiple Linear Regression vs. MANOVA: A Data Scientist’s Guide

As a data scientist, it’s important to understand the difference between simple linear regression, multiple linear regression, and MANOVA. This will come in handy when you’re working with different datasets and trying to figure out which one to use. Here’s a quick overview of each method:

A Short Overview of Simple Linear Regression, Multiple Linear Regression, and MANOVA

Simple linear regression is used to predict the value of a dependent variable (y) based on the value of one independent variable (x). This is the most basic form of regression analysis.

Multiple linear regression is used to predict the value of a dependent variable (y) based on the values of two or more independent variables (x1, x2, x3, etc.). This is more complex than simple linear regression but can provide more accurate predictions.

MANOVA is used to predict the value of a dependent variable (y) based on the values of two or more independent variables (x1, x2, x3, etc.), while also taking into account the relationships between those variables. This is the most complex form of regression analysis but can provide the most accurate predictions.

So, which one should you use? It depends on your dataset and what you’re trying to predict. If you have a small dataset with only one independent variable, then simple linear regression will suffice. If you have a larger dataset with multiple independent variables, then multiple linear regression will be more appropriate. And if you need to take into account the relationships between your independent variables, then MANOVA is the way to go.

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In data science, there are a variety of techniques that can be used to model relationships between variables. Three of the most common techniques are simple linear regression, multiple linear regression, and MANOVA. Although these techniques may appear to be similar at first glance, there are actually some key differences that set them apart. Let’s take a closer look at each technique to see how they differ.

Simple Linear Regression

Simple linear regression is a statistical technique that can be used to model the relationship between a dependent variable and a single independent variable. The dependent variable is the variable that is being predicted, while the independent variable is the variable that is being used to make predictions.

Simple Linear Regression vs. Multiple Linear Regression vs. MANOVA: A Data Scientist's Guide
Linear Regression Basics for Absolute Beginners | by Benjamin Obi Tayo Ph.D. | Towards AI

Multiple Linear Regression

Multiple linear regression is a statistical technique that can be used to model the relationship between a dependent variable and two or more independent variables. As with simple linear regression, the dependent variable is the variable that is being predicted. However, in multiple linear regression, there can be multiple independent variables that are being used to make predictions.


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Simple Linear Regression vs. Multiple Linear Regression vs. MANOVA: A Data Scientist's Guide\
Multiple Linear Regression from scratch using only numpy | by Debidutta Dash | Analytics Vidhya | Medium

MANOVA

MANOVA (multivariate analysis of variance) is a statistical technique that can be used to model the relationship between a dependent variable and two or more independent variables. Unlike simple linear regression or multiple linear regression, MANOVA can only be used when the dependent variable is continuous. Additionally, MANOVA can only be used when there are two or more dependent variables.

Simple Linear Regression vs. Multiple Linear Regression vs. MANOVA: A Data Scientist's Guide
Simple Linear Regression vs. Multiple Linear Regression vs. MANOVA: A Data Scientist’s Guide

When it comes to data modeling, there are a variety of different techniques that can be used. Simple linear regression, multiple linear regression, and MANOVA are three of the most common techniques. Each technique has its own set of benefits and drawbacks that should be considered before deciding which technique to use for a particular project.We often encounter data points that are correlated. For example, the number of hours studied is correlated with the grades achieved. In such cases, we can use regression analysis to study the relationships between the variables.

Simple linear regression is a statistical method that allows us to predict the value of a dependent variable (y) based on the value of an independent variable (x). In other words, we can use simple linear regression to find out how much y will change when x changes.

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Multiple linear regression is a statistical method that allows us to predict the value of a dependent variable (y) based on the values of multiple independent variables (x1, x2, …, xn). In other words, we can use multiple linear regression to find out how much y will change when any of the independent variables changes.

Multivariate analysis of variance (MANOVA) is a statistical method that allows us to compare multiple dependent variables (y1, y2, …, yn) simultaneously. In other words, MANOVA can help us understand how multiple dependent variables vary together.

Simple Linear Regression vs Multiple Linear Regression vs MANOVA: A Comparative Study
The main difference between simple linear regression and multiple linear regression is that simple linear regression can be used to predict the value of a dependent variable based on the value of only one independent variable whereas multiple linear regression can be used to predict the value of a dependent variable based on the values of two or more independent variables. Another difference between simple linear regression and multiple linear regression is that simple linear regression is less likely to produce Type I and Type II errors than multiple linear regression.

Both simple linear regression and multiple linear regression are used to predict future values. However, MANOVA is used to understand how present values vary.

Conclusion:

In this article, we have seen the key differences between simple linear regression vs multiple linear regression vs MANOVA along with their applications. Simple linear regression should be used when there is only one predictor variable whereas multiple linear regressions should be used when there are two or more predictor variables. MANOVA should be used when there are two or more response variables. Hope you found this article helpful!

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Simple Linear Regression vs. Multiple Linear Regression vs. MANOVA: A Data Scientist’s Guide

As a data scientist, it’s important to understand the difference between simple linear regression, multiple linear regression, and MANOVA. This will come in handy when you’re working with different datasets and trying to figure out which one to use. Here’s a quick overview of each method:

A Short Overview of Simple Linear Regression, Multiple Linear Regression, and MANOVA

Simple linear regression is used to predict the value of a dependent variable (y) based on the value of one independent variable (x). This is the most basic form of regression analysis.

Multiple linear regression is used to predict the value of a dependent variable (y) based on the values of two or more independent variables (x1, x2, x3, etc.). This is more complex than simple linear regression but can provide more accurate predictions.

MANOVA is used to predict the value of a dependent variable (y) based on the values of two or more independent variables (x1, x2, x3, etc.), while also taking into account the relationships between those variables. This is the most complex form of regression analysis but can provide the most accurate predictions.

So, which one should you use? It depends on your dataset and what you’re trying to predict. If you have a small dataset with only one independent variable, then simple linear regression will suffice. If you have a larger dataset with multiple independent variables, then multiple linear regression will be more appropriate. And if you need to take into account the relationships between your independent variables, then MANOVA is the way to go.

In data science, there are a variety of techniques that can be used to model relationships between variables. Three of the most common techniques are simple linear regression, multiple linear regression, and MANOVA. Although these techniques may appear to be similar at first glance, there are actually some key differences that set them apart. Let’s take a closer look at each technique to see how they differ.

Simple Linear Regression

Simple linear regression is a statistical technique that can be used to model the relationship between a dependent variable and a single independent variable. The dependent variable is the variable that is being predicted, while the independent variable is the variable that is being used to make predictions.

Simple Linear Regression vs. Multiple Linear Regression vs. MANOVA: A Data Scientist's Guide
Linear Regression Basics for Absolute Beginners | by Benjamin Obi Tayo Ph.D. | Towards AI

Multiple Linear Regression

Multiple linear regression is a statistical technique that can be used to model the relationship between a dependent variable and two or more independent variables. As with simple linear regression, the dependent variable is the variable that is being predicted. However, in multiple linear regression, there can be multiple independent variables that are being used to make predictions.

Simple Linear Regression vs. Multiple Linear Regression vs. MANOVA: A Data Scientist's Guide\
Multiple Linear Regression from scratch using only numpy | by Debidutta Dash | Analytics Vidhya | Medium

MANOVA

MANOVA (multivariate analysis of variance) is a statistical technique that can be used to model the relationship between a dependent variable and two or more independent variables. Unlike simple linear regression or multiple linear regression, MANOVA can only be used when the dependent variable is continuous. Additionally, MANOVA can only be used when there are two or more dependent variables.

Simple Linear Regression vs. Multiple Linear Regression vs. MANOVA: A Data Scientist's Guide
Simple Linear Regression vs. Multiple Linear Regression vs. MANOVA: A Data Scientist’s Guide

When it comes to data modeling, there are a variety of different techniques that can be used. Simple linear regression, multiple linear regression, and MANOVA are three of the most common techniques. Each technique has its own set of benefits and drawbacks that should be considered before deciding which technique to use for a particular project.We often encounter data points that are correlated. For example, the number of hours studied is correlated with the grades achieved. In such cases, we can use regression analysis to study the relationships between the variables.

Simple linear regression is a statistical method that allows us to predict the value of a dependent variable (y) based on the value of an independent variable (x). In other words, we can use simple linear regression to find out how much y will change when x changes.

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Multiple linear regression is a statistical method that allows us to predict the value of a dependent variable (y) based on the values of multiple independent variables (x1, x2, …, xn). In other words, we can use multiple linear regression to find out how much y will change when any of the independent variables changes.

Multivariate analysis of variance (MANOVA) is a statistical method that allows us to compare multiple dependent variables (y1, y2, …, yn) simultaneously. In other words, MANOVA can help us understand how multiple dependent variables vary together.

Simple Linear Regression vs Multiple Linear Regression vs MANOVA: A Comparative Study
The main difference between simple linear regression and multiple linear regression is that simple linear regression can be used to predict the value of a dependent variable based on the value of only one independent variable whereas multiple linear regression can be used to predict the value of a dependent variable based on the values of two or more independent variables. Another difference between simple linear regression and multiple linear regression is that simple linear regression is less likely to produce Type I and Type II errors than multiple linear regression.

Both simple linear regression and multiple linear regression are used to predict future values. However, MANOVA is used to understand how present values vary.

Conclusion:

In this article, we have seen the key differences between simple linear regression vs multiple linear regression vs MANOVA along with their applications. Simple linear regression should be used when there is only one predictor variable whereas multiple linear regressions should be used when there are two or more predictor variables. MANOVA should be used when there are two or more response variables. Hope you found this article helpful!

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What is Problem Formulation in Machine Learning and Top 4 examples of Problem Formulation in Machine Learning?

Summary of Machine Learning and Artificial Intelligence Capabilities

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What is Problem Formulation in Machine Learning and Top 4 examples of Problem Formulation in Machine Learning?

Machine Learning (ML) is a field of Artificial Intelligence (AI) that enables computers to learn from data, without being explicitly programmed. Machine learning algorithms build models based on sample data, known as “training data”, in order to make predictions or decisions, rather than following rules written by humans. Machine learning is closely related to and often overlaps with computational statistics; a discipline that also focuses on prediction-making through the use of computers. Machine learning can be applied in a wide variety of domains, such as medical diagnosis, stock trading, robot control, manufacturing and more.

Problem Formulation in Machine Learning
What is Problem Formulation in Machine Learning and Top 4 examples of Problem Formulation in Machine Learning?

The process of machine learning consists of several steps: first, data is collected; then, a model is selected or created; finally, the model is trained on the collected data and then applied to new data. This process is often referred to as the “machine learning pipeline”. Problem formulation is the second step in this pipeline and it consists of selecting or creating a suitable model for the task at hand and determining how to represent the collected data so that it can be used by the selected model. In other words, problem formulation is the process of taking a real-world problem and translating it into a format that can be solved by a machine learning algorithm.

2023 AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams
2023 AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams

There are many different types of machine learning problems, such as classification, regression, prediction and so on. The choice of which type of problem to formulate depends on the nature of the task at hand and the type of data available. For example, if we want to build a system that can automatically detect fraudulent credit card transactions, we would formulate a classification problem. On the other hand, if our goal is to predict the sale price of houses given information about their size, location and age, we would formulate a regression problem. In general, it is best to start with a simple problem formulation and then move on to more complex ones if needed.

Some common examples of problem formulations in machine learning are:
Classification: given an input data point (e.g., an image), predict its category label (e.g., dog vs cat).
Regression: given an input data point (e.g., size and location of a house), predict a continuous output value (e.g., sale price).
Prediction: given an input sequence (e.g., a series of past stock prices), predict the next value in the sequence (e.g., future stock price).
Anomaly detection: given an input data point (e.g., transaction details), decide whether it is normal or anomalous (i.e., fraudulent).
Recommendation: given information about users (e.g., age and gender) and items (e.g., books and movies), recommend items to users (e.g., suggest books for someone who likes romance novels).
Optimization: given a set of constraints (e.g., budget) and objectives (e.g., maximize profit), find the best solution (e.g., product mix).

Machine Learning For Dummies
Machine Learning For Dummies

ML For Dummies on iOs

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Problem Formulation: What this pipeline phase entails and why it’s important

The problem formulation phase of the ML Pipeline is critical, and it’s where everything begins. Typically, this phase is kicked off with a question of some kind. Examples of these kinds of questions include: Could cars really drive themselves?  What additional product should we offer someone as they checkout? How much storage will clients need from a data center at a given time?

The problem formulation phase starts by seeing a problem and thinking “what question, if I could answer it, would provide the most value to my business?” If I knew the next product a customer was going to buy, is that most valuable? If I knew what was going to be popular over the holidays, is that most valuable? If I better understood who my customers are, is that most valuable?

However, some problems are not so obvious. When sales drop, new competitors emerge, or there’s a big change to a company/team/org, it can be easy to say, “I see the problem!” But sometimes the problem isn’t so clear. Consider self-driving cars. How many people think to themselves, “driving cars is a huge problem”? Probably not many. In fact, there isn’t a problem in the traditional sense of the word but there is an opportunity. Creating self-driving cars is a huge opportunity. That doesn’t mean there isn’t a problem or challenge connected to that opportunity. How do you design a self-driving system? What data would you look at to inform the decisions you make? Will people purchase self-driving cars?

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Part of the problem formulation phase includes seeing where there are opportunities to use machine learning.

In the following practice examples, you are presented with four different business scenarios. For each scenario, consider the following questions:

  1. Is machine learning appropriate for this problem, and why or why not?
  2. What is the ML problem if there is one, and what would a success metric look like?
  3. What kind of ML problem is this?
  4. Is the data appropriate?’

The solutions given in this article are one of the many ways you can formulate a business problem.

I)  Amazon recently began advertising to its customers when they visit the company website. The Director in charge of the initiative wants the advertisements to be as tailored to the customer as possible. You will have access to all the data from the retail webpage, as well as all the customer data.

  1. ML is appropriate because of the scale, variety and speed required. There are potentially thousands of ads and millions of customers that need to be served customized ads immediately as they arrive to the site.
  2. The problem is ads that are not useful to customers are a wasted opportunity and a nuisance to customers, yet not serving ads at all is a wasted opportunity. So how does Amazon serve the most relevant advertisements to its retail customers?
    1. Success would be the purchase of a product that was advertised.
  3. This is a supervised learning problem because we have a labeled data point, our success metric, which is the purchase of a product.
  4. This data is appropriate because it is both the retail webpage data as well as the customer data.

II) You’re a Senior Business Analyst at a social media company that focuses on streaming. Streamers use a combination of hashtags and predefined categories to be discoverable by your platform’s consumers. You ran an analysis on unique streamer counts by hashtags and categories over the last month and found that out of tens of thousands of streamers, almost all use only 40 hashtags and 10 categories despite innumerable hashtags and hundreds of categories. You presume the predefined categories don’t represent all the possibilities very well, and that streamers are simply picking the closest fit. You figure there are likely many categories and groupings of streamers that are not accounted for. So you collect a dataset that consists of all streamer profile descriptions (all text), all the historical chat information for each streamer, and all their videos that have been streamed.

  1. ML is appropriate because of the scale and variability.
  2. The problem is the content of streamers is not being represented by the existing categories. Success would be naturally grouping the streamers into categories based on content and seeing if those align with the hashtags and categories that are being commonly used.  If they do not, then the streamers are not being well represented and you can use these groupings to create new categories.
  3. There isn’t a specific outcome variable. There’s no target or label. So this is an unsupervised problem.
  4. The data is appropriate.

III) You’re a headphone manufacturer who sells directly to big and small electronic stores. As an attempt to increase competitive pricing, Store 1 and Store 2 decided to put together the pricing details for all headphone manufacturers and their products (about 350 products) and conduct daily releases of the data. You will have all the specs from each manufacturer and their product’s pricing. Your sales have recently been dropping so your first concern is whether there are competing products that are priced lower than your flagship product.

  1. ML is probably not necessary for this. You can just search the dataset to see which headphones are priced lower than the flagship, then compare their features and build quality.

IV) You’re a Senior Product Manager at a leading ridesharing company. You did some market research, collected customer feedback, and discovered that both customers and drivers are not happy with an app feature. This feature allows customers to place a pin exactly where they want to be picked up. The customers say drivers rarely stop at the pin location. Drivers say customers most often put the pin in a place they can’t stop. Your company has a relationship with the most used maps app for the driver’s navigation so you leverage this existing relationship to get direct, backend access to their data. This includes latitude and longitude, visual photos of each lat/long, traffic delay details, and regulation data if available (ie- No Parking zones, 3 minute parking zones, fire hydrants, etc.).

  1. ML is appropriate because of the scale and automation involved. It’s not feasible to drive everywhere and write down all the places that are ok for pickup. However, maybe we can predict whether a location is ok for pickup.
  2. The problem is drivers and customers are having poor experiences connecting for pickup, which is pushing customers away from the platform.
    1. Success would be properly identifying appropriate pickup locations so they can be integrated into the feature.
  3. This is a supervised learning problem even though there aren’t any labels, yet. Someone will have to go through a sample of the data to label where there are ok places to park and not park, giving the algorithms some target information.
  4. The data is appropriate once a sample of the dataset has been labeled. There may be some other data that could be included too. What about asking UPS for driver stop information? Where do they stop?

In conclusion, problem formulation is an important step in the machine learning pipeline that should not be overlooked or underestimated. It can make or break a machine learning project; therefore, it is important to take care when formulating machine learning problems.”

AWS machine Learning Specialty Exam Prep MLS-C01
AWS machine Learning Specialty Exam Prep MLS-C01

Step by Step Solution to a Machine Learning Problem – Feature Engineering

Feature Engineering is the act of reshaping and curating existing data to make patters more apparent. This process makes the data easier for an ML model to understand. Using knowledge of the data, features are engineered and  tuned to make ML algorithms work more efficiently.

 

For this problem, imagine a scenario where you are running a real estate brokerage and you want to predict the selling price of a house. Using a specific county dataset and simple information (like the location, total square footage, and number of bedrooms), let’s practice training a baseline model, conducting feature engineering, and tuning a model to make a prediction.

First, load the dataset and take a look at its basic properties.

# Load the dataset
import pandas as pd
import boto3

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df = pd.read_csv(“xxxxx_data_2.csv”)
df.head()

housing dataset example
housing dataset example: xxxxx_data_2.csv

Output:

feature_engineering_dataset_example
feature_engineering_dataset_example

This dataset has 21 columns:

  • id – Unique id number
  • date – Date of the house sale
  • price – Price the house sold for
  • bedrooms – Number of bedrooms
  • bathrooms – Number of bathrooms
  • sqft_living – Number of square feet of the living space
  • sqft_lot – Number of square feet of the lot
  • floors – Number of floors in the house
  • waterfront – Whether the home is on the waterfront
  • view – Number of lot sides with a view
  • condition – Condition of the house
  • grade – Classification by construction quality
  • sqft_above – Number of square feet above ground
  • sqft_basement – Number of square feet below ground
  • yr_built – Year built
  • yr_renovated – Year renovated
  • zipcode – ZIP code
  • lat – Latitude
  • long – Longitude
  • sqft_living15 – Number of square feet of living space in 2015 (can differ from sqft_living in the case of recent renovations)
  • sqrt_lot15 – Nnumber of square feet of lot space in 2015 (can differ from sqft_lot in the case of recent renovations)

This dataset is rich and provides a fantastic playground for the exploration of feature engineering. This exercise will focus on a small number of columns. If you are interested, you could return to this dataset later to practice feature engineering on the remaining columns.

A baseline model

Now, let’s  train a baseline model.

People often look at square footage first when evaluating a home. We will do the same in the oflorur model and ask how well can the cost of the house be approximated based on this number alone. We will train a simple linear learner model (documentation). We will compare to this after finishing the feature engineering.

import sagemaker
import numpy as np
from sklearn.model_selection import train_test_split
import time

t1 = time.time()

# Split training, validation, and test
ys = np.array(df[‘price’]).astype(“float32”)
xs = np.array(df[‘sqft_living’]).astype(“float32”).reshape(-1,1)

np.random.seed(8675309)
train_features, test_features, train_labels, test_labels = train_test_split(xs, ys, test_size=0.2)
val_features, test_features, val_labels, test_labels = train_test_split(test_features, test_labels, test_size=0.5)

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# Train model
linear_model = sagemaker.LinearLearner(role=sagemaker.get_execution_role(),
instance_count=1,
instance_type=’ml.m4.xlarge’,
predictor_type=’regressor’)

train_records = linear_model.record_set(train_features, train_labels, channel=’train’)
val_records = linear_model.record_set(val_features, val_labels, channel=’validation’)
test_records = linear_model.record_set(test_features, test_labels, channel=’test’)

linear_model.fit([train_records, val_records, test_records], logs=False)

sagemaker.analytics.TrainingJobAnalytics(linear_model._current_job_name, metric_names = [‘test:mse’, ‘test:absolute_loss’]).dataframe()

 

If you examine the quality metrics, you will see that the absolute loss is about $175,000.00. This tells us that the model is able to predict within an average of $175k of the true price. For a model based upon a single variable, this is not bad. Let’s try to do some feature engineering to improve on it.

Throughout the following work, we will constantly be adding to a dataframe called encoded. You will start by populating encoded with just the square footage you used previously.

 

encoded = df[[‘sqft_living’]].copy()

Categorical variables

Let’s start by including some categorical variables, beginning with simple binary variables.

The dataset has the waterfront feature, which is a binary variable. We should change the encoding from 'Y' and 'N' to 1 and 0. This can be done using the map function (documentation) provided by Pandas. It expects either a function to apply to that column or a dictionary to look up the correct transformation.

Binary categorical

Let’s write code to transform the waterfront variable into binary values. The skeleton has been provided below.

encoded[‘waterfront’] = df[‘waterfront’].map({‘Y’:1, ‘N’:0})

You can also encode many class categorical variables. Look at column condition, which gives a score of the quality of the house. Looking into the data source shows that the condition can be thought of as an ordinal categorical variable, so it makes sense to encode it with the order.

Ordinal categorical

Using the same method as in question 1, encode the ordinal categorical variable condition into the numerical range of 1 through 5.

encoded[‘condition’] = df[‘condition’].map({‘Poor’:1, ‘Fair’:2, ‘Average’:3, ‘Good’:4, ‘Very Good’:5})

A slightly more complex categorical variable is ZIP code. If you have worked with geospatial data, you may know that the full ZIP code is often too fine-grained to use as a feature on its own. However, there are only 7070 unique ZIP codes in this dataset, so we may use them.

However, we do not want to use unencoded ZIP codes. There is no reason that a larger ZIP code should correspond to a higher or lower price, but it is likely that particular ZIP codes would. This is the perfect case to perform one-hot encoding. You can use the get_dummies function (documentation) from Pandas to do this.

Nominal categorical

Using the Pandas get_dummies function,  add columns to one-hot encode the ZIP code and add it to the dataset.

encoded = pd.concat([encoded, pd.get_dummies(df[‘zipcode’])], axis=1)

In this way, you may freely encode whatever categorical variables you wish. Be aware that for categorical variables with many categories, something will need to be done to reduce the number of columns created.

One additional technique, which is simple but can be highly successful, involves turning the ZIP code into a single numerical column by creating a single feature that is the average price of a home in that ZIP code. This is called target encoding.

To do this, use groupby (documentation) and mean (documentation) to first group the rows of the DataFrame by ZIP code and then take the mean of each group. The resulting object can be mapped over the ZIP code column to encode the feature.

Nominal categorical II

Complete the following code snippet to provide a target encoding for the ZIP code.

means = df.groupby(‘zipcode’)[‘price’].mean()
encoded[‘zip_mean’] = df[‘zipcode’].map(means)

Normally, you only either one-hot encode or target encode. For this exercise, leave both in. In practice, you should try both, see which one performs better on a validation set, and then use that method.

Scaling

Take a look at the dataset. Print a summary of the encoded dataset using describe (documentation).

encoded.describe()

Scaling  - summary of the encoded dataset using describe
Scaling – summary of the encoded dataset using describe

One column ranges from 290290 to 1354013540 (sqft_living), another column ranges from 11 to 55 (condition), 7171 columns are all either 00 or 11 (one-hot encoded ZIP code), and then the final column ranges from a few hundred thousand to a couple million (zip_mean).

In a linear model, these will not be on equal footing. The sqft_living column will be approximately 1300013000 times easier for the model to find a pattern in than the other columns. To solve this, you often want to scale features to a standardized range. In this case, you will scale sqft_living to lie within 00 and 11.

Feature scaling

Fill in the code skeleton below to scale the column of the DataFrame to be between 00 and 11.

sqft_min = encoded[‘sqft_living’].min()
sqft_max = encoded[‘sqft_living’].max()
encoded[‘sqft_living’] = encoded[‘sqft_living’].map(lambda x : (x-sqft_min)/(sqft_max – sqft_min))

cond_min = encoded[‘condition’].min()
cond_max = encoded[‘condition’].max()
encoded[‘condition’] = encoded[‘condition’].map(lambda x : (x-cond_min)/(cond_max – cond_min))]

Read more here….

Amazon Reviews Solution

Predicting Credit Card Fraud Solution

Predicting Airplane Delays Solution

Data Processing for Machine Learning Example

Model Training and Evaluation Examples

Targeting Direct Marketing Solution

What are some good datasets for Data Science and Machine Learning?

What are some good datasets for Data Science and Machine Learning?

AI Dashboard is available on the Web, Apple, Google, and Microsoft, PRO version

What are some good datasets for Data Science and Machine Learning?

Finding good datasets for Data Science and Machine Learning can be a challenge. There are a lot of dataset out there, but not all of them are good for machine learning. In order to find a good dataset, you need to consider what you want to use the dataset for. If you want to use the dataset for training a machine learning model, then you need to make sure that the dataset is representative of the real-world data that you want to use the model on.

2023 AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams
2023 AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams

The dataset should also be large enough to train a robust model. Another important consideration is whether or not the dataset is open source. Open source datasets are typically better because they have been vetted by the community and are more likely to be of high quality. However, open source datasets can also be more difficult to find. A good place to start looking for datasets is on websites like Kaggle and UC Irvine Machine Learning Repository. These websites contain a variety of high-quality datasets that are free to download and use.

What are the Top 10 AWS jobs you can get with an AWS certification in 2022 plus AWS Interview Questions
AWS Data Analytics Specialty Certification Practice Exams

Researchers from IBM, MIT and Harvard Announced The Release Of DARPA “Common Sense AI” Dataset Along With Two Machine Learning Models At ICML 2021

Building machines that can make decisions based on common sense is no easy feat. A machine must be able to do more than merely find patterns in data; it also needs a way of interpreting the intentions and beliefs behind people’s choices.

At the 2021 International Conference on Machine Learning (ICML), Researchers from IBM, MIT, and Harvard University have come together to release a DARPA “Common Sense AI” dataset for benchmarking AI intuition. They are also releasing two machine learning models that represent different approaches to the problem that relies on testing techniques psychologists use to study infants’ behavior to accelerate the development of AI exhibiting common sense.

Person climbing a staircase. Learn Data Science from Scratch: online program with 21 courses

Source – Summary – Paper – IBM Blog

100 million protein structures Dataset by DeepMind

DeepMind creates ‘transformative’ map of human proteins drawn by AI. By the end of the year, DeepMind hopes to release predictions for 100 million protein structures, a dataset that will be “transformative for our understanding of how life works,

Get 20% off Google Google Workspace (Google Meet) Standard Plan with  the following codes: 96DRHDRA9J7GTN6
Get 20% off Google Workspace (Google Meet)  Business Plan (AMERICAS) with  the following codes:  C37HCAQRVR7JTFK Get 20% off Google Workspace (Google Meet) Business Plan (AMERICAS): M9HNXHX3WC9H7YE (Email us for more codes)

Active Anti-Aging Eye Gel, Reduces Dark Circles, Puffy Eyes, Crow's Feet and Fine Lines & Wrinkles, Packed with Hyaluronic Acid & Age Defying Botanicals

Here’s a good article about this topic

Earth’s population reaches 8 billion

Earth's population reaches 8 billion
Earth’s population reaches 8 billion

The most used words on every country’s Wikipedia Page

What are some good datasets for Data Science and Machine Learning?
The most used words on every country’s Wikipedia Page

Who works from home in 2022? Rates by industry 

Who works from home in 2022? Rates by industry
Who works from home in 2022? Rates by industry

Google Dataset Search

Google Dataset Search

Malware traffic dataset

Comprises 1914081 records created from all malware traffic analysis .net PCAP files, from 2013 to 2021. The logs are generated using Suricata and Zeek.

Originator: ali_alwashali


AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence (OpenAI, ChatGPT, Google Bard, Generative AI, Discriminative AI, xAI, LLMs, GPUs, Machine Learning, NLP, Promp Engineering)

Percent of “foreign-born” population in each US and EU state or country.

For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state 🇺🇸🇪🇺

Author: Here

Percent of “foreign-born” population in each US and EU state or country. For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state.

Examples of “foreign-born” in this context:

If you are looking for an all-in-one solution to help you prepare for the AWS Cloud Practitioner Certification Exam, look no further than this AWS Cloud Practitioner CCP CLF-C02 book

  • Person born in Spain and living in France is NOT “foreign-born”

  • Person born in Turkey and living in France is “foreign-born”

  • Person born in Florida and living in Texas is NOT “foreign-born”

  • Person born in Mexico and living in Texas is “foreign-born”

  • Person born in Florida and living in France is “foreign-born”

  • Person born in France and living in Florida is “foreign-born”

🇺🇸🇪🇺🗺️

Note: Poland, Ireland, Germany, Greece, Cyprus, Malta, Portugal uses Eurostat 2010 Migration data and Croatia has no data at all

Link1

Link2

Link3

Tools: MS Office

Source: Here

35% of “entry-level” jobs on LinkedIn require 3+ years of experience

What are some good datasets for Data Science and Machine Learning?
What are some good datasets for Data Science and Machine Learning?

Source: LinkedIn data  (see original post)

Tool: Photoshop from my colleague

Person climbing a staircase. Learn Data Science from Scratch: online program with 21 courses

Djamgatech: Build the skills that’ll drive your career into six figures: Get Djamgatech.

 
 

The Biggest Source of Power in Every US and Canadian State and Province 

The Biggest Source of Power in Every State and Province
The Biggest Source of Power in Every State and Province

Top 10 largest oil fields by 2021 production

Top 10 largest oil fields by 2021 production
Top 10 largest oil fields by 2021 production

Latest complete Netflix movie dataset

Created from 4 APIs. 11K+ rows and 30+ attributes of Netflix (Ratings, earnings, actors, language, availability, movie trailers, and many more)

Dataset on Kaggle.

Explore this dataset using FlixGem.com (this dataset is powering this webapp)

Dataset on Google Sheets.

The Largest Entertainment Streaming Companies
The Largest Entertainment Streaming Companies

The F word in Popular Movies

r/dataisbeautiful - [OC] The F word in Popular Movies

The easiest words to rhyme – Words that have the most rhymes

Post image

Common Crawl

A corpus of web crawl data composed of over 50 billion web pages. The Common Crawl corpus contains petabytes of data collected since 2008. It contains raw web page data, extracted metadata and text extractions.

AWS CLI Access (No AWS account required)

aws s3 ls s3://commoncrawl/ --no-sign-request

s3://commoncrawl/crawl-data/CC-MAIN-2021-17 – April 2021

 Dataset on protein prices

Ace the Microsoft Azure Fundamentals AZ-900 Certification Exam: Pass the Azure Fundamentals Exam with Ease

Data on Primary Commodity Prices are updated monthly based on the IMF’s Primary Commodity Price System.

Excel Database

 CPOST dataset on suicide attacks over four decades

The University of Chicago Project on Security and Threats presents the updated and expanded Database on Suicide Attacks (DSAT), which now links to Uppsala Conflict Data Program data on armed conflicts and includes a new dataset measuring the alliance and rivalry relationships among militant groups with connections to suicide attack groups. Access it here.

Credit Card Dataset – Survey of Consumer Finances (SCF) Combined Extract Data 1989-2019

You can do a lot of aggregated analysis in a pretty straightforward way there.

Drone imagery with annotations for small object detection and tracking dataset

11 TB dataset of drone imagery with annotations for small object detection and tracking

Download and more information are available here

Dataset License: CDLA-Sharing-1.0

Helper scripts for accessing the dataset: DATASET.md

Dataset Exploration: Colab

Suicide rate among countries with the highest Human Development Index

Suicide rate among countries with the highest Human Development Index
Suicide rate among countries with the highest Human Development Index

NOAA High-Resolution Rapid Refresh (HRRR) Model

The HRRR is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model, initialized by 3km grids with 3km radar assimilation. Radar data is assimilated in the HRRR every 15 min over a 1-h period adding further detail to that provided by the hourly data assimilation from the 13km radar-enhanced Rapid Refresh.

Registry of Open Data on AWS

This registry exists to help people discover and share datasets that are available via AWS resources. Learn more about sharing data on AWS.

See all usage examples for datasets listed in this registry.

See datasets from Digital Earth AfricaFacebook Data for GoodNASA Space Act AgreementNIH STRIDESNOAA Big Data ProgramSpace Telescope Science Institute, and Amazon Sustainability Data Initiative.

Textbook Question Answering (TQA)

1,076 textbook lessons, 26,260 questions, 6229 images

Documentation: allenai.org/data/tqa

Download

Harmonized Cancer Datasets: Genomic Data Commons Data Portal

The GDC Data Portal is a robust data-driven platform that allows cancer
researchers and bioinformaticians to search and download cancer data for analysis.

Genomic Data Commons Data Portal
Genomic Data Commons Data Portal

The Cancer Genome Atlas

The Cancer Genome Atlas (TCGA), a collaboration between the National Cancer Institute (NCI) and National Human Genome Research Institute (NHGRI), aims to generate comprehensive, multi-dimensional maps of the key genomic changes in major types and subtypes of cancer.

AWS CLI Access (No AWS account required)

aws s3 ls s3://tcga-2-open/ --no-sign-request

Therapeutically Applicable Research to Generate Effective Treatments (TARGET)

The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) program applies a comprehensive genomic approach to determine molecular changes that drive childhood cancers. The goal of the program is to use data to guide the development of effective, less toxic therapies. TARGET is organized into a collaborative network of disease-specific project teams.  TARGET projects provide comprehensive molecular characterization to determine the genetic changes that drive the initiation and progression of childhood cancers. The dataset contains open Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-Seq Isoform Expression Quantification, miRNA-Seq miRNA Expression Quantification data from Genomic Data Commons (GDC), and open data from GDC Legacy Archive. Access it here.

Genome Aggregation Database (gnomAD)

The Genome Aggregation Database (gnomAD) is a resource developed by an international coalition of investigators that aggregates and harmonizes both exome and genome data from a wide range of large-scale human sequencing projects. The summary data provided here are released for the benefit of the wider scientific community without restriction on use. Downloads

SQuAD (Stanford Question Answering Dataset)

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. Access it here.

PubMed Diabetes Dataset

The Pubmed Diabetes dataset consists of 19717 scientific publications from PubMed database pertaining to diabetes classified into one of three classes. The citation network consists of 44338 links. Each publication in the dataset is described by a TF/IDF weighted word vector from a dictionary which consists of 500 unique words. The README file in the dataset provides more details.

Download Link

Drug-Target Interaction Dataset

This dataset contains interactions between drugs and targets collected from DrugBank, KEGG Drug, DCDB, and Matador. It was originally collected by Perlman et al. It contains 315 drugs, 250 targets, 1,306 drug-target interactions, 5 types of drug-drug similarities, and 3 types of target-target similarities. Drug-drug similarities include Chemical-based, Ligand-based, Expression-based, Side-effect-based, and Annotation-based similarities. Target-target similarities include Sequence-based, Protein-protein interaction network-based, and Gene Ontology-based similarities. The original task on the dataset is to predict new interactions between drugs and targets based on different types of similarities in the network. Download link

Pharmacogenomics Datasets

PharmGKB data and knowledge is available as downloads. It is often critical to check with their curators at feedback@pharmgkb.org before embarking on a large project using these data, to be sure that the files and data they make available are being interpreted correctly. PharmGKB generally does NOT need to be a co-author on such analyses; They just want to make sure that there is a correct understanding of our data before lots of resources are spent.

Amazon Omics

Store, query, analyze, and generate insights from genomic and other omics data.

Amazon Omics
Amazon Omics

Pancreatic Cancer Organoid Profiling

The dataset contains open RNA-Seq Gene Expression Quantification data and controlled WGS/WXS/RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic Mutation, and RNA-Seq Splice Junction Quantification. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://gdc-organoid-pancreatic-phs001611-2-open/ --no-sign-request

 
 
Behshad Behzadi on LinkedIn: Partnering with iCAD to improve breast cancer screening
 

From AI Research to Real world Clinical Practice:
After a pivotal moment in 2020 to show our AI technology performed better than radiologists in a retrospective study at identifying signs of breast cancer, today a new important milestone is achieved: Google Health announces our first commercial agreement to license our mammography AI research model to be integrated in real-world clinical practice.

This can make healthcare AI to be more accessible and eventually saves more lives.

#ai #research #google #health #healthcare #breastcancer #mammography

Africa Soil Information Service (AfSIS) Soil Chemistry

This dataset contains soil infrared spectral data and paired soil property reference measurements for georeferenced soil samples that were collected through the Africa Soil Information Service (AfSIS) project, which lasted from 2009 through 2018. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://afsis/ --no-sign-request

Dataset for Affective States in E-Environments

DAiSEE is the first multi-label video classification dataset comprising of 9068 video snippets captured from 112 users for recognizing the user affective states of boredom, confusion, engagement, and frustration “in the wild”. The dataset has four levels of labels namely – very low, low, high, and very high for each of the affective states, which are crowd annotated and correlated with a gold standard annotation created using a team of expert psychologists. Download it here.

NatureServe Explorer Dataset

NatureServe Explorer provides conservation status, taxonomy, distribution, and life history information for more than 95,000 plants and animals in the United States and Canada, and more than 10,000 vegetation communities and ecological systems in the Western Hemisphere.

The data available through NatureServe Explorer represents data managed in the NatureServe Central Databases. These databases are dynamic, being continually enhanced and refined through the input of hundreds of natural heritage program scientists and other collaborators. NatureServe Explorer is updated from these central databases to reflect information from new field surveys, the latest taxonomic treatments and other scientific publications, and new conservation status assessments. Explore Data here

Flight Records in the US

Airline On-Time Performance and Causes of Flight Delays – On_Time Data.

This database contains scheduled and actual departure and arrival times, reason of delay. reported by certified U.S. air carriers that account for at least one percent of domestic scheduled passenger revenues. The data is collected by the Office of Airline Information, Bureau of Transportation Statistics (BTS).

FlightAware.com has data but you need to pay for a full dataset.

The anyflights package supplies a set of functions to generate air travel data (and data packages!) similar to nycflights13. With a user-defined year and airport, the anyflights function will grab data on:

  • flights: all flights that departed a given airport in a given year and month
  • weather: hourly meterological data for a given airport in a given year and month
  • airports: airport names, FAA codes, and locations
  • airlines: translation between two letter carrier (airline) codes and names
  • planes: construction information about each plane found in flights

Airline On-Time Statistics and Delay Causes

The U.S. Department of Transportation’s (DOT) Bureau of Transportation Statistics (BTS) tracks the on-time performance of domestic flights operated by large air carriers. Summary information on the number of on-time, delayed, canceled and diverted flights appears in DOT’s monthly Air Travel Consumer Report, published about 30 days after the month’s end, as well as in summary tables posted on this website. BTS began collecting details on the causes of flight delays in June 2003. Summary statistics and raw data are made available to the public at the time the Air Travel Consumer Report is released. Access it here

Worldwide flight data

Open flights: As of January 2017, the OpenFlights Airports Database contains over 10,000 airports, train stations and ferry terminals spanning the globe

Download: airports.dat (Airports only, high quality)

Download: airports-extended.dat (Airports, train stations and ferry terminals, including user contributions)

Bureau of Transportation:

Flightera.net seems to have a lot of good data for free. It has in-depth data on flights and doesn’t seem limited by date. I can’t speak on the validity of the data though.

flightradar24.com has lots of data, also historically, they might be willing to help you get it in a nice format.

 

2019 Crime statistics in the USA

Dataset with arrest in US by race and separate states. Download Excel here

Researchers from IBM, MIT and Harvard Announced The Release Of DARPA “Common Sense AI” Dataset Along With Two Machine Learning Models At ICML 2021

Building machines that can make decisions based on common sense is no easy feat. A machine must be able to do more than merely find patterns in data; it also needs a way of interpreting the intentions and beliefs behind people’s choices.

At the 2021 International Conference on Machine Learning (ICML), Researchers from IBM, MIT, and Harvard University have come together to release a DARPA “Common Sense AI” dataset for benchmarking AI intuition. They are also releasing two machine learning models that represent different approaches to the problem that relies on testing techniques psychologists use to study infants’ behavior to accelerate the development of AI exhibiting common sense.

Source – Summary – Paper – IBM Blog

100 million protein structures Dataset by DeepMind

DeepMind creates ‘transformative’ map of human proteins drawn by AI. By the end of the year, DeepMind hopes to release predictions for 100 million protein structures, a dataset that will be “transformative for our understanding of how life works,

Here’s a good article about this topic

Google Dataset Search

Google Dataset Search

Malware traffic dataset

Comprises 1914081 records created from all malware traffic analysis .net PCAP files, from 2013 to 2021. The logs are generated using Suricata and Zeek.

Originator: ali_alwashali

Percent of “foreign-born” population in each US and EU state or country.

For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state 🇺🇸🇪🇺

Author: Here

Percent of “foreign-born” population in each US and EU state or country. For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state.

Examples of “foreign-born” in this context:

  • Person born in Spain and living in France is NOT “foreign-born”

  • Person born in Turkey and living in France is “foreign-born”

  • Person born in Florida and living in Texas is NOT “foreign-born”

  • Person born in Mexico and living in Texas is “foreign-born”

  • Person born in Florida and living in France is “foreign-born”

  • Person born in France and living in Florida is “foreign-born”

🇺🇸🇪🇺🗺️

Note: Poland, Ireland, Germany, Greece, Cyprus, Malta, Portugal uses Eurostat 2010 Migration data and Croatia has no data at all

Link1

Link2

Link3

Tools: MS Office

Source: Here

35% of “entry-level” jobs on LinkedIn require 3+ years of experience

r/dataisbeautiful - [OC] 35% of "entry-level" jobs on LinkedIn require 3+ years of experience

Source: LinkedIn data  (see original post)

Tool: Photoshop from my colleague

Latest complete Netflix movie dataset

Created from 4 APIs. 11K+ rows and 30+ attributes of Netflix (Ratings, earnings, actors, language, availability, movie trailers, and many more)

Dataset on Kaggle.

Explore this dataset using FlixGem.com (this dataset is powering this webapp)

Dataset on Google Sheets.

Common Crawl

A corpus of web crawl data composed of over 50 billion web pages. The Common Crawl corpus contains petabytes of data collected since 2008. It contains raw web page data, extracted metadata and text extractions.

AWS CLI Access (No AWS account required)

aws s3 ls s3://commoncrawl/ --no-sign-request

s3://commoncrawl/crawl-data/CC-MAIN-2021-17 – April 2021

 Dataset on protein prices

Data on Primary Commodity Prices are updated monthly based on the IMF’s Primary Commodity Price System.

Excel Database

 CPOST dataset on suicide attacks over four decades

The University of Chicago Project on Security and Threats presents the updated and expanded Database on Suicide Attacks (DSAT), which now links to Uppsala Conflict Data Program data on armed conflicts and includes a new dataset measuring the alliance and rivalry relationships among militant groups with connections to suicide attack groups. Access it here.

Credit Card Dataset – Survey of Consumer Finances (SCF) Combined Extract Data 1989-2019

You can do a lot of aggregated analysis in a pretty straightforward way there.

Drone imagery with annotations for small object detection and tracking dataset

11 TB dataset of drone imagery with annotations for small object detection and tracking

Download and more information are available here

Dataset License: CDLA-Sharing-1.0

Helper scripts for accessing the dataset: DATASET.md

Dataset Exploration: Colab

NOAA High-Resolution Rapid Refresh (HRRR) Model

The HRRR is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model, initialized by 3km grids with 3km radar assimilation. Radar data is assimilated in the HRRR every 15 min over a 1-h period adding further detail to that provided by the hourly data assimilation from the 13km radar-enhanced Rapid Refresh.

Registry of Open Data on AWS

This registry exists to help people discover and share datasets that are available via AWS resources. Learn more about sharing data on AWS.

See all usage examples for datasets listed in this registry.

See datasets from Digital Earth AfricaFacebook Data for GoodNASA Space Act AgreementNIH STRIDESNOAA Big Data ProgramSpace Telescope Science Institute, and Amazon Sustainability Data Initiative.

Textbook Question Answering (TQA)

1,076 textbook lessons, 26,260 questions, 6229 images

Documentation: allenai.org/data/tqa

Download

Harmonized Cancer Datasets: Genomic Data Commons Data Portal

The GDC Data Portal is a robust data-driven platform that allows cancer
researchers and bioinformaticians to search and download cancer data for analysis.

Genomic Data Commons Data Portal
Genomic Data Commons Data Portal

The Cancer Genome Atlas

The Cancer Genome Atlas (TCGA), a collaboration between the National Cancer Institute (NCI) and National Human Genome Research Institute (NHGRI), aims to generate comprehensive, multi-dimensional maps of the key genomic changes in major types and subtypes of cancer.

AWS CLI Access (No AWS account required)

aws s3 ls s3://tcga-2-open/ --no-sign-request

Therapeutically Applicable Research to Generate Effective Treatments (TARGET)

The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) program applies a comprehensive genomic approach to determine molecular changes that drive childhood cancers. The goal of the program is to use data to guide the development of effective, less toxic therapies. TARGET is organized into a collaborative network of disease-specific project teams.  TARGET projects provide comprehensive molecular characterization to determine the genetic changes that drive the initiation and progression of childhood cancers. The dataset contains open Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-Seq Isoform Expression Quantification, miRNA-Seq miRNA Expression Quantification data from Genomic Data Commons (GDC), and open data from GDC Legacy Archive. Access it here.

Genome Aggregation Database (gnomAD)

The Genome Aggregation Database (gnomAD) is a resource developed by an international coalition of investigators that aggregates and harmonizes both exome and genome data from a wide range of large-scale human sequencing projects. The summary data provided here are released for the benefit of the wider scientific community without restriction on use. Downloads

SQuAD (Stanford Question Answering Dataset)

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. Access it here.

PubMed Diabetes Dataset

The Pubmed Diabetes dataset consists of 19717 scientific publications from PubMed database pertaining to diabetes classified into one of three classes. The citation network consists of 44338 links. Each publication in the dataset is described by a TF/IDF weighted word vector from a dictionary which consists of 500 unique words. The README file in the dataset provides more details.

Download Link

Drug-Target Interaction Dataset

This dataset contains interactions between drugs and targets collected from DrugBank, KEGG Drug, DCDB, and Matador. It was originally collected by Perlman et al. It contains 315 drugs, 250 targets, 1,306 drug-target interactions, 5 types of drug-drug similarities, and 3 types of target-target similarities. Drug-drug similarities include Chemical-based, Ligand-based, Expression-based, Side-effect-based, and Annotation-based similarities. Target-target similarities include Sequence-based, Protein-protein interaction network-based, and Gene Ontology-based similarities. The original task on the dataset is to predict new interactions between drugs and targets based on different types of similarities in the network. Download link

Pharmacogenomics Datasets

PharmGKB data and knowledge is available as downloads. It is often critical to check with their curators at feedback@pharmgkb.org before embarking on a large project using these data, to be sure that the files and data they make available are being interpreted correctly. PharmGKB generally does NOT need to be a co-author on such analyses; They just want to make sure that there is a correct understanding of our data before lots of resources are spent.

Pancreatic Cancer Organoid Profiling

The dataset contains open RNA-Seq Gene Expression Quantification data and controlled WGS/WXS/RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic Mutation, and RNA-Seq Splice Junction Quantification. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://gdc-organoid-pancreatic-phs001611-2-open/ --no-sign-request

Africa Soil Information Service (AfSIS) Soil Chemistry

This dataset contains soil infrared spectral data and paired soil property reference measurements for georeferenced soil samples that were collected through the Africa Soil Information Service (AfSIS) project, which lasted from 2009 through 2018. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://afsis/ --no-sign-request

Dataset for Affective States in E-Environments

DAiSEE is the first multi-label video classification dataset comprising of 9068 video snippets captured from 112 users for recognizing the user affective states of boredom, confusion, engagement, and frustration “in the wild”. The dataset has four levels of labels namely – very low, low, high, and very high for each of the affective states, which are crowd annotated and correlated with a gold standard annotation created using a team of expert psychologists. Download it here.

NatureServe Explorer Dataset

NatureServe Explorer provides conservation status, taxonomy, distribution, and life history information for more than 95,000 plants and animals in the United States and Canada, and more than 10,000 vegetation communities and ecological systems in the Western Hemisphere.

The data available through NatureServe Explorer represents data managed in the NatureServe Central Databases. These databases are dynamic, being continually enhanced and refined through the input of hundreds of natural heritage program scientists and other collaborators. NatureServe Explorer is updated from these central databases to reflect information from new field surveys, the latest taxonomic treatments and other scientific publications, and new conservation status assessments. Explore Data here

Flight Records in the US

Airline On-Time Performance and Causes of Flight Delays – On_Time Data.

This database contains scheduled and actual departure and arrival times, reason of delay. reported by certified U.S. air carriers that account for at least one percent of domestic scheduled passenger revenues. The data is collected by the Office of Airline Information, Bureau of Transportation Statistics (BTS).

FlightAware.com has data but you need to pay for a full dataset.

The anyflights package supplies a set of functions to generate air travel data (and data packages!) similar to nycflights13. With a user-defined year and airport, the anyflights function will grab data on:

  • flights: all flights that departed a given airport in a given year and month
  • weather: hourly meterological data for a given airport in a given year and month
  • airports: airport names, FAA codes, and locations
  • airlines: translation between two letter carrier (airline) codes and names
  • planes: construction information about each plane found in flights

Airline On-Time Statistics and Delay Causes

The U.S. Department of Transportation’s (DOT) Bureau of Transportation Statistics (BTS) tracks the on-time performance of domestic flights operated by large air carriers. Summary information on the number of on-time, delayed, canceled and diverted flights appears in DOT’s monthly Air Travel Consumer Report, published about 30 days after the month’s end, as well as in summary tables posted on this website. BTS began collecting details on the causes of flight delays in June 2003. Summary statistics and raw data are made available to the public at the time the Air Travel Consumer Report is released. Access it here

Worldwide flight data

Open flights: As of January 2017, the OpenFlights Airports Database contains over 10,000 airports, train stations and ferry terminals spanning the globe

Download: airports.dat (Airports only, high quality)

Download: airports-extended.dat (Airports, train stations and ferry terminals, including user contributions)

Bureau of Transportation:

Flightera.net seems to have a lot of good data for free. It has in-depth data on flights and doesn’t seem limited by date. I can’t speak on the validity of the data though.

flightradar24.com has lots of data, also historically, they might be willing to help you get it in a nice format.

 

2019 Crime statistics in the USA

Dataset with arrest in US by race and separate states. Download Excel here

Researchers from IBM, MIT and Harvard Announced The Release Of DARPA “Common Sense AI” Dataset Along With Two Machine Learning Models At ICML 2021

Building machines that can make decisions based on common sense is no easy feat. A machine must be able to do more than merely find patterns in data; it also needs a way of interpreting the intentions and beliefs behind people’s choices.

At the 2021 International Conference on Machine Learning (ICML), Researchers from IBM, MIT, and Harvard University have come together to release a DARPA “Common Sense AI” dataset for benchmarking AI intuition. They are also releasing two machine learning models that represent different approaches to the problem that relies on testing techniques psychologists use to study infants’ behavior to accelerate the development of AI exhibiting common sense.

Source – Summary – Paper – IBM Blog

100 million protein structures Dataset by DeepMind

DeepMind creates ‘transformative’ map of human proteins drawn by AI. By the end of the year, DeepMind hopes to release predictions for 100 million protein structures, a dataset that will be “transformative for our understanding of how life works,

Here’s a good article about this topic

Google Dataset Search

Google Dataset Search

Malware traffic dataset

Comprises 1914081 records created from all malware traffic analysis .net PCAP files, from 2013 to 2021. The logs are generated using Suricata and Zeek.

Originator: ali_alwashali

Percent of “foreign-born” population in each US and EU state or country.

For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state 🇺🇸🇪🇺

Author: Here

Percent of “foreign-born” population in each US and EU state or country. For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state.

Examples of “foreign-born” in this context:

  • Person born in Spain and living in France is NOT “foreign-born”

  • Person born in Turkey and living in France is “foreign-born”

  • Person born in Florida and living in Texas is NOT “foreign-born”

  • Person born in Mexico and living in Texas is “foreign-born”

  • Person born in Florida and living in France is “foreign-born”

  • Person born in France and living in Florida is “foreign-born”

🇺🇸🇪🇺🗺️

Note: Poland, Ireland, Germany, Greece, Cyprus, Malta, Portugal uses Eurostat 2010 Migration data and Croatia has no data at all

Link1

Link2

Link3

Tools: MS Office

Source: Here

35% of “entry-level” jobs on LinkedIn require 3+ years of experience

r/dataisbeautiful - [OC] 35% of "entry-level" jobs on LinkedIn require 3+ years of experience

Source: LinkedIn data  (see original post)

Tool: Photoshop from my colleague

Latest complete Netflix movie dataset

Created from 4 APIs. 11K+ rows and 30+ attributes of Netflix (Ratings, earnings, actors, language, availability, movie trailers, and many more)

Dataset on Kaggle.

Explore this dataset using FlixGem.com (this dataset is powering this webapp)

Dataset on Google Sheets.

Common Crawl

A corpus of web crawl data composed of over 50 billion web pages. The Common Crawl corpus contains petabytes of data collected since 2008. It contains raw web page data, extracted metadata and text extractions.

AWS CLI Access (No AWS account required)

aws s3 ls s3://commoncrawl/ --no-sign-request

s3://commoncrawl/crawl-data/CC-MAIN-2021-17 – April 2021

 Dataset on protein prices

Data on Primary Commodity Prices are updated monthly based on the IMF’s Primary Commodity Price System.

Excel Database

 CPOST dataset on suicide attacks over four decades

The University of Chicago Project on Security and Threats presents the updated and expanded Database on Suicide Attacks (DSAT), which now links to Uppsala Conflict Data Program data on armed conflicts and includes a new dataset measuring the alliance and rivalry relationships among militant groups with connections to suicide attack groups. Access it here.

Credit Card Dataset – Survey of Consumer Finances (SCF) Combined Extract Data 1989-2019

You can do a lot of aggregated analysis in a pretty straightforward way there.

Drone imagery with annotations for small object detection and tracking dataset

11 TB dataset of drone imagery with annotations for small object detection and tracking

Download and more information are available here

Dataset License: CDLA-Sharing-1.0

Helper scripts for accessing the dataset: DATASET.md

Dataset Exploration: Colab

NOAA High-Resolution Rapid Refresh (HRRR) Model

The HRRR is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model, initialized by 3km grids with 3km radar assimilation. Radar data is assimilated in the HRRR every 15 min over a 1-h period adding further detail to that provided by the hourly data assimilation from the 13km radar-enhanced Rapid Refresh.

Registry of Open Data on AWS

This registry exists to help people discover and share datasets that are available via AWS resources. Learn more about sharing data on AWS.

See all usage examples for datasets listed in this registry.

See datasets from Digital Earth AfricaFacebook Data for GoodNASA Space Act AgreementNIH STRIDESNOAA Big Data ProgramSpace Telescope Science Institute, and Amazon Sustainability Data Initiative.

Textbook Question Answering (TQA)

1,076 textbook lessons, 26,260 questions, 6229 images

Documentation: allenai.org/data/tqa

Download

Harmonized Cancer Datasets: Genomic Data Commons Data Portal

The GDC Data Portal is a robust data-driven platform that allows cancer
researchers and bioinformaticians to search and download cancer data for analysis.

Genomic Data Commons Data Portal
Genomic Data Commons Data Portal

The Cancer Genome Atlas

The Cancer Genome Atlas (TCGA), a collaboration between the National Cancer Institute (NCI) and National Human Genome Research Institute (NHGRI), aims to generate comprehensive, multi-dimensional maps of the key genomic changes in major types and subtypes of cancer.

AWS CLI Access (No AWS account required)

aws s3 ls s3://tcga-2-open/ --no-sign-request

Therapeutically Applicable Research to Generate Effective Treatments (TARGET)

The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) program applies a comprehensive genomic approach to determine molecular changes that drive childhood cancers. The goal of the program is to use data to guide the development of effective, less toxic therapies. TARGET is organized into a collaborative network of disease-specific project teams.  TARGET projects provide comprehensive molecular characterization to determine the genetic changes that drive the initiation and progression of childhood cancers. The dataset contains open Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-Seq Isoform Expression Quantification, miRNA-Seq miRNA Expression Quantification data from Genomic Data Commons (GDC), and open data from GDC Legacy Archive. Access it here.

Genome Aggregation Database (gnomAD)

The Genome Aggregation Database (gnomAD) is a resource developed by an international coalition of investigators that aggregates and harmonizes both exome and genome data from a wide range of large-scale human sequencing projects. The summary data provided here are released for the benefit of the wider scientific community without restriction on use. Downloads

SQuAD (Stanford Question Answering Dataset)

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. Access it here.

PubMed Diabetes Dataset

The Pubmed Diabetes dataset consists of 19717 scientific publications from PubMed database pertaining to diabetes classified into one of three classes. The citation network consists of 44338 links. Each publication in the dataset is described by a TF/IDF weighted word vector from a dictionary which consists of 500 unique words. The README file in the dataset provides more details.

Download Link

Drug-Target Interaction Dataset

This dataset contains interactions between drugs and targets collected from DrugBank, KEGG Drug, DCDB, and Matador. It was originally collected by Perlman et al. It contains 315 drugs, 250 targets, 1,306 drug-target interactions, 5 types of drug-drug similarities, and 3 types of target-target similarities. Drug-drug similarities include Chemical-based, Ligand-based, Expression-based, Side-effect-based, and Annotation-based similarities. Target-target similarities include Sequence-based, Protein-protein interaction network-based, and Gene Ontology-based similarities. The original task on the dataset is to predict new interactions between drugs and targets based on different types of similarities in the network. Download link

Pharmacogenomics Datasets

PharmGKB data and knowledge is available as downloads. It is often critical to check with their curators at feedback@pharmgkb.org before embarking on a large project using these data, to be sure that the files and data they make available are being interpreted correctly. PharmGKB generally does NOT need to be a co-author on such analyses; They just want to make sure that there is a correct understanding of our data before lots of resources are spent.

Pancreatic Cancer Organoid Profiling

The dataset contains open RNA-Seq Gene Expression Quantification data and controlled WGS/WXS/RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic Mutation, and RNA-Seq Splice Junction Quantification. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://gdc-organoid-pancreatic-phs001611-2-open/ --no-sign-request

Africa Soil Information Service (AfSIS) Soil Chemistry

This dataset contains soil infrared spectral data and paired soil property reference measurements for georeferenced soil samples that were collected through the Africa Soil Information Service (AfSIS) project, which lasted from 2009 through 2018. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://afsis/ --no-sign-request

Dataset for Affective States in E-Environments

DAiSEE is the first multi-label video classification dataset comprising of 9068 video snippets captured from 112 users for recognizing the user affective states of boredom, confusion, engagement, and frustration “in the wild”. The dataset has four levels of labels namely – very low, low, high, and very high for each of the affective states, which are crowd annotated and correlated with a gold standard annotation created using a team of expert psychologists. Download it here.

NatureServe Explorer Dataset

NatureServe Explorer provides conservation status, taxonomy, distribution, and life history information for more than 95,000 plants and animals in the United States and Canada, and more than 10,000 vegetation communities and ecological systems in the Western Hemisphere.

The data available through NatureServe Explorer represents data managed in the NatureServe Central Databases. These databases are dynamic, being continually enhanced and refined through the input of hundreds of natural heritage program scientists and other collaborators. NatureServe Explorer is updated from these central databases to reflect information from new field surveys, the latest taxonomic treatments and other scientific publications, and new conservation status assessments. Explore Data here

Flight Records in the US

Airline On-Time Performance and Causes of Flight Delays – On_Time Data.

This database contains scheduled and actual departure and arrival times, reason of delay. reported by certified U.S. air carriers that account for at least one percent of domestic scheduled passenger revenues. The data is collected by the Office of Airline Information, Bureau of Transportation Statistics (BTS).

FlightAware.com has data but you need to pay for a full dataset.

The anyflights package supplies a set of functions to generate air travel data (and data packages!) similar to nycflights13. With a user-defined year and airport, the anyflights function will grab data on:

  • flights: all flights that departed a given airport in a given year and month
  • weather: hourly meterological data for a given airport in a given year and month
  • airports: airport names, FAA codes, and locations
  • airlines: translation between two letter carrier (airline) codes and names
  • planes: construction information about each plane found in flights

Airline On-Time Statistics and Delay Causes

The U.S. Department of Transportation’s (DOT) Bureau of Transportation Statistics (BTS) tracks the on-time performance of domestic flights operated by large air carriers. Summary information on the number of on-time, delayed, canceled and diverted flights appears in DOT’s monthly Air Travel Consumer Report, published about 30 days after the month’s end, as well as in summary tables posted on this website. BTS began collecting details on the causes of flight delays in June 2003. Summary statistics and raw data are made available to the public at the time the Air Travel Consumer Report is released. Access it here

Worldwide flight data

Open flights: As of January 2017, the OpenFlights Airports Database contains over 10,000 airports, train stations and ferry terminals spanning the globe

Download: airports.dat (Airports only, high quality)

Download: airports-extended.dat (Airports, train stations and ferry terminals, including user contributions)

Bureau of Transportation:

Flightera.net seems to have a lot of good data for free. It has in-depth data on flights and doesn’t seem limited by date. I can’t speak on the validity of the data though.

flightradar24.com has lots of data, also historically, they might be willing to help you get it in a nice format.

 

2019 Crime statistics in the USA

Dataset with arrest in US by race and separate states. Download Excel here

Researchers from IBM, MIT and Harvard Announced The Release Of DARPA “Common Sense AI” Dataset Along With Two Machine Learning Models At ICML 2021

Building machines that can make decisions based on common sense is no easy feat. A machine must be able to do more than merely find patterns in data; it also needs a way of interpreting the intentions and beliefs behind people’s choices.

At the 2021 International Conference on Machine Learning (ICML), Researchers from IBM, MIT, and Harvard University have come together to release a DARPA “Common Sense AI” dataset for benchmarking AI intuition. They are also releasing two machine learning models that represent different approaches to the problem that relies on testing techniques psychologists use to study infants’ behavior to accelerate the development of AI exhibiting common sense.

Source – Summary – Paper – IBM Blog

100 million protein structures Dataset by DeepMind

DeepMind creates ‘transformative’ map of human proteins drawn by AI. By the end of the year, DeepMind hopes to release predictions for 100 million protein structures, a dataset that will be “transformative for our understanding of how life works,

Here’s a good article about this topic

Google Dataset Search

Google Dataset Search

Malware traffic dataset

Comprises 1914081 records created from all malware traffic analysis .net PCAP files, from 2013 to 2021. The logs are generated using Suricata and Zeek.

Originator: ali_alwashali

Percent of “foreign-born” population in each US and EU state or country.

For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state 🇺🇸🇪🇺

Author: Here

Percent of “foreign-born” population in each US and EU state or country. For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state.

Examples of “foreign-born” in this context:

  • Person born in Spain and living in France is NOT “foreign-born”

  • Person born in Turkey and living in France is “foreign-born”

  • Person born in Florida and living in Texas is NOT “foreign-born”

  • Person born in Mexico and living in Texas is “foreign-born”

  • Person born in Florida and living in France is “foreign-born”

  • Person born in France and living in Florida is “foreign-born”

🇺🇸🇪🇺🗺️

Note: Poland, Ireland, Germany, Greece, Cyprus, Malta, Portugal uses Eurostat 2010 Migration data and Croatia has no data at all

Link1

Link2

Link3

Tools: MS Office

Source: Here

35% of “entry-level” jobs on LinkedIn require 3+ years of experience

r/dataisbeautiful - [OC] 35% of "entry-level" jobs on LinkedIn require 3+ years of experience

Source: LinkedIn data  (see original post)

Tool: Photoshop from my colleague

Latest complete Netflix movie dataset

Created from 4 APIs. 11K+ rows and 30+ attributes of Netflix (Ratings, earnings, actors, language, availability, movie trailers, and many more)

Dataset on Kaggle.

Explore this dataset using FlixGem.com (this dataset is powering this webapp)

Dataset on Google Sheets.

Common Crawl

A corpus of web crawl data composed of over 50 billion web pages. The Common Crawl corpus contains petabytes of data collected since 2008. It contains raw web page data, extracted metadata and text extractions.

AWS CLI Access (No AWS account required)

aws s3 ls s3://commoncrawl/ --no-sign-request

s3://commoncrawl/crawl-data/CC-MAIN-2021-17 – April 2021

 Dataset on protein prices

Data on Primary Commodity Prices are updated monthly based on the IMF’s Primary Commodity Price System.

Excel Database

 CPOST dataset on suicide attacks over four decades

The University of Chicago Project on Security and Threats presents the updated and expanded Database on Suicide Attacks (DSAT), which now links to Uppsala Conflict Data Program data on armed conflicts and includes a new dataset measuring the alliance and rivalry relationships among militant groups with connections to suicide attack groups. Access it here.

Credit Card Dataset – Survey of Consumer Finances (SCF) Combined Extract Data 1989-2019

You can do a lot of aggregated analysis in a pretty straightforward way there.

Drone imagery with annotations for small object detection and tracking dataset

11 TB dataset of drone imagery with annotations for small object detection and tracking

Download and more information are available here

Dataset License: CDLA-Sharing-1.0

Helper scripts for accessing the dataset: DATASET.md

Dataset Exploration: Colab

NOAA High-Resolution Rapid Refresh (HRRR) Model

The HRRR is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model, initialized by 3km grids with 3km radar assimilation. Radar data is assimilated in the HRRR every 15 min over a 1-h period adding further detail to that provided by the hourly data assimilation from the 13km radar-enhanced Rapid Refresh.

Registry of Open Data on AWS

This registry exists to help people discover and share datasets that are available via AWS resources. Learn more about sharing data on AWS.

See all usage examples for datasets listed in this registry.

See datasets from Digital Earth AfricaFacebook Data for GoodNASA Space Act AgreementNIH STRIDESNOAA Big Data ProgramSpace Telescope Science Institute, and Amazon Sustainability Data Initiative.

Textbook Question Answering (TQA)

1,076 textbook lessons, 26,260 questions, 6229 images

Documentation: allenai.org/data/tqa

Download

Harmonized Cancer Datasets: Genomic Data Commons Data Portal

The GDC Data Portal is a robust data-driven platform that allows cancer
researchers and bioinformaticians to search and download cancer data for analysis.

Genomic Data Commons Data Portal
Genomic Data Commons Data Portal

The Cancer Genome Atlas

The Cancer Genome Atlas (TCGA), a collaboration between the National Cancer Institute (NCI) and National Human Genome Research Institute (NHGRI), aims to generate comprehensive, multi-dimensional maps of the key genomic changes in major types and subtypes of cancer.

AWS CLI Access (No AWS account required)

aws s3 ls s3://tcga-2-open/ --no-sign-request

Therapeutically Applicable Research to Generate Effective Treatments (TARGET)

The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) program applies a comprehensive genomic approach to determine molecular changes that drive childhood cancers. The goal of the program is to use data to guide the development of effective, less toxic therapies. TARGET is organized into a collaborative network of disease-specific project teams.  TARGET projects provide comprehensive molecular characterization to determine the genetic changes that drive the initiation and progression of childhood cancers. The dataset contains open Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-Seq Isoform Expression Quantification, miRNA-Seq miRNA Expression Quantification data from Genomic Data Commons (GDC), and open data from GDC Legacy Archive. Access it here.

Genome Aggregation Database (gnomAD)

The Genome Aggregation Database (gnomAD) is a resource developed by an international coalition of investigators that aggregates and harmonizes both exome and genome data from a wide range of large-scale human sequencing projects. The summary data provided here are released for the benefit of the wider scientific community without restriction on use. Downloads

SQuAD (Stanford Question Answering Dataset)

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. Access it here.

PubMed Diabetes Dataset

The Pubmed Diabetes dataset consists of 19717 scientific publications from PubMed database pertaining to diabetes classified into one of three classes. The citation network consists of 44338 links. Each publication in the dataset is described by a TF/IDF weighted word vector from a dictionary which consists of 500 unique words. The README file in the dataset provides more details.

Download Link

Drug-Target Interaction Dataset

This dataset contains interactions between drugs and targets collected from DrugBank, KEGG Drug, DCDB, and Matador. It was originally collected by Perlman et al. It contains 315 drugs, 250 targets, 1,306 drug-target interactions, 5 types of drug-drug similarities, and 3 types of target-target similarities. Drug-drug similarities include Chemical-based, Ligand-based, Expression-based, Side-effect-based, and Annotation-based similarities. Target-target similarities include Sequence-based, Protein-protein interaction network-based, and Gene Ontology-based similarities. The original task on the dataset is to predict new interactions between drugs and targets based on different types of similarities in the network. Download link

Pharmacogenomics Datasets

PharmGKB data and knowledge is available as downloads. It is often critical to check with their curators at feedback@pharmgkb.org before embarking on a large project using these data, to be sure that the files and data they make available are being interpreted correctly. PharmGKB generally does NOT need to be a co-author on such analyses; They just want to make sure that there is a correct understanding of our data before lots of resources are spent.

Pancreatic Cancer Organoid Profiling

The dataset contains open RNA-Seq Gene Expression Quantification data and controlled WGS/WXS/RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic Mutation, and RNA-Seq Splice Junction Quantification. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://gdc-organoid-pancreatic-phs001611-2-open/ --no-sign-request

Africa Soil Information Service (AfSIS) Soil Chemistry

This dataset contains soil infrared spectral data and paired soil property reference measurements for georeferenced soil samples that were collected through the Africa Soil Information Service (AfSIS) project, which lasted from 2009 through 2018. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://afsis/ --no-sign-request

Dataset for Affective States in E-Environments

DAiSEE is the first multi-label video classification dataset comprising of 9068 video snippets captured from 112 users for recognizing the user affective states of boredom, confusion, engagement, and frustration “in the wild”. The dataset has four levels of labels namely – very low, low, high, and very high for each of the affective states, which are crowd annotated and correlated with a gold standard annotation created using a team of expert psychologists. Download it here.

NatureServe Explorer Dataset

NatureServe Explorer provides conservation status, taxonomy, distribution, and life history information for more than 95,000 plants and animals in the United States and Canada, and more than 10,000 vegetation communities and ecological systems in the Western Hemisphere.

The data available through NatureServe Explorer represents data managed in the NatureServe Central Databases. These databases are dynamic, being continually enhanced and refined through the input of hundreds of natural heritage program scientists and other collaborators. NatureServe Explorer is updated from these central databases to reflect information from new field surveys, the latest taxonomic treatments and other scientific publications, and new conservation status assessments. Explore Data here

Flight Records in the US

Airline On-Time Performance and Causes of Flight Delays – On_Time Data.

This database contains scheduled and actual departure and arrival times, reason of delay. reported by certified U.S. air carriers that account for at least one percent of domestic scheduled passenger revenues. The data is collected by the Office of Airline Information, Bureau of Transportation Statistics (BTS).

FlightAware.com has data but you need to pay for a full dataset.

The anyflights package supplies a set of functions to generate air travel data (and data packages!) similar to nycflights13. With a user-defined year and airport, the anyflights function will grab data on:

  • flights: all flights that departed a given airport in a given year and month
  • weather: hourly meterological data for a given airport in a given year and month
  • airports: airport names, FAA codes, and locations
  • airlines: translation between two letter carrier (airline) codes and names
  • planes: construction information about each plane found in flights

Airline On-Time Statistics and Delay Causes

The U.S. Department of Transportation’s (DOT) Bureau of Transportation Statistics (BTS) tracks the on-time performance of domestic flights operated by large air carriers. Summary information on the number of on-time, delayed, canceled and diverted flights appears in DOT’s monthly Air Travel Consumer Report, published about 30 days after the month’s end, as well as in summary tables posted on this website. BTS began collecting details on the causes of flight delays in June 2003. Summary statistics and raw data are made available to the public at the time the Air Travel Consumer Report is released. Access it here

Worldwide flight data

Open flights: As of January 2017, the OpenFlights Airports Database contains over 10,000 airports, train stations and ferry terminals spanning the globe

Download: airports.dat (Airports only, high quality)

Download: airports-extended.dat (Airports, train stations and ferry terminals, including user contributions)

Bureau of Transportation:

Flightera.net seems to have a lot of good data for free. It has in-depth data on flights and doesn’t seem limited by date. I can’t speak on the validity of the data though.

flightradar24.com has lots of data, also historically, they might be willing to help you get it in a nice format.

 

2019 Crime statistics in the USA

Dataset with arrest in US by race and separate states. Download Excel here

Researchers from IBM, MIT and Harvard Announced The Release Of DARPA “Common Sense AI” Dataset Along With Two Machine Learning Models At ICML 2021

Building machines that can make decisions based on common sense is no easy feat. A machine must be able to do more than merely find patterns in data; it also needs a way of interpreting the intentions and beliefs behind people’s choices.

At the 2021 International Conference on Machine Learning (ICML), Researchers from IBM, MIT, and Harvard University have come together to release a DARPA “Common Sense AI” dataset for benchmarking AI intuition. They are also releasing two machine learning models that represent different approaches to the problem that relies on testing techniques psychologists use to study infants’ behavior to accelerate the development of AI exhibiting common sense.

Source – Summary – Paper – IBM Blog

100 million protein structures Dataset by DeepMind

DeepMind creates ‘transformative’ map of human proteins drawn by AI. By the end of the year, DeepMind hopes to release predictions for 100 million protein structures, a dataset that will be “transformative for our understanding of how life works,

Here’s a good article about this topic

Google Dataset Search

Google Dataset Search

Malware traffic dataset

Comprises 1914081 records created from all malware traffic analysis .net PCAP files, from 2013 to 2021. The logs are generated using Suricata and Zeek.

Originator: ali_alwashali

Percent of “foreign-born” population in each US and EU state or country.

For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state 🇺🇸🇪🇺

Author: Here

Percent of “foreign-born” population in each US and EU state or country. For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state.

Examples of “foreign-born” in this context:

  • Person born in Spain and living in France is NOT “foreign-born”

  • Person born in Turkey and living in France is “foreign-born”

  • Person born in Florida and living in Texas is NOT “foreign-born”

  • Person born in Mexico and living in Texas is “foreign-born”

  • Person born in Florida and living in France is “foreign-born”

  • Person born in France and living in Florida is “foreign-born”

🇺🇸🇪🇺🗺️

Note: Poland, Ireland, Germany, Greece, Cyprus, Malta, Portugal uses Eurostat 2010 Migration data and Croatia has no data at all

Link1

Link2

Link3

Tools: MS Office

Source: Here

35% of “entry-level” jobs on LinkedIn require 3+ years of experience

r/dataisbeautiful - [OC] 35% of "entry-level" jobs on LinkedIn require 3+ years of experience

Source: LinkedIn data  (see original post)

Tool: Photoshop from my colleague

Latest complete Netflix movie dataset

Created from 4 APIs. 11K+ rows and 30+ attributes of Netflix (Ratings, earnings, actors, language, availability, movie trailers, and many more)

Dataset on Kaggle.

Explore this dataset using FlixGem.com (this dataset is powering this webapp)

Dataset on Google Sheets.

Common Crawl

A corpus of web crawl data composed of over 50 billion web pages. The Common Crawl corpus contains petabytes of data collected since 2008. It contains raw web page data, extracted metadata and text extractions.

AWS CLI Access (No AWS account required)

aws s3 ls s3://commoncrawl/ --no-sign-request

s3://commoncrawl/crawl-data/CC-MAIN-2021-17 – April 2021

 Dataset on protein prices

Data on Primary Commodity Prices are updated monthly based on the IMF’s Primary Commodity Price System.

Excel Database

 CPOST dataset on suicide attacks over four decades

The University of Chicago Project on Security and Threats presents the updated and expanded Database on Suicide Attacks (DSAT), which now links to Uppsala Conflict Data Program data on armed conflicts and includes a new dataset measuring the alliance and rivalry relationships among militant groups with connections to suicide attack groups. Access it here.

Credit Card Dataset – Survey of Consumer Finances (SCF) Combined Extract Data 1989-2019

You can do a lot of aggregated analysis in a pretty straightforward way there.

Drone imagery with annotations for small object detection and tracking dataset

11 TB dataset of drone imagery with annotations for small object detection and tracking

Download and more information are available here

Dataset License: CDLA-Sharing-1.0

Helper scripts for accessing the dataset: DATASET.md

Dataset Exploration: Colab

NOAA High-Resolution Rapid Refresh (HRRR) Model

The HRRR is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model, initialized by 3km grids with 3km radar assimilation. Radar data is assimilated in the HRRR every 15 min over a 1-h period adding further detail to that provided by the hourly data assimilation from the 13km radar-enhanced Rapid Refresh.

Registry of Open Data on AWS

This registry exists to help people discover and share datasets that are available via AWS resources. Learn more about sharing data on AWS.

See all usage examples for datasets listed in this registry.

See datasets from Digital Earth AfricaFacebook Data for GoodNASA Space Act AgreementNIH STRIDESNOAA Big Data ProgramSpace Telescope Science Institute, and Amazon Sustainability Data Initiative.

Textbook Question Answering (TQA)

1,076 textbook lessons, 26,260 questions, 6229 images

Documentation: allenai.org/data/tqa

Download

Harmonized Cancer Datasets: Genomic Data Commons Data Portal

The GDC Data Portal is a robust data-driven platform that allows cancer
researchers and bioinformaticians to search and download cancer data for analysis.

Genomic Data Commons Data Portal
Genomic Data Commons Data Portal

The Cancer Genome Atlas

The Cancer Genome Atlas (TCGA), a collaboration between the National Cancer Institute (NCI) and National Human Genome Research Institute (NHGRI), aims to generate comprehensive, multi-dimensional maps of the key genomic changes in major types and subtypes of cancer.

AWS CLI Access (No AWS account required)

aws s3 ls s3://tcga-2-open/ --no-sign-request

Therapeutically Applicable Research to Generate Effective Treatments (TARGET)

The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) program applies a comprehensive genomic approach to determine molecular changes that drive childhood cancers. The goal of the program is to use data to guide the development of effective, less toxic therapies. TARGET is organized into a collaborative network of disease-specific project teams.  TARGET projects provide comprehensive molecular characterization to determine the genetic changes that drive the initiation and progression of childhood cancers. The dataset contains open Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-Seq Isoform Expression Quantification, miRNA-Seq miRNA Expression Quantification data from Genomic Data Commons (GDC), and open data from GDC Legacy Archive. Access it here.

Genome Aggregation Database (gnomAD)

The Genome Aggregation Database (gnomAD) is a resource developed by an international coalition of investigators that aggregates and harmonizes both exome and genome data from a wide range of large-scale human sequencing projects. The summary data provided here are released for the benefit of the wider scientific community without restriction on use. Downloads

SQuAD (Stanford Question Answering Dataset)

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. Access it here.

PubMed Diabetes Dataset

The Pubmed Diabetes dataset consists of 19717 scientific publications from PubMed database pertaining to diabetes classified into one of three classes. The citation network consists of 44338 links. Each publication in the dataset is described by a TF/IDF weighted word vector from a dictionary which consists of 500 unique words. The README file in the dataset provides more details.

Download Link

Drug-Target Interaction Dataset

This dataset contains interactions between drugs and targets collected from DrugBank, KEGG Drug, DCDB, and Matador. It was originally collected by Perlman et al. It contains 315 drugs, 250 targets, 1,306 drug-target interactions, 5 types of drug-drug similarities, and 3 types of target-target similarities. Drug-drug similarities include Chemical-based, Ligand-based, Expression-based, Side-effect-based, and Annotation-based similarities. Target-target similarities include Sequence-based, Protein-protein interaction network-based, and Gene Ontology-based similarities. The original task on the dataset is to predict new interactions between drugs and targets based on different types of similarities in the network. Download link

Pharmacogenomics Datasets

PharmGKB data and knowledge is available as downloads. It is often critical to check with their curators at feedback@pharmgkb.org before embarking on a large project using these data, to be sure that the files and data they make available are being interpreted correctly. PharmGKB generally does NOT need to be a co-author on such analyses; They just want to make sure that there is a correct understanding of our data before lots of resources are spent.

Pancreatic Cancer Organoid Profiling

The dataset contains open RNA-Seq Gene Expression Quantification data and controlled WGS/WXS/RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic Mutation, and RNA-Seq Splice Junction Quantification. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://gdc-organoid-pancreatic-phs001611-2-open/ --no-sign-request

Africa Soil Information Service (AfSIS) Soil Chemistry

This dataset contains soil infrared spectral data and paired soil property reference measurements for georeferenced soil samples that were collected through the Africa Soil Information Service (AfSIS) project, which lasted from 2009 through 2018. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://afsis/ --no-sign-request

Dataset for Affective States in E-Environments

DAiSEE is the first multi-label video classification dataset comprising of 9068 video snippets captured from 112 users for recognizing the user affective states of boredom, confusion, engagement, and frustration “in the wild”. The dataset has four levels of labels namely – very low, low, high, and very high for each of the affective states, which are crowd annotated and correlated with a gold standard annotation created using a team of expert psychologists. Download it here.

NatureServe Explorer Dataset

NatureServe Explorer provides conservation status, taxonomy, distribution, and life history information for more than 95,000 plants and animals in the United States and Canada, and more than 10,000 vegetation communities and ecological systems in the Western Hemisphere.

The data available through NatureServe Explorer represents data managed in the NatureServe Central Databases. These databases are dynamic, being continually enhanced and refined through the input of hundreds of natural heritage program scientists and other collaborators. NatureServe Explorer is updated from these central databases to reflect information from new field surveys, the latest taxonomic treatments and other scientific publications, and new conservation status assessments. Explore Data here

Flight Records in the US

Airline On-Time Performance and Causes of Flight Delays – On_Time Data.

This database contains scheduled and actual departure and arrival times, reason of delay. reported by certified U.S. air carriers that account for at least one percent of domestic scheduled passenger revenues. The data is collected by the Office of Airline Information, Bureau of Transportation Statistics (BTS).

FlightAware.com has data but you need to pay for a full dataset.

The anyflights package supplies a set of functions to generate air travel data (and data packages!) similar to nycflights13. With a user-defined year and airport, the anyflights function will grab data on:

  • flights: all flights that departed a given airport in a given year and month
  • weather: hourly meterological data for a given airport in a given year and month
  • airports: airport names, FAA codes, and locations
  • airlines: translation between two letter carrier (airline) codes and names
  • planes: construction information about each plane found in flights

Airline On-Time Statistics and Delay Causes

The U.S. Department of Transportation’s (DOT) Bureau of Transportation Statistics (BTS) tracks the on-time performance of domestic flights operated by large air carriers. Summary information on the number of on-time, delayed, canceled and diverted flights appears in DOT’s monthly Air Travel Consumer Report, published about 30 days after the month’s end, as well as in summary tables posted on this website. BTS began collecting details on the causes of flight delays in June 2003. Summary statistics and raw data are made available to the public at the time the Air Travel Consumer Report is released. Access it here

Worldwide flight data

Open flights: As of January 2017, the OpenFlights Airports Database contains over 10,000 airports, train stations and ferry terminals spanning the globe

Download: airports.dat (Airports only, high quality)

Download: airports-extended.dat (Airports, train stations and ferry terminals, including user contributions)

Bureau of Transportation:

Flightera.net seems to have a lot of good data for free. It has in-depth data on flights and doesn’t seem limited by date. I can’t speak on the validity of the data though.

flightradar24.com has lots of data, also historically, they might be willing to help you get it in a nice format.

 

2019 Crime statistics in the USA

Dataset with arrest in US by race and separate states. Download Excel here

Researchers from IBM, MIT and Harvard Announced The Release Of DARPA “Common Sense AI” Dataset Along With Two Machine Learning Models At ICML 2021

Building machines that can make decisions based on common sense is no easy feat. A machine must be able to do more than merely find patterns in data; it also needs a way of interpreting the intentions and beliefs behind people’s choices.

At the 2021 International Conference on Machine Learning (ICML), Researchers from IBM, MIT, and Harvard University have come together to release a DARPA “Common Sense AI” dataset for benchmarking AI intuition. They are also releasing two machine learning models that represent different approaches to the problem that relies on testing techniques psychologists use to study infants’ behavior to accelerate the development of AI exhibiting common sense.

Source – Summary – Paper – IBM Blog

100 million protein structures Dataset by DeepMind

DeepMind creates ‘transformative’ map of human proteins drawn by AI. By the end of the year, DeepMind hopes to release predictions for 100 million protein structures, a dataset that will be “transformative for our understanding of how life works,

Here’s a good article about this topic

Google Dataset Search

Google Dataset Search

Malware traffic dataset

Comprises 1914081 records created from all malware traffic analysis .net PCAP files, from 2013 to 2021. The logs are generated using Suricata and Zeek.

Originator: ali_alwashali

Percent of “foreign-born” population in each US and EU state or country.

For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state 🇺🇸🇪🇺

Author: Here

Percent of “foreign-born” population in each US and EU state or country. For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state.

Examples of “foreign-born” in this context:

  • Person born in Spain and living in France is NOT “foreign-born”

  • Person born in Turkey and living in France is “foreign-born”

  • Person born in Florida and living in Texas is NOT “foreign-born”

  • Person born in Mexico and living in Texas is “foreign-born”

  • Person born in Florida and living in France is “foreign-born”

  • Person born in France and living in Florida is “foreign-born”

🇺🇸🇪🇺🗺️

Note: Poland, Ireland, Germany, Greece, Cyprus, Malta, Portugal uses Eurostat 2010 Migration data and Croatia has no data at all

Link1

Link2

Link3

Tools: MS Office

Source: Here

35% of “entry-level” jobs on LinkedIn require 3+ years of experience

r/dataisbeautiful - [OC] 35% of "entry-level" jobs on LinkedIn require 3+ years of experience

Source: LinkedIn data  (see original post)

Tool: Photoshop from my colleague

Latest complete Netflix movie dataset

Created from 4 APIs. 11K+ rows and 30+ attributes of Netflix (Ratings, earnings, actors, language, availability, movie trailers, and many more)

Dataset on Kaggle.

Explore this dataset using FlixGem.com (this dataset is powering this webapp)

Dataset on Google Sheets.

Common Crawl

A corpus of web crawl data composed of over 50 billion web pages. The Common Crawl corpus contains petabytes of data collected since 2008. It contains raw web page data, extracted metadata and text extractions.

AWS CLI Access (No AWS account required)

aws s3 ls s3://commoncrawl/ --no-sign-request

s3://commoncrawl/crawl-data/CC-MAIN-2021-17 – April 2021

 Dataset on protein prices

Data on Primary Commodity Prices are updated monthly based on the IMF’s Primary Commodity Price System.

Excel Database

 CPOST dataset on suicide attacks over four decades

The University of Chicago Project on Security and Threats presents the updated and expanded Database on Suicide Attacks (DSAT), which now links to Uppsala Conflict Data Program data on armed conflicts and includes a new dataset measuring the alliance and rivalry relationships among militant groups with connections to suicide attack groups. Access it here.

Credit Card Dataset – Survey of Consumer Finances (SCF) Combined Extract Data 1989-2019

You can do a lot of aggregated analysis in a pretty straightforward way there.

Drone imagery with annotations for small object detection and tracking dataset

11 TB dataset of drone imagery with annotations for small object detection and tracking

Download and more information are available here

Dataset License: CDLA-Sharing-1.0

Helper scripts for accessing the dataset: DATASET.md

Dataset Exploration: Colab

NOAA High-Resolution Rapid Refresh (HRRR) Model

The HRRR is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model, initialized by 3km grids with 3km radar assimilation. Radar data is assimilated in the HRRR every 15 min over a 1-h period adding further detail to that provided by the hourly data assimilation from the 13km radar-enhanced Rapid Refresh.

Registry of Open Data on AWS

This registry exists to help people discover and share datasets that are available via AWS resources. Learn more about sharing data on AWS.

See all usage examples for datasets listed in this registry.

See datasets from Digital Earth AfricaFacebook Data for GoodNASA Space Act AgreementNIH STRIDESNOAA Big Data ProgramSpace Telescope Science Institute, and Amazon Sustainability Data Initiative.

Textbook Question Answering (TQA)

1,076 textbook lessons, 26,260 questions, 6229 images

Documentation: allenai.org/data/tqa

Download

Harmonized Cancer Datasets: Genomic Data Commons Data Portal

The GDC Data Portal is a robust data-driven platform that allows cancer
researchers and bioinformaticians to search and download cancer data for analysis.

Genomic Data Commons Data Portal
Genomic Data Commons Data Portal

The Cancer Genome Atlas

The Cancer Genome Atlas (TCGA), a collaboration between the National Cancer Institute (NCI) and National Human Genome Research Institute (NHGRI), aims to generate comprehensive, multi-dimensional maps of the key genomic changes in major types and subtypes of cancer.

AWS CLI Access (No AWS account required)

aws s3 ls s3://tcga-2-open/ --no-sign-request

Therapeutically Applicable Research to Generate Effective Treatments (TARGET)

The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) program applies a comprehensive genomic approach to determine molecular changes that drive childhood cancers. The goal of the program is to use data to guide the development of effective, less toxic therapies. TARGET is organized into a collaborative network of disease-specific project teams.  TARGET projects provide comprehensive molecular characterization to determine the genetic changes that drive the initiation and progression of childhood cancers. The dataset contains open Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-Seq Isoform Expression Quantification, miRNA-Seq miRNA Expression Quantification data from Genomic Data Commons (GDC), and open data from GDC Legacy Archive. Access it here.

Genome Aggregation Database (gnomAD)

The Genome Aggregation Database (gnomAD) is a resource developed by an international coalition of investigators that aggregates and harmonizes both exome and genome data from a wide range of large-scale human sequencing projects. The summary data provided here are released for the benefit of the wider scientific community without restriction on use. Downloads

SQuAD (Stanford Question Answering Dataset)

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. Access it here.

PubMed Diabetes Dataset

The Pubmed Diabetes dataset consists of 19717 scientific publications from PubMed database pertaining to diabetes classified into one of three classes. The citation network consists of 44338 links. Each publication in the dataset is described by a TF/IDF weighted word vector from a dictionary which consists of 500 unique words. The README file in the dataset provides more details.

Download Link

Drug-Target Interaction Dataset

This dataset contains interactions between drugs and targets collected from DrugBank, KEGG Drug, DCDB, and Matador. It was originally collected by Perlman et al. It contains 315 drugs, 250 targets, 1,306 drug-target interactions, 5 types of drug-drug similarities, and 3 types of target-target similarities. Drug-drug similarities include Chemical-based, Ligand-based, Expression-based, Side-effect-based, and Annotation-based similarities. Target-target similarities include Sequence-based, Protein-protein interaction network-based, and Gene Ontology-based similarities. The original task on the dataset is to predict new interactions between drugs and targets based on different types of similarities in the network. Download link

Pharmacogenomics Datasets

PharmGKB data and knowledge is available as downloads. It is often critical to check with their curators at feedback@pharmgkb.org before embarking on a large project using these data, to be sure that the files and data they make available are being interpreted correctly. PharmGKB generally does NOT need to be a co-author on such analyses; They just want to make sure that there is a correct understanding of our data before lots of resources are spent.

Pancreatic Cancer Organoid Profiling

The dataset contains open RNA-Seq Gene Expression Quantification data and controlled WGS/WXS/RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic Mutation, and RNA-Seq Splice Junction Quantification. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://gdc-organoid-pancreatic-phs001611-2-open/ --no-sign-request

Africa Soil Information Service (AfSIS) Soil Chemistry

This dataset contains soil infrared spectral data and paired soil property reference measurements for georeferenced soil samples that were collected through the Africa Soil Information Service (AfSIS) project, which lasted from 2009 through 2018. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://afsis/ --no-sign-request

Dataset for Affective States in E-Environments

DAiSEE is the first multi-label video classification dataset comprising of 9068 video snippets captured from 112 users for recognizing the user affective states of boredom, confusion, engagement, and frustration “in the wild”. The dataset has four levels of labels namely – very low, low, high, and very high for each of the affective states, which are crowd annotated and correlated with a gold standard annotation created using a team of expert psychologists. Download it here.

NatureServe Explorer Dataset

NatureServe Explorer provides conservation status, taxonomy, distribution, and life history information for more than 95,000 plants and animals in the United States and Canada, and more than 10,000 vegetation communities and ecological systems in the Western Hemisphere.

The data available through NatureServe Explorer represents data managed in the NatureServe Central Databases. These databases are dynamic, being continually enhanced and refined through the input of hundreds of natural heritage program scientists and other collaborators. NatureServe Explorer is updated from these central databases to reflect information from new field surveys, the latest taxonomic treatments and other scientific publications, and new conservation status assessments. Explore Data here

Flight Records in the US

Airline On-Time Performance and Causes of Flight Delays – On_Time Data.

This database contains scheduled and actual departure and arrival times, reason of delay. reported by certified U.S. air carriers that account for at least one percent of domestic scheduled passenger revenues. The data is collected by the Office of Airline Information, Bureau of Transportation Statistics (BTS).

FlightAware.com has data but you need to pay for a full dataset.

The anyflights package supplies a set of functions to generate air travel data (and data packages!) similar to nycflights13. With a user-defined year and airport, the anyflights function will grab data on:

  • flights: all flights that departed a given airport in a given year and month
  • weather: hourly meterological data for a given airport in a given year and month
  • airports: airport names, FAA codes, and locations
  • airlines: translation between two letter carrier (airline) codes and names
  • planes: construction information about each plane found in flights

Airline On-Time Statistics and Delay Causes

The U.S. Department of Transportation’s (DOT) Bureau of Transportation Statistics (BTS) tracks the on-time performance of domestic flights operated by large air carriers. Summary information on the number of on-time, delayed, canceled and diverted flights appears in DOT’s monthly Air Travel Consumer Report, published about 30 days after the month’s end, as well as in summary tables posted on this website. BTS began collecting details on the causes of flight delays in June 2003. Summary statistics and raw data are made available to the public at the time the Air Travel Consumer Report is released. Access it here

Worldwide flight data

Open flights: As of January 2017, the OpenFlights Airports Database contains over 10,000 airports, train stations and ferry terminals spanning the globe

Download: airports.dat (Airports only, high quality)

Download: airports-extended.dat (Airports, train stations and ferry terminals, including user contributions)

Bureau of Transportation:

Flightera.net seems to have a lot of good data for free. It has in-depth data on flights and doesn’t seem limited by date. I can’t speak on the validity of the data though.

flightradar24.com has lots of data, also historically, they might be willing to help you get it in a nice format.

 

2019 Crime statistics in the USA

Dataset with arrest in US by race and separate states. Download Excel here

Researchers from IBM, MIT and Harvard Announced The Release Of DARPA “Common Sense AI” Dataset Along With Two Machine Learning Models At ICML 2021

Building machines that can make decisions based on common sense is no easy feat. A machine must be able to do more than merely find patterns in data; it also needs a way of interpreting the intentions and beliefs behind people’s choices.

At the 2021 International Conference on Machine Learning (ICML), Researchers from IBM, MIT, and Harvard University have come together to release a DARPA “Common Sense AI” dataset for benchmarking AI intuition. They are also releasing two machine learning models that represent different approaches to the problem that relies on testing techniques psychologists use to study infants’ behavior to accelerate the development of AI exhibiting common sense.

Source – Summary – Paper – IBM Blog

100 million protein structures Dataset by DeepMind

DeepMind creates ‘transformative’ map of human proteins drawn by AI. By the end of the year, DeepMind hopes to release predictions for 100 million protein structures, a dataset that will be “transformative for our understanding of how life works,

Here’s a good article about this topic

Google Dataset Search

Google Dataset Search

Malware traffic dataset

Comprises 1914081 records created from all malware traffic analysis .net PCAP files, from 2013 to 2021. The logs are generated using Suricata and Zeek.

Originator: ali_alwashali

Percent of “foreign-born” population in each US and EU state or country.

For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state 🇺🇸🇪🇺

Author: Here

Percent of “foreign-born” population in each US and EU state or country. For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state.

Examples of “foreign-born” in this context:

  • Person born in Spain and living in France is NOT “foreign-born”

  • Person born in Turkey and living in France is “foreign-born”

  • Person born in Florida and living in Texas is NOT “foreign-born”

  • Person born in Mexico and living in Texas is “foreign-born”

  • Person born in Florida and living in France is “foreign-born”

  • Person born in France and living in Florida is “foreign-born”

🇺🇸🇪🇺🗺️

Note: Poland, Ireland, Germany, Greece, Cyprus, Malta, Portugal uses Eurostat 2010 Migration data and Croatia has no data at all

Link1

Link2

Link3

Tools: MS Office

Source: Here

35% of “entry-level” jobs on LinkedIn require 3+ years of experience

r/dataisbeautiful - [OC] 35% of "entry-level" jobs on LinkedIn require 3+ years of experience

Source: LinkedIn data  (see original post)

Tool: Photoshop from my colleague

Latest complete Netflix movie dataset

Created from 4 APIs. 11K+ rows and 30+ attributes of Netflix (Ratings, earnings, actors, language, availability, movie trailers, and many more)

Dataset on Kaggle.

Explore this dataset using FlixGem.com (this dataset is powering this webapp)

Dataset on Google Sheets.

Common Crawl

A corpus of web crawl data composed of over 50 billion web pages. The Common Crawl corpus contains petabytes of data collected since 2008. It contains raw web page data, extracted metadata and text extractions.

AWS CLI Access (No AWS account required)

aws s3 ls s3://commoncrawl/ --no-sign-request

s3://commoncrawl/crawl-data/CC-MAIN-2021-17 – April 2021

 Dataset on protein prices

Data on Primary Commodity Prices are updated monthly based on the IMF’s Primary Commodity Price System.

Excel Database

 CPOST dataset on suicide attacks over four decades

The University of Chicago Project on Security and Threats presents the updated and expanded Database on Suicide Attacks (DSAT), which now links to Uppsala Conflict Data Program data on armed conflicts and includes a new dataset measuring the alliance and rivalry relationships among militant groups with connections to suicide attack groups. Access it here.

Credit Card Dataset – Survey of Consumer Finances (SCF) Combined Extract Data 1989-2019

You can do a lot of aggregated analysis in a pretty straightforward way there.

Drone imagery with annotations for small object detection and tracking dataset

11 TB dataset of drone imagery with annotations for small object detection and tracking

Download and more information are available here

Dataset License: CDLA-Sharing-1.0

Helper scripts for accessing the dataset: DATASET.md

Dataset Exploration: Colab

NOAA High-Resolution Rapid Refresh (HRRR) Model

The HRRR is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model, initialized by 3km grids with 3km radar assimilation. Radar data is assimilated in the HRRR every 15 min over a 1-h period adding further detail to that provided by the hourly data assimilation from the 13km radar-enhanced Rapid Refresh.

Registry of Open Data on AWS

This registry exists to help people discover and share datasets that are available via AWS resources. Learn more about sharing data on AWS.

See all usage examples for datasets listed in this registry.

See datasets from Digital Earth AfricaFacebook Data for GoodNASA Space Act AgreementNIH STRIDESNOAA Big Data ProgramSpace Telescope Science Institute, and Amazon Sustainability Data Initiative.

Textbook Question Answering (TQA)

1,076 textbook lessons, 26,260 questions, 6229 images

Documentation: allenai.org/data/tqa

Download

Harmonized Cancer Datasets: Genomic Data Commons Data Portal

The GDC Data Portal is a robust data-driven platform that allows cancer
researchers and bioinformaticians to search and download cancer data for analysis.

Genomic Data Commons Data Portal
Genomic Data Commons Data Portal

The Cancer Genome Atlas

The Cancer Genome Atlas (TCGA), a collaboration between the National Cancer Institute (NCI) and National Human Genome Research Institute (NHGRI), aims to generate comprehensive, multi-dimensional maps of the key genomic changes in major types and subtypes of cancer.

AWS CLI Access (No AWS account required)

aws s3 ls s3://tcga-2-open/ --no-sign-request

Therapeutically Applicable Research to Generate Effective Treatments (TARGET)

The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) program applies a comprehensive genomic approach to determine molecular changes that drive childhood cancers. The goal of the program is to use data to guide the development of effective, less toxic therapies. TARGET is organized into a collaborative network of disease-specific project teams.  TARGET projects provide comprehensive molecular characterization to determine the genetic changes that drive the initiation and progression of childhood cancers. The dataset contains open Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-Seq Isoform Expression Quantification, miRNA-Seq miRNA Expression Quantification data from Genomic Data Commons (GDC), and open data from GDC Legacy Archive. Access it here.

Genome Aggregation Database (gnomAD)

The Genome Aggregation Database (gnomAD) is a resource developed by an international coalition of investigators that aggregates and harmonizes both exome and genome data from a wide range of large-scale human sequencing projects. The summary data provided here are released for the benefit of the wider scientific community without restriction on use. Downloads

SQuAD (Stanford Question Answering Dataset)

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. Access it here.

PubMed Diabetes Dataset

The Pubmed Diabetes dataset consists of 19717 scientific publications from PubMed database pertaining to diabetes classified into one of three classes. The citation network consists of 44338 links. Each publication in the dataset is described by a TF/IDF weighted word vector from a dictionary which consists of 500 unique words. The README file in the dataset provides more details.

Download Link

Drug-Target Interaction Dataset

This dataset contains interactions between drugs and targets collected from DrugBank, KEGG Drug, DCDB, and Matador. It was originally collected by Perlman et al. It contains 315 drugs, 250 targets, 1,306 drug-target interactions, 5 types of drug-drug similarities, and 3 types of target-target similarities. Drug-drug similarities include Chemical-based, Ligand-based, Expression-based, Side-effect-based, and Annotation-based similarities. Target-target similarities include Sequence-based, Protein-protein interaction network-based, and Gene Ontology-based similarities. The original task on the dataset is to predict new interactions between drugs and targets based on different types of similarities in the network. Download link

Pharmacogenomics Datasets

PharmGKB data and knowledge is available as downloads. It is often critical to check with their curators at feedback@pharmgkb.org before embarking on a large project using these data, to be sure that the files and data they make available are being interpreted correctly. PharmGKB generally does NOT need to be a co-author on such analyses; They just want to make sure that there is a correct understanding of our data before lots of resources are spent.

Pancreatic Cancer Organoid Profiling

The dataset contains open RNA-Seq Gene Expression Quantification data and controlled WGS/WXS/RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic Mutation, and RNA-Seq Splice Junction Quantification. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://gdc-organoid-pancreatic-phs001611-2-open/ --no-sign-request

Africa Soil Information Service (AfSIS) Soil Chemistry

This dataset contains soil infrared spectral data and paired soil property reference measurements for georeferenced soil samples that were collected through the Africa Soil Information Service (AfSIS) project, which lasted from 2009 through 2018. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://afsis/ --no-sign-request

Dataset for Affective States in E-Environments

DAiSEE is the first multi-label video classification dataset comprising of 9068 video snippets captured from 112 users for recognizing the user affective states of boredom, confusion, engagement, and frustration “in the wild”. The dataset has four levels of labels namely – very low, low, high, and very high for each of the affective states, which are crowd annotated and correlated with a gold standard annotation created using a team of expert psychologists. Download it here.

NatureServe Explorer Dataset

NatureServe Explorer provides conservation status, taxonomy, distribution, and life history information for more than 95,000 plants and animals in the United States and Canada, and more than 10,000 vegetation communities and ecological systems in the Western Hemisphere.

The data available through NatureServe Explorer represents data managed in the NatureServe Central Databases. These databases are dynamic, being continually enhanced and refined through the input of hundreds of natural heritage program scientists and other collaborators. NatureServe Explorer is updated from these central databases to reflect information from new field surveys, the latest taxonomic treatments and other scientific publications, and new conservation status assessments. Explore Data here

Flight Records in the US

Airline On-Time Performance and Causes of Flight Delays – On_Time Data.

This database contains scheduled and actual departure and arrival times, reason of delay. reported by certified U.S. air carriers that account for at least one percent of domestic scheduled passenger revenues. The data is collected by the Office of Airline Information, Bureau of Transportation Statistics (BTS).

FlightAware.com has data but you need to pay for a full dataset.

The anyflights package supplies a set of functions to generate air travel data (and data packages!) similar to nycflights13. With a user-defined year and airport, the anyflights function will grab data on:

  • flights: all flights that departed a given airport in a given year and month
  • weather: hourly meterological data for a given airport in a given year and month
  • airports: airport names, FAA codes, and locations
  • airlines: translation between two letter carrier (airline) codes and names
  • planes: construction information about each plane found in flights

Airline On-Time Statistics and Delay Causes

The U.S. Department of Transportation’s (DOT) Bureau of Transportation Statistics (BTS) tracks the on-time performance of domestic flights operated by large air carriers. Summary information on the number of on-time, delayed, canceled and diverted flights appears in DOT’s monthly Air Travel Consumer Report, published about 30 days after the month’s end, as well as in summary tables posted on this website. BTS began collecting details on the causes of flight delays in June 2003. Summary statistics and raw data are made available to the public at the time the Air Travel Consumer Report is released. Access it here

Worldwide flight data

Open flights: As of January 2017, the OpenFlights Airports Database contains over 10,000 airports, train stations and ferry terminals spanning the globe

Download: airports.dat (Airports only, high quality)

Download: airports-extended.dat (Airports, train stations and ferry terminals, including user contributions)

Bureau of Transportation:

Flightera.net seems to have a lot of good data for free. It has in-depth data on flights and doesn’t seem limited by date. I can’t speak on the validity of the data though.

flightradar24.com has lots of data, also historically, they might be willing to help you get it in a nice format.

 

2019 Crime statistics in the USA

Dataset with arrest in US by race and separate states. Download Excel here

Researchers from IBM, MIT and Harvard Announced The Release Of DARPA “Common Sense AI” Dataset Along With Two Machine Learning Models At ICML 2021

Building machines that can make decisions based on common sense is no easy feat. A machine must be able to do more than merely find patterns in data; it also needs a way of interpreting the intentions and beliefs behind people’s choices.

At the 2021 International Conference on Machine Learning (ICML), Researchers from IBM, MIT, and Harvard University have come together to release a DARPA “Common Sense AI” dataset for benchmarking AI intuition. They are also releasing two machine learning models that represent different approaches to the problem that relies on testing techniques psychologists use to study infants’ behavior to accelerate the development of AI exhibiting common sense.

Source – Summary – Paper – IBM Blog

100 million protein structures Dataset by DeepMind

DeepMind creates ‘transformative’ map of human proteins drawn by AI. By the end of the year, DeepMind hopes to release predictions for 100 million protein structures, a dataset that will be “transformative for our understanding of how life works,

Here’s a good article about this topic

Google Dataset Search

Google Dataset Search

Malware traffic dataset

Comprises 1914081 records created from all malware traffic analysis .net PCAP files, from 2013 to 2021. The logs are generated using Suricata and Zeek.

Originator: ali_alwashali

Percent of “foreign-born” population in each US and EU state or country.

For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state 🇺🇸🇪🇺

Author: Here

Percent of “foreign-born” population in each US and EU state or country. For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state.

Examples of “foreign-born” in this context:

  • Person born in Spain and living in France is NOT “foreign-born”

  • Person born in Turkey and living in France is “foreign-born”

  • Person born in Florida and living in Texas is NOT “foreign-born”

  • Person born in Mexico and living in Texas is “foreign-born”

  • Person born in Florida and living in France is “foreign-born”

  • Person born in France and living in Florida is “foreign-born”

🇺🇸🇪🇺🗺️

Note: Poland, Ireland, Germany, Greece, Cyprus, Malta, Portugal uses Eurostat 2010 Migration data and Croatia has no data at all

Link1

Link2

Link3

Tools: MS Office

Source: Here

35% of “entry-level” jobs on LinkedIn require 3+ years of experience

r/dataisbeautiful - [OC] 35% of "entry-level" jobs on LinkedIn require 3+ years of experience

Source: LinkedIn data  (see original post)

Tool: Photoshop from my colleague

Latest complete Netflix movie dataset

Created from 4 APIs. 11K+ rows and 30+ attributes of Netflix (Ratings, earnings, actors, language, availability, movie trailers, and many more)

Dataset on Kaggle.

Explore this dataset using FlixGem.com (this dataset is powering this webapp)

Dataset on Google Sheets.

Common Crawl

A corpus of web crawl data composed of over 50 billion web pages. The Common Crawl corpus contains petabytes of data collected since 2008. It contains raw web page data, extracted metadata and text extractions.

AWS CLI Access (No AWS account required)

aws s3 ls s3://commoncrawl/ --no-sign-request

s3://commoncrawl/crawl-data/CC-MAIN-2021-17 – April 2021

 Dataset on protein prices

Data on Primary Commodity Prices are updated monthly based on the IMF’s Primary Commodity Price System.

Excel Database

 CPOST dataset on suicide attacks over four decades

The University of Chicago Project on Security and Threats presents the updated and expanded Database on Suicide Attacks (DSAT), which now links to Uppsala Conflict Data Program data on armed conflicts and includes a new dataset measuring the alliance and rivalry relationships among militant groups with connections to suicide attack groups. Access it here.

Credit Card Dataset – Survey of Consumer Finances (SCF) Combined Extract Data 1989-2019

You can do a lot of aggregated analysis in a pretty straightforward way there.

Drone imagery with annotations for small object detection and tracking dataset

11 TB dataset of drone imagery with annotations for small object detection and tracking

Download and more information are available here

Dataset License: CDLA-Sharing-1.0

Helper scripts for accessing the dataset: DATASET.md

Dataset Exploration: Colab

NOAA High-Resolution Rapid Refresh (HRRR) Model

The HRRR is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model, initialized by 3km grids with 3km radar assimilation. Radar data is assimilated in the HRRR every 15 min over a 1-h period adding further detail to that provided by the hourly data assimilation from the 13km radar-enhanced Rapid Refresh.

Registry of Open Data on AWS

This registry exists to help people discover and share datasets that are available via AWS resources. Learn more about sharing data on AWS.

See all usage examples for datasets listed in this registry.

See datasets from Digital Earth AfricaFacebook Data for GoodNASA Space Act AgreementNIH STRIDESNOAA Big Data ProgramSpace Telescope Science Institute, and Amazon Sustainability Data Initiative.

Textbook Question Answering (TQA)

1,076 textbook lessons, 26,260 questions, 6229 images

Documentation: allenai.org/data/tqa

Download

Harmonized Cancer Datasets: Genomic Data Commons Data Portal

The GDC Data Portal is a robust data-driven platform that allows cancer
researchers and bioinformaticians to search and download cancer data for analysis.

Genomic Data Commons Data Portal
Genomic Data Commons Data Portal

The Cancer Genome Atlas

The Cancer Genome Atlas (TCGA), a collaboration between the National Cancer Institute (NCI) and National Human Genome Research Institute (NHGRI), aims to generate comprehensive, multi-dimensional maps of the key genomic changes in major types and subtypes of cancer.

AWS CLI Access (No AWS account required)

aws s3 ls s3://tcga-2-open/ --no-sign-request

Therapeutically Applicable Research to Generate Effective Treatments (TARGET)

The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) program applies a comprehensive genomic approach to determine molecular changes that drive childhood cancers. The goal of the program is to use data to guide the development of effective, less toxic therapies. TARGET is organized into a collaborative network of disease-specific project teams.  TARGET projects provide comprehensive molecular characterization to determine the genetic changes that drive the initiation and progression of childhood cancers. The dataset contains open Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-Seq Isoform Expression Quantification, miRNA-Seq miRNA Expression Quantification data from Genomic Data Commons (GDC), and open data from GDC Legacy Archive. Access it here.

Genome Aggregation Database (gnomAD)

The Genome Aggregation Database (gnomAD) is a resource developed by an international coalition of investigators that aggregates and harmonizes both exome and genome data from a wide range of large-scale human sequencing projects. The summary data provided here are released for the benefit of the wider scientific community without restriction on use. Downloads

SQuAD (Stanford Question Answering Dataset)

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. Access it here.

PubMed Diabetes Dataset

The Pubmed Diabetes dataset consists of 19717 scientific publications from PubMed database pertaining to diabetes classified into one of three classes. The citation network consists of 44338 links. Each publication in the dataset is described by a TF/IDF weighted word vector from a dictionary which consists of 500 unique words. The README file in the dataset provides more details.

Download Link

Drug-Target Interaction Dataset

This dataset contains interactions between drugs and targets collected from DrugBank, KEGG Drug, DCDB, and Matador. It was originally collected by Perlman et al. It contains 315 drugs, 250 targets, 1,306 drug-target interactions, 5 types of drug-drug similarities, and 3 types of target-target similarities. Drug-drug similarities include Chemical-based, Ligand-based, Expression-based, Side-effect-based, and Annotation-based similarities. Target-target similarities include Sequence-based, Protein-protein interaction network-based, and Gene Ontology-based similarities. The original task on the dataset is to predict new interactions between drugs and targets based on different types of similarities in the network. Download link

Pharmacogenomics Datasets

PharmGKB data and knowledge is available as downloads. It is often critical to check with their curators at feedback@pharmgkb.org before embarking on a large project using these data, to be sure that the files and data they make available are being interpreted correctly. PharmGKB generally does NOT need to be a co-author on such analyses; They just want to make sure that there is a correct understanding of our data before lots of resources are spent.

Pancreatic Cancer Organoid Profiling

The dataset contains open RNA-Seq Gene Expression Quantification data and controlled WGS/WXS/RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic Mutation, and RNA-Seq Splice Junction Quantification. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://gdc-organoid-pancreatic-phs001611-2-open/ --no-sign-request

Africa Soil Information Service (AfSIS) Soil Chemistry

This dataset contains soil infrared spectral data and paired soil property reference measurements for georeferenced soil samples that were collected through the Africa Soil Information Service (AfSIS) project, which lasted from 2009 through 2018. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://afsis/ --no-sign-request

Dataset for Affective States in E-Environments

DAiSEE is the first multi-label video classification dataset comprising of 9068 video snippets captured from 112 users for recognizing the user affective states of boredom, confusion, engagement, and frustration “in the wild”. The dataset has four levels of labels namely – very low, low, high, and very high for each of the affective states, which are crowd annotated and correlated with a gold standard annotation created using a team of expert psychologists. Download it here.

NatureServe Explorer Dataset

NatureServe Explorer provides conservation status, taxonomy, distribution, and life history information for more than 95,000 plants and animals in the United States and Canada, and more than 10,000 vegetation communities and ecological systems in the Western Hemisphere.

The data available through NatureServe Explorer represents data managed in the NatureServe Central Databases. These databases are dynamic, being continually enhanced and refined through the input of hundreds of natural heritage program scientists and other collaborators. NatureServe Explorer is updated from these central databases to reflect information from new field surveys, the latest taxonomic treatments and other scientific publications, and new conservation status assessments. Explore Data here

Flight Records in the US

Airline On-Time Performance and Causes of Flight Delays – On_Time Data.

This database contains scheduled and actual departure and arrival times, reason of delay. reported by certified U.S. air carriers that account for at least one percent of domestic scheduled passenger revenues. The data is collected by the Office of Airline Information, Bureau of Transportation Statistics (BTS).

FlightAware.com has data but you need to pay for a full dataset.

The anyflights package supplies a set of functions to generate air travel data (and data packages!) similar to nycflights13. With a user-defined year and airport, the anyflights function will grab data on:

  • flights: all flights that departed a given airport in a given year and month
  • weather: hourly meterological data for a given airport in a given year and month
  • airports: airport names, FAA codes, and locations
  • airlines: translation between two letter carrier (airline) codes and names
  • planes: construction information about each plane found in flights

Airline On-Time Statistics and Delay Causes

The U.S. Department of Transportation’s (DOT) Bureau of Transportation Statistics (BTS) tracks the on-time performance of domestic flights operated by large air carriers. Summary information on the number of on-time, delayed, canceled and diverted flights appears in DOT’s monthly Air Travel Consumer Report, published about 30 days after the month’s end, as well as in summary tables posted on this website. BTS began collecting details on the causes of flight delays in June 2003. Summary statistics and raw data are made available to the public at the time the Air Travel Consumer Report is released. Access it here

Worldwide flight data

Open flights: As of January 2017, the OpenFlights Airports Database contains over 10,000 airports, train stations and ferry terminals spanning the globe

Download: airports.dat (Airports only, high quality)

Download: airports-extended.dat (Airports, train stations and ferry terminals, including user contributions)

Bureau of Transportation:

Flightera.net seems to have a lot of good data for free. It has in-depth data on flights and doesn’t seem limited by date. I can’t speak on the validity of the data though.

flightradar24.com has lots of data, also historically, they might be willing to help you get it in a nice format.

 

2019 Crime statistics in the USA

Dataset with arrest in US by race and separate states. Download Excel here

Researchers from IBM, MIT and Harvard Announced The Release Of DARPA “Common Sense AI” Dataset Along With Two Machine Learning Models At ICML 2021

Building machines that can make decisions based on common sense is no easy feat. A machine must be able to do more than merely find patterns in data; it also needs a way of interpreting the intentions and beliefs behind people’s choices.

At the 2021 International Conference on Machine Learning (ICML), Researchers from IBM, MIT, and Harvard University have come together to release a DARPA “Common Sense AI” dataset for benchmarking AI intuition. They are also releasing two machine learning models that represent different approaches to the problem that relies on testing techniques psychologists use to study infants’ behavior to accelerate the development of AI exhibiting common sense.

Source – Summary – Paper – IBM Blog

100 million protein structures Dataset by DeepMind

DeepMind creates ‘transformative’ map of human proteins drawn by AI. By the end of the year, DeepMind hopes to release predictions for 100 million protein structures, a dataset that will be “transformative for our understanding of how life works,

Here’s a good article about this topic

Google Dataset Search

Google Dataset Search

Malware traffic dataset

Comprises 1914081 records created from all malware traffic analysis .net PCAP files, from 2013 to 2021. The logs are generated using Suricata and Zeek.

Originator: ali_alwashali

Percent of “foreign-born” population in each US and EU state or country.

For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state 🇺🇸🇪🇺

Author: Here

Percent of “foreign-born” population in each US and EU state or country. For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state.

Examples of “foreign-born” in this context:

  • Person born in Spain and living in France is NOT “foreign-born”

  • Person born in Turkey and living in France is “foreign-born”

  • Person born in Florida and living in Texas is NOT “foreign-born”

  • Person born in Mexico and living in Texas is “foreign-born”

  • Person born in Florida and living in France is “foreign-born”

  • Person born in France and living in Florida is “foreign-born”

🇺🇸🇪🇺🗺️

Note: Poland, Ireland, Germany, Greece, Cyprus, Malta, Portugal uses Eurostat 2010 Migration data and Croatia has no data at all

Link1

Link2

Link3

Tools: MS Office

Source: Here

35% of “entry-level” jobs on LinkedIn require 3+ years of experience

r/dataisbeautiful - [OC] 35% of "entry-level" jobs on LinkedIn require 3+ years of experience

Source: LinkedIn data  (see original post)

Tool: Photoshop from my colleague

Latest complete Netflix movie dataset

Created from 4 APIs. 11K+ rows and 30+ attributes of Netflix (Ratings, earnings, actors, language, availability, movie trailers, and many more)

Dataset on Kaggle.

Explore this dataset using FlixGem.com (this dataset is powering this webapp)

Dataset on Google Sheets.

Common Crawl

A corpus of web crawl data composed of over 50 billion web pages. The Common Crawl corpus contains petabytes of data collected since 2008. It contains raw web page data, extracted metadata and text extractions.

AWS CLI Access (No AWS account required)

aws s3 ls s3://commoncrawl/ --no-sign-request

s3://commoncrawl/crawl-data/CC-MAIN-2021-17 – April 2021

 Dataset on protein prices

Data on Primary Commodity Prices are updated monthly based on the IMF’s Primary Commodity Price System.

Excel Database

 CPOST dataset on suicide attacks over four decades

The University of Chicago Project on Security and Threats presents the updated and expanded Database on Suicide Attacks (DSAT), which now links to Uppsala Conflict Data Program data on armed conflicts and includes a new dataset measuring the alliance and rivalry relationships among militant groups with connections to suicide attack groups. Access it here.

Credit Card Dataset – Survey of Consumer Finances (SCF) Combined Extract Data 1989-2019

You can do a lot of aggregated analysis in a pretty straightforward way there.

Drone imagery with annotations for small object detection and tracking dataset

11 TB dataset of drone imagery with annotations for small object detection and tracking

Download and more information are available here

Dataset License: CDLA-Sharing-1.0

Helper scripts for accessing the dataset: DATASET.md

Dataset Exploration: Colab

NOAA High-Resolution Rapid Refresh (HRRR) Model

The HRRR is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model, initialized by 3km grids with 3km radar assimilation. Radar data is assimilated in the HRRR every 15 min over a 1-h period adding further detail to that provided by the hourly data assimilation from the 13km radar-enhanced Rapid Refresh.

Registry of Open Data on AWS

This registry exists to help people discover and share datasets that are available via AWS resources. Learn more about sharing data on AWS.

See all usage examples for datasets listed in this registry.

See datasets from Digital Earth AfricaFacebook Data for GoodNASA Space Act AgreementNIH STRIDESNOAA Big Data ProgramSpace Telescope Science Institute, and Amazon Sustainability Data Initiative.

Textbook Question Answering (TQA)

1,076 textbook lessons, 26,260 questions, 6229 images

Documentation: allenai.org/data/tqa

Download

Harmonized Cancer Datasets: Genomic Data Commons Data Portal

The GDC Data Portal is a robust data-driven platform that allows cancer
researchers and bioinformaticians to search and download cancer data for analysis.

Genomic Data Commons Data Portal
Genomic Data Commons Data Portal

The Cancer Genome Atlas

The Cancer Genome Atlas (TCGA), a collaboration between the National Cancer Institute (NCI) and National Human Genome Research Institute (NHGRI), aims to generate comprehensive, multi-dimensional maps of the key genomic changes in major types and subtypes of cancer.

AWS CLI Access (No AWS account required)

aws s3 ls s3://tcga-2-open/ --no-sign-request

Therapeutically Applicable Research to Generate Effective Treatments (TARGET)

The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) program applies a comprehensive genomic approach to determine molecular changes that drive childhood cancers. The goal of the program is to use data to guide the development of effective, less toxic therapies. TARGET is organized into a collaborative network of disease-specific project teams.  TARGET projects provide comprehensive molecular characterization to determine the genetic changes that drive the initiation and progression of childhood cancers. The dataset contains open Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-Seq Isoform Expression Quantification, miRNA-Seq miRNA Expression Quantification data from Genomic Data Commons (GDC), and open data from GDC Legacy Archive. Access it here.

Genome Aggregation Database (gnomAD)

The Genome Aggregation Database (gnomAD) is a resource developed by an international coalition of investigators that aggregates and harmonizes both exome and genome data from a wide range of large-scale human sequencing projects. The summary data provided here are released for the benefit of the wider scientific community without restriction on use. Downloads

SQuAD (Stanford Question Answering Dataset)

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. Access it here.

PubMed Diabetes Dataset

The Pubmed Diabetes dataset consists of 19717 scientific publications from PubMed database pertaining to diabetes classified into one of three classes. The citation network consists of 44338 links. Each publication in the dataset is described by a TF/IDF weighted word vector from a dictionary which consists of 500 unique words. The README file in the dataset provides more details.

Download Link

Drug-Target Interaction Dataset

This dataset contains interactions between drugs and targets collected from DrugBank, KEGG Drug, DCDB, and Matador. It was originally collected by Perlman et al. It contains 315 drugs, 250 targets, 1,306 drug-target interactions, 5 types of drug-drug similarities, and 3 types of target-target similarities. Drug-drug similarities include Chemical-based, Ligand-based, Expression-based, Side-effect-based, and Annotation-based similarities. Target-target similarities include Sequence-based, Protein-protein interaction network-based, and Gene Ontology-based similarities. The original task on the dataset is to predict new interactions between drugs and targets based on different types of similarities in the network. Download link

Pharmacogenomics Datasets

PharmGKB data and knowledge is available as downloads. It is often critical to check with their curators at feedback@pharmgkb.org before embarking on a large project using these data, to be sure that the files and data they make available are being interpreted correctly. PharmGKB generally does NOT need to be a co-author on such analyses; They just want to make sure that there is a correct understanding of our data before lots of resources are spent.

Pancreatic Cancer Organoid Profiling

The dataset contains open RNA-Seq Gene Expression Quantification data and controlled WGS/WXS/RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic Mutation, and RNA-Seq Splice Junction Quantification. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://gdc-organoid-pancreatic-phs001611-2-open/ --no-sign-request

Africa Soil Information Service (AfSIS) Soil Chemistry

This dataset contains soil infrared spectral data and paired soil property reference measurements for georeferenced soil samples that were collected through the Africa Soil Information Service (AfSIS) project, which lasted from 2009 through 2018. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://afsis/ --no-sign-request

Dataset for Affective States in E-Environments

DAiSEE is the first multi-label video classification dataset comprising of 9068 video snippets captured from 112 users for recognizing the user affective states of boredom, confusion, engagement, and frustration “in the wild”. The dataset has four levels of labels namely – very low, low, high, and very high for each of the affective states, which are crowd annotated and correlated with a gold standard annotation created using a team of expert psychologists. Download it here.

NatureServe Explorer Dataset

NatureServe Explorer provides conservation status, taxonomy, distribution, and life history information for more than 95,000 plants and animals in the United States and Canada, and more than 10,000 vegetation communities and ecological systems in the Western Hemisphere.

The data available through NatureServe Explorer represents data managed in the NatureServe Central Databases. These databases are dynamic, being continually enhanced and refined through the input of hundreds of natural heritage program scientists and other collaborators. NatureServe Explorer is updated from these central databases to reflect information from new field surveys, the latest taxonomic treatments and other scientific publications, and new conservation status assessments. Explore Data here

Flight Records in the US

Airline On-Time Performance and Causes of Flight Delays – On_Time Data.

This database contains scheduled and actual departure and arrival times, reason of delay. reported by certified U.S. air carriers that account for at least one percent of domestic scheduled passenger revenues. The data is collected by the Office of Airline Information, Bureau of Transportation Statistics (BTS).

FlightAware.com has data but you need to pay for a full dataset.

The anyflights package supplies a set of functions to generate air travel data (and data packages!) similar to nycflights13. With a user-defined year and airport, the anyflights function will grab data on:

  • flights: all flights that departed a given airport in a given year and month
  • weather: hourly meterological data for a given airport in a given year and month
  • airports: airport names, FAA codes, and locations
  • airlines: translation between two letter carrier (airline) codes and names
  • planes: construction information about each plane found in flights

Airline On-Time Statistics and Delay Causes

The U.S. Department of Transportation’s (DOT) Bureau of Transportation Statistics (BTS) tracks the on-time performance of domestic flights operated by large air carriers. Summary information on the number of on-time, delayed, canceled and diverted flights appears in DOT’s monthly Air Travel Consumer Report, published about 30 days after the month’s end, as well as in summary tables posted on this website. BTS began collecting details on the causes of flight delays in June 2003. Summary statistics and raw data are made available to the public at the time the Air Travel Consumer Report is released. Access it here

Worldwide flight data

Open flights: As of January 2017, the OpenFlights Airports Database contains over 10,000 airports, train stations and ferry terminals spanning the globe

Download: airports.dat (Airports only, high quality)

Download: airports-extended.dat (Airports, train stations and ferry terminals, including user contributions)

Bureau of Transportation:

Flightera.net seems to have a lot of good data for free. It has in-depth data on flights and doesn’t seem limited by date. I can’t speak on the validity of the data though.

flightradar24.com has lots of data, also historically, they might be willing to help you get it in a nice format.

 

2019 Crime statistics in the USA

Dataset with arrest in US by race and separate states. Download Excel here

Researchers from IBM, MIT and Harvard Announced The Release Of DARPA “Common Sense AI” Dataset Along With Two Machine Learning Models At ICML 2021

Building machines that can make decisions based on common sense is no easy feat. A machine must be able to do more than merely find patterns in data; it also needs a way of interpreting the intentions and beliefs behind people’s choices.

At the 2021 International Conference on Machine Learning (ICML), Researchers from IBM, MIT, and Harvard University have come together to release a DARPA “Common Sense AI” dataset for benchmarking AI intuition. They are also releasing two machine learning models that represent different approaches to the problem that relies on testing techniques psychologists use to study infants’ behavior to accelerate the development of AI exhibiting common sense.

Source – Summary – Paper – IBM Blog

100 million protein structures Dataset by DeepMind

DeepMind creates ‘transformative’ map of human proteins drawn by AI. By the end of the year, DeepMind hopes to release predictions for 100 million protein structures, a dataset that will be “transformative for our understanding of how life works,

Here’s a good article about this topic

Google Dataset Search

Google Dataset Search

Malware traffic dataset

Comprises 1914081 records created from all malware traffic analysis .net PCAP files, from 2013 to 2021. The logs are generated using Suricata and Zeek.

Originator: ali_alwashali

Percent of “foreign-born” population in each US and EU state or country.

For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state 🇺🇸🇪🇺

Author: Here

Percent of “foreign-born” population in each US and EU state or country. For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state.

Examples of “foreign-born” in this context:

  • Person born in Spain and living in France is NOT “foreign-born”

  • Person born in Turkey and living in France is “foreign-born”

  • Person born in Florida and living in Texas is NOT “foreign-born”

  • Person born in Mexico and living in Texas is “foreign-born”

  • Person born in Florida and living in France is “foreign-born”

  • Person born in France and living in Florida is “foreign-born”

🇺🇸🇪🇺🗺️

Note: Poland, Ireland, Germany, Greece, Cyprus, Malta, Portugal uses Eurostat 2010 Migration data and Croatia has no data at all

Link1

Link2

Link3

Tools: MS Office

Source: Here

35% of “entry-level” jobs on LinkedIn require 3+ years of experience

r/dataisbeautiful - [OC] 35% of "entry-level" jobs on LinkedIn require 3+ years of experience

Source: LinkedIn data  (see original post)

Tool: Photoshop from my colleague

Latest complete Netflix movie dataset

Created from 4 APIs. 11K+ rows and 30+ attributes of Netflix (Ratings, earnings, actors, language, availability, movie trailers, and many more)

Dataset on Kaggle.

Explore this dataset using FlixGem.com (this dataset is powering this webapp)

Dataset on Google Sheets.

Common Crawl

A corpus of web crawl data composed of over 50 billion web pages. The Common Crawl corpus contains petabytes of data collected since 2008. It contains raw web page data, extracted metadata and text extractions.

AWS CLI Access (No AWS account required)

aws s3 ls s3://commoncrawl/ --no-sign-request

s3://commoncrawl/crawl-data/CC-MAIN-2021-17 – April 2021

 Dataset on protein prices

Data on Primary Commodity Prices are updated monthly based on the IMF’s Primary Commodity Price System.

Excel Database

 CPOST dataset on suicide attacks over four decades

The University of Chicago Project on Security and Threats presents the updated and expanded Database on Suicide Attacks (DSAT), which now links to Uppsala Conflict Data Program data on armed conflicts and includes a new dataset measuring the alliance and rivalry relationships among militant groups with connections to suicide attack groups. Access it here.

Credit Card Dataset – Survey of Consumer Finances (SCF) Combined Extract Data 1989-2019

You can do a lot of aggregated analysis in a pretty straightforward way there.

Drone imagery with annotations for small object detection and tracking dataset

11 TB dataset of drone imagery with annotations for small object detection and tracking

Download and more information are available here

Dataset License: CDLA-Sharing-1.0

Helper scripts for accessing the dataset: DATASET.md

Dataset Exploration: Colab

NOAA High-Resolution Rapid Refresh (HRRR) Model

The HRRR is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model, initialized by 3km grids with 3km radar assimilation. Radar data is assimilated in the HRRR every 15 min over a 1-h period adding further detail to that provided by the hourly data assimilation from the 13km radar-enhanced Rapid Refresh.

Registry of Open Data on AWS

This registry exists to help people discover and share datasets that are available via AWS resources. Learn more about sharing data on AWS.

See all usage examples for datasets listed in this registry.

See datasets from Digital Earth AfricaFacebook Data for GoodNASA Space Act AgreementNIH STRIDESNOAA Big Data ProgramSpace Telescope Science Institute, and Amazon Sustainability Data Initiative.

Textbook Question Answering (TQA)

1,076 textbook lessons, 26,260 questions, 6229 images

Documentation: allenai.org/data/tqa

Download

Harmonized Cancer Datasets: Genomic Data Commons Data Portal

The GDC Data Portal is a robust data-driven platform that allows cancer
researchers and bioinformaticians to search and download cancer data for analysis.

Genomic Data Commons Data Portal
Genomic Data Commons Data Portal

The Cancer Genome Atlas

The Cancer Genome Atlas (TCGA), a collaboration between the National Cancer Institute (NCI) and National Human Genome Research Institute (NHGRI), aims to generate comprehensive, multi-dimensional maps of the key genomic changes in major types and subtypes of cancer.

AWS CLI Access (No AWS account required)

aws s3 ls s3://tcga-2-open/ --no-sign-request

Therapeutically Applicable Research to Generate Effective Treatments (TARGET)

The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) program applies a comprehensive genomic approach to determine molecular changes that drive childhood cancers. The goal of the program is to use data to guide the development of effective, less toxic therapies. TARGET is organized into a collaborative network of disease-specific project teams.  TARGET projects provide comprehensive molecular characterization to determine the genetic changes that drive the initiation and progression of childhood cancers. The dataset contains open Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-Seq Isoform Expression Quantification, miRNA-Seq miRNA Expression Quantification data from Genomic Data Commons (GDC), and open data from GDC Legacy Archive. Access it here.

Genome Aggregation Database (gnomAD)

The Genome Aggregation Database (gnomAD) is a resource developed by an international coalition of investigators that aggregates and harmonizes both exome and genome data from a wide range of large-scale human sequencing projects. The summary data provided here are released for the benefit of the wider scientific community without restriction on use. Downloads

SQuAD (Stanford Question Answering Dataset)

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. Access it here.

PubMed Diabetes Dataset

The Pubmed Diabetes dataset consists of 19717 scientific publications from PubMed database pertaining to diabetes classified into one of three classes. The citation network consists of 44338 links. Each publication in the dataset is described by a TF/IDF weighted word vector from a dictionary which consists of 500 unique words. The README file in the dataset provides more details.

Download Link

Drug-Target Interaction Dataset

This dataset contains interactions between drugs and targets collected from DrugBank, KEGG Drug, DCDB, and Matador. It was originally collected by Perlman et al. It contains 315 drugs, 250 targets, 1,306 drug-target interactions, 5 types of drug-drug similarities, and 3 types of target-target similarities. Drug-drug similarities include Chemical-based, Ligand-based, Expression-based, Side-effect-based, and Annotation-based similarities. Target-target similarities include Sequence-based, Protein-protein interaction network-based, and Gene Ontology-based similarities. The original task on the dataset is to predict new interactions between drugs and targets based on different types of similarities in the network. Download link

Pharmacogenomics Datasets

PharmGKB data and knowledge is available as downloads. It is often critical to check with their curators at feedback@pharmgkb.org before embarking on a large project using these data, to be sure that the files and data they make available are being interpreted correctly. PharmGKB generally does NOT need to be a co-author on such analyses; They just want to make sure that there is a correct understanding of our data before lots of resources are spent.

Pancreatic Cancer Organoid Profiling

The dataset contains open RNA-Seq Gene Expression Quantification data and controlled WGS/WXS/RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic Mutation, and RNA-Seq Splice Junction Quantification. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://gdc-organoid-pancreatic-phs001611-2-open/ --no-sign-request

Africa Soil Information Service (AfSIS) Soil Chemistry

This dataset contains soil infrared spectral data and paired soil property reference measurements for georeferenced soil samples that were collected through the Africa Soil Information Service (AfSIS) project, which lasted from 2009 through 2018. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://afsis/ --no-sign-request

Dataset for Affective States in E-Environments

DAiSEE is the first multi-label video classification dataset comprising of 9068 video snippets captured from 112 users for recognizing the user affective states of boredom, confusion, engagement, and frustration “in the wild”. The dataset has four levels of labels namely – very low, low, high, and very high for each of the affective states, which are crowd annotated and correlated with a gold standard annotation created using a team of expert psychologists. Download it here.

NatureServe Explorer Dataset

NatureServe Explorer provides conservation status, taxonomy, distribution, and life history information for more than 95,000 plants and animals in the United States and Canada, and more than 10,000 vegetation communities and ecological systems in the Western Hemisphere.

The data available through NatureServe Explorer represents data managed in the NatureServe Central Databases. These databases are dynamic, being continually enhanced and refined through the input of hundreds of natural heritage program scientists and other collaborators. NatureServe Explorer is updated from these central databases to reflect information from new field surveys, the latest taxonomic treatments and other scientific publications, and new conservation status assessments. Explore Data here

Flight Records in the US

Airline On-Time Performance and Causes of Flight Delays – On_Time Data.

This database contains scheduled and actual departure and arrival times, reason of delay. reported by certified U.S. air carriers that account for at least one percent of domestic scheduled passenger revenues. The data is collected by the Office of Airline Information, Bureau of Transportation Statistics (BTS).

FlightAware.com has data but you need to pay for a full dataset.

The anyflights package supplies a set of functions to generate air travel data (and data packages!) similar to nycflights13. With a user-defined year and airport, the anyflights function will grab data on:

  • flights: all flights that departed a given airport in a given year and month
  • weather: hourly meterological data for a given airport in a given year and month
  • airports: airport names, FAA codes, and locations
  • airlines: translation between two letter carrier (airline) codes and names
  • planes: construction information about each plane found in flights

Airline On-Time Statistics and Delay Causes

The U.S. Department of Transportation’s (DOT) Bureau of Transportation Statistics (BTS) tracks the on-time performance of domestic flights operated by large air carriers. Summary information on the number of on-time, delayed, canceled and diverted flights appears in DOT’s monthly Air Travel Consumer Report, published about 30 days after the month’s end, as well as in summary tables posted on this website. BTS began collecting details on the causes of flight delays in June 2003. Summary statistics and raw data are made available to the public at the time the Air Travel Consumer Report is released. Access it here

Worldwide flight data

Open flights: As of January 2017, the OpenFlights Airports Database contains over 10,000 airports, train stations and ferry terminals spanning the globe

Download: airports.dat (Airports only, high quality)

Download: airports-extended.dat (Airports, train stations and ferry terminals, including user contributions)

Bureau of Transportation:

Flightera.net seems to have a lot of good data for free. It has in-depth data on flights and doesn’t seem limited by date. I can’t speak on the validity of the data though.

flightradar24.com has lots of data, also historically, they might be willing to help you get it in a nice format.

 

2019 Crime statistics in the USA

Dataset with arrest in US by race and separate states. Download Excel here

Researchers from IBM, MIT and Harvard Announced The Release Of DARPA “Common Sense AI” Dataset Along With Two Machine Learning Models At ICML 2021

Building machines that can make decisions based on common sense is no easy feat. A machine must be able to do more than merely find patterns in data; it also needs a way of interpreting the intentions and beliefs behind people’s choices.

At the 2021 International Conference on Machine Learning (ICML), Researchers from IBM, MIT, and Harvard University have come together to release a DARPA “Common Sense AI” dataset for benchmarking AI intuition. They are also releasing two machine learning models that represent different approaches to the problem that relies on testing techniques psychologists use to study infants’ behavior to accelerate the development of AI exhibiting common sense.

Source – Summary – Paper – IBM Blog

100 million protein structures Dataset by DeepMind

DeepMind creates ‘transformative’ map of human proteins drawn by AI. By the end of the year, DeepMind hopes to release predictions for 100 million protein structures, a dataset that will be “transformative for our understanding of how life works,

Here’s a good article about this topic

Google Dataset Search

Google Dataset Search

Malware traffic dataset

Comprises 1914081 records created from all malware traffic analysis .net PCAP files, from 2013 to 2021. The logs are generated using Suricata and Zeek.

Originator: ali_alwashali

Percent of “foreign-born” population in each US and EU state or country.

For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state 🇺🇸🇪🇺

Author: Here

Percent of “foreign-born” population in each US and EU state or country. For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state.

Examples of “foreign-born” in this context:

  • Person born in Spain and living in France is NOT “foreign-born”

  • Person born in Turkey and living in France is “foreign-born”

  • Person born in Florida and living in Texas is NOT “foreign-born”

  • Person born in Mexico and living in Texas is “foreign-born”

  • Person born in Florida and living in France is “foreign-born”

  • Person born in France and living in Florida is “foreign-born”

🇺🇸🇪🇺🗺️

Note: Poland, Ireland, Germany, Greece, Cyprus, Malta, Portugal uses Eurostat 2010 Migration data and Croatia has no data at all

Link1

Link2

Link3

Tools: MS Office

Source: Here

35% of “entry-level” jobs on LinkedIn require 3+ years of experience

r/dataisbeautiful - [OC] 35% of "entry-level" jobs on LinkedIn require 3+ years of experience

Source: LinkedIn data  (see original post)

Tool: Photoshop from my colleague

Latest complete Netflix movie dataset

Created from 4 APIs. 11K+ rows and 30+ attributes of Netflix (Ratings, earnings, actors, language, availability, movie trailers, and many more)

Dataset on Kaggle.

Explore this dataset using FlixGem.com (this dataset is powering this webapp)

Dataset on Google Sheets.

Common Crawl

A corpus of web crawl data composed of over 50 billion web pages. The Common Crawl corpus contains petabytes of data collected since 2008. It contains raw web page data, extracted metadata and text extractions.

AWS CLI Access (No AWS account required)

aws s3 ls s3://commoncrawl/ --no-sign-request

s3://commoncrawl/crawl-data/CC-MAIN-2021-17 – April 2021

 Dataset on protein prices

Data on Primary Commodity Prices are updated monthly based on the IMF’s Primary Commodity Price System.

Excel Database

 CPOST dataset on suicide attacks over four decades

The University of Chicago Project on Security and Threats presents the updated and expanded Database on Suicide Attacks (DSAT), which now links to Uppsala Conflict Data Program data on armed conflicts and includes a new dataset measuring the alliance and rivalry relationships among militant groups with connections to suicide attack groups. Access it here.

Credit Card Dataset – Survey of Consumer Finances (SCF) Combined Extract Data 1989-2019

You can do a lot of aggregated analysis in a pretty straightforward way there.

Drone imagery with annotations for small object detection and tracking dataset

11 TB dataset of drone imagery with annotations for small object detection and tracking

Download and more information are available here

Dataset License: CDLA-Sharing-1.0

Helper scripts for accessing the dataset: DATASET.md

Dataset Exploration: Colab

NOAA High-Resolution Rapid Refresh (HRRR) Model

The HRRR is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model, initialized by 3km grids with 3km radar assimilation. Radar data is assimilated in the HRRR every 15 min over a 1-h period adding further detail to that provided by the hourly data assimilation from the 13km radar-enhanced Rapid Refresh.

Registry of Open Data on AWS

This registry exists to help people discover and share datasets that are available via AWS resources. Learn more about sharing data on AWS.

See all usage examples for datasets listed in this registry.

See datasets from Digital Earth AfricaFacebook Data for GoodNASA Space Act AgreementNIH STRIDESNOAA Big Data ProgramSpace Telescope Science Institute, and Amazon Sustainability Data Initiative.

Textbook Question Answering (TQA)

1,076 textbook lessons, 26,260 questions, 6229 images

Documentation: allenai.org/data/tqa

Download

Harmonized Cancer Datasets: Genomic Data Commons Data Portal

The GDC Data Portal is a robust data-driven platform that allows cancer
researchers and bioinformaticians to search and download cancer data for analysis.

Genomic Data Commons Data Portal
Genomic Data Commons Data Portal

The Cancer Genome Atlas

The Cancer Genome Atlas (TCGA), a collaboration between the National Cancer Institute (NCI) and National Human Genome Research Institute (NHGRI), aims to generate comprehensive, multi-dimensional maps of the key genomic changes in major types and subtypes of cancer.

AWS CLI Access (No AWS account required)

aws s3 ls s3://tcga-2-open/ --no-sign-request

Therapeutically Applicable Research to Generate Effective Treatments (TARGET)

The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) program applies a comprehensive genomic approach to determine molecular changes that drive childhood cancers. The goal of the program is to use data to guide the development of effective, less toxic therapies. TARGET is organized into a collaborative network of disease-specific project teams.  TARGET projects provide comprehensive molecular characterization to determine the genetic changes that drive the initiation and progression of childhood cancers. The dataset contains open Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-Seq Isoform Expression Quantification, miRNA-Seq miRNA Expression Quantification data from Genomic Data Commons (GDC), and open data from GDC Legacy Archive. Access it here.

Genome Aggregation Database (gnomAD)

The Genome Aggregation Database (gnomAD) is a resource developed by an international coalition of investigators that aggregates and harmonizes both exome and genome data from a wide range of large-scale human sequencing projects. The summary data provided here are released for the benefit of the wider scientific community without restriction on use. Downloads

SQuAD (Stanford Question Answering Dataset)

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. Access it here.

PubMed Diabetes Dataset

The Pubmed Diabetes dataset consists of 19717 scientific publications from PubMed database pertaining to diabetes classified into one of three classes. The citation network consists of 44338 links. Each publication in the dataset is described by a TF/IDF weighted word vector from a dictionary which consists of 500 unique words. The README file in the dataset provides more details.

Download Link

Drug-Target Interaction Dataset

This dataset contains interactions between drugs and targets collected from DrugBank, KEGG Drug, DCDB, and Matador. It was originally collected by Perlman et al. It contains 315 drugs, 250 targets, 1,306 drug-target interactions, 5 types of drug-drug similarities, and 3 types of target-target similarities. Drug-drug similarities include Chemical-based, Ligand-based, Expression-based, Side-effect-based, and Annotation-based similarities. Target-target similarities include Sequence-based, Protein-protein interaction network-based, and Gene Ontology-based similarities. The original task on the dataset is to predict new interactions between drugs and targets based on different types of similarities in the network. Download link

Pharmacogenomics Datasets

PharmGKB data and knowledge is available as downloads. It is often critical to check with their curators at feedback@pharmgkb.org before embarking on a large project using these data, to be sure that the files and data they make available are being interpreted correctly. PharmGKB generally does NOT need to be a co-author on such analyses; They just want to make sure that there is a correct understanding of our data before lots of resources are spent.

Pancreatic Cancer Organoid Profiling

The dataset contains open RNA-Seq Gene Expression Quantification data and controlled WGS/WXS/RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic Mutation, and RNA-Seq Splice Junction Quantification. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://gdc-organoid-pancreatic-phs001611-2-open/ --no-sign-request

Africa Soil Information Service (AfSIS) Soil Chemistry

This dataset contains soil infrared spectral data and paired soil property reference measurements for georeferenced soil samples that were collected through the Africa Soil Information Service (AfSIS) project, which lasted from 2009 through 2018. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://afsis/ --no-sign-request

Dataset for Affective States in E-Environments

DAiSEE is the first multi-label video classification dataset comprising of 9068 video snippets captured from 112 users for recognizing the user affective states of boredom, confusion, engagement, and frustration “in the wild”. The dataset has four levels of labels namely – very low, low, high, and very high for each of the affective states, which are crowd annotated and correlated with a gold standard annotation created using a team of expert psychologists. Download it here.

NatureServe Explorer Dataset

NatureServe Explorer provides conservation status, taxonomy, distribution, and life history information for more than 95,000 plants and animals in the United States and Canada, and more than 10,000 vegetation communities and ecological systems in the Western Hemisphere.

The data available through NatureServe Explorer represents data managed in the NatureServe Central Databases. These databases are dynamic, being continually enhanced and refined through the input of hundreds of natural heritage program scientists and other collaborators. NatureServe Explorer is updated from these central databases to reflect information from new field surveys, the latest taxonomic treatments and other scientific publications, and new conservation status assessments. Explore Data here

Flight Records in the US

Airline On-Time Performance and Causes of Flight Delays – On_Time Data.

This database contains scheduled and actual departure and arrival times, reason of delay. reported by certified U.S. air carriers that account for at least one percent of domestic scheduled passenger revenues. The data is collected by the Office of Airline Information, Bureau of Transportation Statistics (BTS).

FlightAware.com has data but you need to pay for a full dataset.

The anyflights package supplies a set of functions to generate air travel data (and data packages!) similar to nycflights13. With a user-defined year and airport, the anyflights function will grab data on:

  • flights: all flights that departed a given airport in a given year and month
  • weather: hourly meterological data for a given airport in a given year and month
  • airports: airport names, FAA codes, and locations
  • airlines: translation between two letter carrier (airline) codes and names
  • planes: construction information about each plane found in flights

Airline On-Time Statistics and Delay Causes

The U.S. Department of Transportation’s (DOT) Bureau of Transportation Statistics (BTS) tracks the on-time performance of domestic flights operated by large air carriers. Summary information on the number of on-time, delayed, canceled and diverted flights appears in DOT’s monthly Air Travel Consumer Report, published about 30 days after the month’s end, as well as in summary tables posted on this website. BTS began collecting details on the causes of flight delays in June 2003. Summary statistics and raw data are made available to the public at the time the Air Travel Consumer Report is released. Access it here

Worldwide flight data

Open flights: As of January 2017, the OpenFlights Airports Database contains over 10,000 airports, train stations and ferry terminals spanning the globe

Download: airports.dat (Airports only, high quality)

Download: airports-extended.dat (Airports, train stations and ferry terminals, including user contributions)

Bureau of Transportation:

Flightera.net seems to have a lot of good data for free. It has in-depth data on flights and doesn’t seem limited by date. I can’t speak on the validity of the data though.

flightradar24.com has lots of data, also historically, they might be willing to help you get it in a nice format.

 

2019 Crime statistics in the USA

Dataset with arrest in US by race and separate states. Download Excel here

  • Node.js – Async non-blocking event-driven JavaScript runtime built on Chrome’s V8 JavaScript engine.
  • Frontend Development
  • iOS – Mobile operating system for Apple phones and tablets.
  • Android – Mobile operating system developed by Google.
  • IoT & Hybrid Apps
  • Electron – Cross-platform native desktop apps using JavaScript/HTML/CSS.
  • Cordova – JavaScript API for hybrid apps.
  • React Native – JavaScript framework for writing natively rendering mobile apps for iOS and Android.
  • Xamarin – Mobile app development IDE, testing, and distribution.
  • Linux
    • Containers
    • eBPF – Virtual machine that allows you to write more efficient and powerful tracing and monitoring for Linux systems.
    • Arch-based Projects – Linux distributions and projects based on Arch Linux.
  • macOS – Operating system for Apple’s Mac computers.
  • watchOS – Operating system for the Apple Watch.
  • JVM
  • Salesforce
  • Amazon Web Services
  • Windows
  • IPFS – P2P hypermedia protocol.
  • Fuse – Mobile development tools.
  • Heroku – Cloud platform as a service.
  • Raspberry Pi – Credit card-sized computer aimed at teaching kids programming, but capable of a lot more.
  • Qt – Cross-platform GUI app framework.
  • WebExtensions – Cross-browser extension system.
  • RubyMotion – Write cross-platform native apps for iOS, Android, macOS, tvOS, and watchOS in Ruby.
  • Smart TV – Create apps for different TV platforms.
  • GNOME – Simple and distraction-free desktop environment for Linux.
  • KDE – A free software community dedicated to creating an open and user-friendly computing experience.
  • .NET
    • Core
    • Roslyn – Open-source compilers and code analysis APIs for C# and VB.NET languages.
  • Amazon Alexa – Virtual home assistant.
  • DigitalOcean – Cloud computing platform designed for developers.
  • Flutter – Google’s mobile SDK for building native iOS and Android apps from a single codebase written in Dart.
  • Home Assistant – Open source home automation that puts local control and privacy first.
  • IBM Cloud – Cloud platform for developers and companies.
  • Firebase – App development platform built on Google Cloud Platform.
  • Robot Operating System 2.0 – Set of software libraries and tools that help you build robot apps.
  • Adafruit IO – Visualize and store data from any device.
  • Cloudflare – CDN, DNS, DDoS protection, and security for your site.
  • Actions on Google – Developer platform for Google Assistant.
  • ESP – Low-cost microcontrollers with WiFi and broad IoT applications.
  • Deno – A secure runtime for JavaScript and TypeScript that uses V8 and is built in Rust.
  • DOS – Operating system for x86-based personal computers that was popular during the 1980s and early 1990s.
  • Nix – Package manager for Linux and other Unix systems that makes package management reliable and reproducible.

Researchers from IBM, MIT and Harvard Announced The Release Of DARPA “Common Sense AI” Dataset Along With Two Machine Learning Models At ICML 2021

Building machines that can make decisions based on common sense is no easy feat. A machine must be able to do more than merely find patterns in data; it also needs a way of interpreting the intentions and beliefs behind people’s choices.

At the 2021 International Conference on Machine Learning (ICML), Researchers from IBM, MIT, and Harvard University have come together to release a DARPA “Common Sense AI” dataset for benchmarking AI intuition. They are also releasing two machine learning models that represent different approaches to the problem that relies on testing techniques psychologists use to study infants’ behavior to accelerate the development of AI exhibiting common sense.

Source – Summary – Paper – IBM Blog

100 million protein structures Dataset by DeepMind

DeepMind creates ‘transformative’ map of human proteins drawn by AI. By the end of the year, DeepMind hopes to release predictions for 100 million protein structures, a dataset that will be “transformative for our understanding of how life works,

Here’s a good article about this topic

Google Dataset Search

Google Dataset Search

Malware traffic dataset

Comprises 1914081 records created from all malware traffic analysis .net PCAP files, from 2013 to 2021. The logs are generated using Suricata and Zeek.

Originator: ali_alwashali

Percent of “foreign-born” population in each US and EU state or country.

For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state 🇺🇸🇪🇺

Author: Here

Percent of “foreign-born” population in each US and EU state or country. For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state.

Examples of “foreign-born” in this context:

  • Person born in Spain and living in France is NOT “foreign-born”

  • Person born in Turkey and living in France is “foreign-born”

  • Person born in Florida and living in Texas is NOT “foreign-born”

  • Person born in Mexico and living in Texas is “foreign-born”

  • Person born in Florida and living in France is “foreign-born”

  • Person born in France and living in Florida is “foreign-born”

🇺🇸🇪🇺🗺️

Note: Poland, Ireland, Germany, Greece, Cyprus, Malta, Portugal uses Eurostat 2010 Migration data and Croatia has no data at all

Link1

Link2

Link3

Tools: MS Office

Source: Here

35% of “entry-level” jobs on LinkedIn require 3+ years of experience

r/dataisbeautiful - [OC] 35% of "entry-level" jobs on LinkedIn require 3+ years of experience

Source: LinkedIn data  (see original post)

Tool: Photoshop from my colleague

Latest complete Netflix movie dataset

Created from 4 APIs. 11K+ rows and 30+ attributes of Netflix (Ratings, earnings, actors, language, availability, movie trailers, and many more)

Dataset on Kaggle.

Explore this dataset using FlixGem.com (this dataset is powering this webapp)

Dataset on Google Sheets.

Common Crawl

A corpus of web crawl data composed of over 50 billion web pages. The Common Crawl corpus contains petabytes of data collected since 2008. It contains raw web page data, extracted metadata and text extractions.

AWS CLI Access (No AWS account required)

aws s3 ls s3://commoncrawl/ --no-sign-request

s3://commoncrawl/crawl-data/CC-MAIN-2021-17 – April 2021

 Dataset on protein prices

Data on Primary Commodity Prices are updated monthly based on the IMF’s Primary Commodity Price System.

Excel Database

 CPOST dataset on suicide attacks over four decades

The University of Chicago Project on Security and Threats presents the updated and expanded Database on Suicide Attacks (DSAT), which now links to Uppsala Conflict Data Program data on armed conflicts and includes a new dataset measuring the alliance and rivalry relationships among militant groups with connections to suicide attack groups. Access it here.

Credit Card Dataset – Survey of Consumer Finances (SCF) Combined Extract Data 1989-2019

You can do a lot of aggregated analysis in a pretty straightforward way there.

Drone imagery with annotations for small object detection and tracking dataset

11 TB dataset of drone imagery with annotations for small object detection and tracking

Download and more information are available here

Dataset License: CDLA-Sharing-1.0

Helper scripts for accessing the dataset: DATASET.md

Dataset Exploration: Colab

NOAA High-Resolution Rapid Refresh (HRRR) Model

The HRRR is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model, initialized by 3km grids with 3km radar assimilation. Radar data is assimilated in the HRRR every 15 min over a 1-h period adding further detail to that provided by the hourly data assimilation from the 13km radar-enhanced Rapid Refresh.

Registry of Open Data on AWS

This registry exists to help people discover and share datasets that are available via AWS resources. Learn more about sharing data on AWS.

See all usage examples for datasets listed in this registry.

See datasets from Digital Earth AfricaFacebook Data for GoodNASA Space Act AgreementNIH STRIDESNOAA Big Data ProgramSpace Telescope Science Institute, and Amazon Sustainability Data Initiative.

Textbook Question Answering (TQA)

1,076 textbook lessons, 26,260 questions, 6229 images

Documentation: allenai.org/data/tqa

Download

Harmonized Cancer Datasets: Genomic Data Commons Data Portal

The GDC Data Portal is a robust data-driven platform that allows cancer
researchers and bioinformaticians to search and download cancer data for analysis.

Genomic Data Commons Data Portal
Genomic Data Commons Data Portal

The Cancer Genome Atlas

The Cancer Genome Atlas (TCGA), a collaboration between the National Cancer Institute (NCI) and National Human Genome Research Institute (NHGRI), aims to generate comprehensive, multi-dimensional maps of the key genomic changes in major types and subtypes of cancer.

AWS CLI Access (No AWS account required)

aws s3 ls s3://tcga-2-open/ --no-sign-request

Therapeutically Applicable Research to Generate Effective Treatments (TARGET)

The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) program applies a comprehensive genomic approach to determine molecular changes that drive childhood cancers. The goal of the program is to use data to guide the development of effective, less toxic therapies. TARGET is organized into a collaborative network of disease-specific project teams.  TARGET projects provide comprehensive molecular characterization to determine the genetic changes that drive the initiation and progression of childhood cancers. The dataset contains open Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-Seq Isoform Expression Quantification, miRNA-Seq miRNA Expression Quantification data from Genomic Data Commons (GDC), and open data from GDC Legacy Archive. Access it here.

Genome Aggregation Database (gnomAD)

The Genome Aggregation Database (gnomAD) is a resource developed by an international coalition of investigators that aggregates and harmonizes both exome and genome data from a wide range of large-scale human sequencing projects. The summary data provided here are released for the benefit of the wider scientific community without restriction on use. Downloads

SQuAD (Stanford Question Answering Dataset)

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. Access it here.

PubMed Diabetes Dataset

The Pubmed Diabetes dataset consists of 19717 scientific publications from PubMed database pertaining to diabetes classified into one of three classes. The citation network consists of 44338 links. Each publication in the dataset is described by a TF/IDF weighted word vector from a dictionary which consists of 500 unique words. The README file in the dataset provides more details.

Download Link

Drug-Target Interaction Dataset

This dataset contains interactions between drugs and targets collected from DrugBank, KEGG Drug, DCDB, and Matador. It was originally collected by Perlman et al. It contains 315 drugs, 250 targets, 1,306 drug-target interactions, 5 types of drug-drug similarities, and 3 types of target-target similarities. Drug-drug similarities include Chemical-based, Ligand-based, Expression-based, Side-effect-based, and Annotation-based similarities. Target-target similarities include Sequence-based, Protein-protein interaction network-based, and Gene Ontology-based similarities. The original task on the dataset is to predict new interactions between drugs and targets based on different types of similarities in the network. Download link

Pharmacogenomics Datasets

PharmGKB data and knowledge is available as downloads. It is often critical to check with their curators at feedback@pharmgkb.org before embarking on a large project using these data, to be sure that the files and data they make available are being interpreted correctly. PharmGKB generally does NOT need to be a co-author on such analyses; They just want to make sure that there is a correct understanding of our data before lots of resources are spent.

Pancreatic Cancer Organoid Profiling

The dataset contains open RNA-Seq Gene Expression Quantification data and controlled WGS/WXS/RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic Mutation, and RNA-Seq Splice Junction Quantification. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://gdc-organoid-pancreatic-phs001611-2-open/ --no-sign-request

Africa Soil Information Service (AfSIS) Soil Chemistry

This dataset contains soil infrared spectral data and paired soil property reference measurements for georeferenced soil samples that were collected through the Africa Soil Information Service (AfSIS) project, which lasted from 2009 through 2018. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://afsis/ --no-sign-request

Dataset for Affective States in E-Environments

DAiSEE is the first multi-label video classification dataset comprising of 9068 video snippets captured from 112 users for recognizing the user affective states of boredom, confusion, engagement, and frustration “in the wild”. The dataset has four levels of labels namely – very low, low, high, and very high for each of the affective states, which are crowd annotated and correlated with a gold standard annotation created using a team of expert psychologists. Download it here.

NatureServe Explorer Dataset

NatureServe Explorer provides conservation status, taxonomy, distribution, and life history information for more than 95,000 plants and animals in the United States and Canada, and more than 10,000 vegetation communities and ecological systems in the Western Hemisphere.

The data available through NatureServe Explorer represents data managed in the NatureServe Central Databases. These databases are dynamic, being continually enhanced and refined through the input of hundreds of natural heritage program scientists and other collaborators. NatureServe Explorer is updated from these central databases to reflect information from new field surveys, the latest taxonomic treatments and other scientific publications, and new conservation status assessments. Explore Data here

Flight Records in the US

Airline On-Time Performance and Causes of Flight Delays – On_Time Data.

This database contains scheduled and actual departure and arrival times, reason of delay. reported by certified U.S. air carriers that account for at least one percent of domestic scheduled passenger revenues. The data is collected by the Office of Airline Information, Bureau of Transportation Statistics (BTS).

FlightAware.com has data but you need to pay for a full dataset.

The anyflights package supplies a set of functions to generate air travel data (and data packages!) similar to nycflights13. With a user-defined year and airport, the anyflights function will grab data on:

  • flights: all flights that departed a given airport in a given year and month
  • weather: hourly meterological data for a given airport in a given year and month
  • airports: airport names, FAA codes, and locations
  • airlines: translation between two letter carrier (airline) codes and names
  • planes: construction information about each plane found in flights

Airline On-Time Statistics and Delay Causes

The U.S. Department of Transportation’s (DOT) Bureau of Transportation Statistics (BTS) tracks the on-time performance of domestic flights operated by large air carriers. Summary information on the number of on-time, delayed, canceled and diverted flights appears in DOT’s monthly Air Travel Consumer Report, published about 30 days after the month’s end, as well as in summary tables posted on this website. BTS began collecting details on the causes of flight delays in June 2003. Summary statistics and raw data are made available to the public at the time the Air Travel Consumer Report is released. Access it here

Worldwide flight data

Open flights: As of January 2017, the OpenFlights Airports Database contains over 10,000 airports, train stations and ferry terminals spanning the globe

Download: airports.dat (Airports only, high quality)

Download: airports-extended.dat (Airports, train stations and ferry terminals, including user contributions)

Bureau of Transportation:

Flightera.net seems to have a lot of good data for free. It has in-depth data on flights and doesn’t seem limited by date. I can’t speak on the validity of the data though.

flightradar24.com has lots of data, also historically, they might be willing to help you get it in a nice format.

 

2019 Crime statistics in the USA

Dataset with arrest in US by race and separate states. Download Excel here

  • JavaScript
  • Swift – Apple’s compiled programming language that is secure, modern, programmer-friendly, and fast.
  • Python – General-purpose programming language designed for readability.
    • Asyncio – Asynchronous I/O in Python 3.
    • Scientific Audio – Scientific research in audio/music.
    • CircuitPython – A version of Python for microcontrollers.
    • Data Science – Data analysis and machine learning.
    • Typing – Optional static typing for Python.
    • MicroPython – A lean and efficient implementation of Python 3 for microcontrollers.
  • Rust
  • Haskell
  • PureScript
  • Go
  • Scala
    • Scala Native – Optimizing ahead-of-time compiler for Scala based on LLVM.
  • Ruby
  • Clojure
  • ClojureScript
  • Elixir
  • Elm
  • Erlang
  • Julia – High-level dynamic programming language designed to address the needs of high-performance numerical analysis and computational science.
  • Lua
  • C
  • C/C++ – General-purpose language with a bias toward system programming and embedded, resource-constrained software.
  • R – Functional programming language and environment for statistical computing and graphics.
  • D
  • Common Lisp – Powerful dynamic multiparadigm language that facilitates iterative and interactive development.
  • Perl
  • Groovy
  • Dart
  • Java – Popular secure object-oriented language designed for flexibility to “write once, run anywhere”.
  • Kotlin
  • OCaml
  • ColdFusion
  • Fortran
  • PHP – Server-side scripting language.
  • Pascal
  • AutoHotkey
  • AutoIt
  • Crystal
  • Frege – Haskell for the JVM.
  • CMake – Build, test, and package software.
  • ActionScript 3 – Object-oriented language targeting Adobe AIR.
  • Eta – Functional programming language for the JVM.
  • Idris – General purpose pure functional programming language with dependent types influenced by Haskell and ML.
  • Ada/SPARK – Modern programming language designed for large, long-lived apps where reliability and efficiency are essential.
  • Q# – Domain-specific programming language used for expressing quantum algorithms.
  • Imba – Programming language inspired by Ruby and Python and compiles to performant JavaScript.
  • Vala – Programming language designed to take full advantage of the GLib and GNOME ecosystems, while preserving the speed of C code.
  • Coq – Formal language and environment for programming and specification which facilitates interactive development of machine-checked proofs.
  • V – Simple, fast, safe, compiled language for developing maintainable software.

Researchers from IBM, MIT and Harvard Announced The Release Of DARPA “Common Sense AI” Dataset Along With Two Machine Learning Models At ICML 2021

Building machines that can make decisions based on common sense is no easy feat. A machine must be able to do more than merely find patterns in data; it also needs a way of interpreting the intentions and beliefs behind people’s choices.

At the 2021 International Conference on Machine Learning (ICML), Researchers from IBM, MIT, and Harvard University have come together to release a DARPA “Common Sense AI” dataset for benchmarking AI intuition. They are also releasing two machine learning models that represent different approaches to the problem that relies on testing techniques psychologists use to study infants’ behavior to accelerate the development of AI exhibiting common sense.

Source – Summary – Paper – IBM Blog

100 million protein structures Dataset by DeepMind

DeepMind creates ‘transformative’ map of human proteins drawn by AI. By the end of the year, DeepMind hopes to release predictions for 100 million protein structures, a dataset that will be “transformative for our understanding of how life works,

Here’s a good article about this topic

Google Dataset Search

Google Dataset Search

Malware traffic dataset

Comprises 1914081 records created from all malware traffic analysis .net PCAP files, from 2013 to 2021. The logs are generated using Suricata and Zeek.

Originator: ali_alwashali

Percent of “foreign-born” population in each US and EU state or country.

For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state 🇺🇸🇪🇺

Author: Here

Percent of “foreign-born” population in each US and EU state or country. For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state.

Examples of “foreign-born” in this context:

  • Person born in Spain and living in France is NOT “foreign-born”

  • Person born in Turkey and living in France is “foreign-born”

  • Person born in Florida and living in Texas is NOT “foreign-born”

  • Person born in Mexico and living in Texas is “foreign-born”

  • Person born in Florida and living in France is “foreign-born”

  • Person born in France and living in Florida is “foreign-born”

🇺🇸🇪🇺🗺️

Note: Poland, Ireland, Germany, Greece, Cyprus, Malta, Portugal uses Eurostat 2010 Migration data and Croatia has no data at all

Link1

Link2

Link3

Tools: MS Office

Source: Here

35% of “entry-level” jobs on LinkedIn require 3+ years of experience

r/dataisbeautiful - [OC] 35% of "entry-level" jobs on LinkedIn require 3+ years of experience

Source: LinkedIn data  (see original post)

Tool: Photoshop from my colleague

Latest complete Netflix movie dataset

Created from 4 APIs. 11K+ rows and 30+ attributes of Netflix (Ratings, earnings, actors, language, availability, movie trailers, and many more)

Dataset on Kaggle.

Explore this dataset using FlixGem.com (this dataset is powering this webapp)

Dataset on Google Sheets.

Common Crawl

A corpus of web crawl data composed of over 50 billion web pages. The Common Crawl corpus contains petabytes of data collected since 2008. It contains raw web page data, extracted metadata and text extractions.

AWS CLI Access (No AWS account required)

aws s3 ls s3://commoncrawl/ --no-sign-request

s3://commoncrawl/crawl-data/CC-MAIN-2021-17 – April 2021

 Dataset on protein prices

Data on Primary Commodity Prices are updated monthly based on the IMF’s Primary Commodity Price System.

Excel Database

 CPOST dataset on suicide attacks over four decades

The University of Chicago Project on Security and Threats presents the updated and expanded Database on Suicide Attacks (DSAT), which now links to Uppsala Conflict Data Program data on armed conflicts and includes a new dataset measuring the alliance and rivalry relationships among militant groups with connections to suicide attack groups. Access it here.

Credit Card Dataset – Survey of Consumer Finances (SCF) Combined Extract Data 1989-2019

You can do a lot of aggregated analysis in a pretty straightforward way there.

Drone imagery with annotations for small object detection and tracking dataset

11 TB dataset of drone imagery with annotations for small object detection and tracking

Download and more information are available here

Dataset License: CDLA-Sharing-1.0

Helper scripts for accessing the dataset: DATASET.md

Dataset Exploration: Colab

NOAA High-Resolution Rapid Refresh (HRRR) Model

The HRRR is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model, initialized by 3km grids with 3km radar assimilation. Radar data is assimilated in the HRRR every 15 min over a 1-h period adding further detail to that provided by the hourly data assimilation from the 13km radar-enhanced Rapid Refresh.

Registry of Open Data on AWS

This registry exists to help people discover and share datasets that are available via AWS resources. Learn more about sharing data on AWS.

See all usage examples for datasets listed in this registry.

See datasets from Digital Earth AfricaFacebook Data for GoodNASA Space Act AgreementNIH STRIDESNOAA Big Data ProgramSpace Telescope Science Institute, and Amazon Sustainability Data Initiative.

Textbook Question Answering (TQA)

1,076 textbook lessons, 26,260 questions, 6229 images

Documentation: allenai.org/data/tqa

Download

Harmonized Cancer Datasets: Genomic Data Commons Data Portal

The GDC Data Portal is a robust data-driven platform that allows cancer
researchers and bioinformaticians to search and download cancer data for analysis.

Genomic Data Commons Data Portal
Genomic Data Commons Data Portal

The Cancer Genome Atlas

The Cancer Genome Atlas (TCGA), a collaboration between the National Cancer Institute (NCI) and National Human Genome Research Institute (NHGRI), aims to generate comprehensive, multi-dimensional maps of the key genomic changes in major types and subtypes of cancer.

AWS CLI Access (No AWS account required)

aws s3 ls s3://tcga-2-open/ --no-sign-request

Therapeutically Applicable Research to Generate Effective Treatments (TARGET)

The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) program applies a comprehensive genomic approach to determine molecular changes that drive childhood cancers. The goal of the program is to use data to guide the development of effective, less toxic therapies. TARGET is organized into a collaborative network of disease-specific project teams.  TARGET projects provide comprehensive molecular characterization to determine the genetic changes that drive the initiation and progression of childhood cancers. The dataset contains open Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-Seq Isoform Expression Quantification, miRNA-Seq miRNA Expression Quantification data from Genomic Data Commons (GDC), and open data from GDC Legacy Archive. Access it here.

Genome Aggregation Database (gnomAD)

The Genome Aggregation Database (gnomAD) is a resource developed by an international coalition of investigators that aggregates and harmonizes both exome and genome data from a wide range of large-scale human sequencing projects. The summary data provided here are released for the benefit of the wider scientific community without restriction on use. Downloads

SQuAD (Stanford Question Answering Dataset)

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. Access it here.

PubMed Diabetes Dataset

The Pubmed Diabetes dataset consists of 19717 scientific publications from PubMed database pertaining to diabetes classified into one of three classes. The citation network consists of 44338 links. Each publication in the dataset is described by a TF/IDF weighted word vector from a dictionary which consists of 500 unique words. The README file in the dataset provides more details.

Download Link

Drug-Target Interaction Dataset

This dataset contains interactions between drugs and targets collected from DrugBank, KEGG Drug, DCDB, and Matador. It was originally collected by Perlman et al. It contains 315 drugs, 250 targets, 1,306 drug-target interactions, 5 types of drug-drug similarities, and 3 types of target-target similarities. Drug-drug similarities include Chemical-based, Ligand-based, Expression-based, Side-effect-based, and Annotation-based similarities. Target-target similarities include Sequence-based, Protein-protein interaction network-based, and Gene Ontology-based similarities. The original task on the dataset is to predict new interactions between drugs and targets based on different types of similarities in the network. Download link

Pharmacogenomics Datasets

PharmGKB data and knowledge is available as downloads. It is often critical to check with their curators at feedback@pharmgkb.org before embarking on a large project using these data, to be sure that the files and data they make available are being interpreted correctly. PharmGKB generally does NOT need to be a co-author on such analyses; They just want to make sure that there is a correct understanding of our data before lots of resources are spent.

Pancreatic Cancer Organoid Profiling

The dataset contains open RNA-Seq Gene Expression Quantification data and controlled WGS/WXS/RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic Mutation, and RNA-Seq Splice Junction Quantification. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://gdc-organoid-pancreatic-phs001611-2-open/ --no-sign-request

Africa Soil Information Service (AfSIS) Soil Chemistry

This dataset contains soil infrared spectral data and paired soil property reference measurements for georeferenced soil samples that were collected through the Africa Soil Information Service (AfSIS) project, which lasted from 2009 through 2018. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://afsis/ --no-sign-request

Dataset for Affective States in E-Environments

DAiSEE is the first multi-label video classification dataset comprising of 9068 video snippets captured from 112 users for recognizing the user affective states of boredom, confusion, engagement, and frustration “in the wild”. The dataset has four levels of labels namely – very low, low, high, and very high for each of the affective states, which are crowd annotated and correlated with a gold standard annotation created using a team of expert psychologists. Download it here.

NatureServe Explorer Dataset

NatureServe Explorer provides conservation status, taxonomy, distribution, and life history information for more than 95,000 plants and animals in the United States and Canada, and more than 10,000 vegetation communities and ecological systems in the Western Hemisphere.

The data available through NatureServe Explorer represents data managed in the NatureServe Central Databases. These databases are dynamic, being continually enhanced and refined through the input of hundreds of natural heritage program scientists and other collaborators. NatureServe Explorer is updated from these central databases to reflect information from new field surveys, the latest taxonomic treatments and other scientific publications, and new conservation status assessments. Explore Data here

Flight Records in the US

Airline On-Time Performance and Causes of Flight Delays – On_Time Data.

This database contains scheduled and actual departure and arrival times, reason of delay. reported by certified U.S. air carriers that account for at least one percent of domestic scheduled passenger revenues. The data is collected by the Office of Airline Information, Bureau of Transportation Statistics (BTS).

FlightAware.com has data but you need to pay for a full dataset.

The anyflights package supplies a set of functions to generate air travel data (and data packages!) similar to nycflights13. With a user-defined year and airport, the anyflights function will grab data on:

  • flights: all flights that departed a given airport in a given year and month
  • weather: hourly meterological data for a given airport in a given year and month
  • airports: airport names, FAA codes, and locations
  • airlines: translation between two letter carrier (airline) codes and names
  • planes: construction information about each plane found in flights

Airline On-Time Statistics and Delay Causes

The U.S. Department of Transportation’s (DOT) Bureau of Transportation Statistics (BTS) tracks the on-time performance of domestic flights operated by large air carriers. Summary information on the number of on-time, delayed, canceled and diverted flights appears in DOT’s monthly Air Travel Consumer Report, published about 30 days after the month’s end, as well as in summary tables posted on this website. BTS began collecting details on the causes of flight delays in June 2003. Summary statistics and raw data are made available to the public at the time the Air Travel Consumer Report is released. Access it here

Worldwide flight data

Open flights: As of January 2017, the OpenFlights Airports Database contains over 10,000 airports, train stations and ferry terminals spanning the globe

Download: airports.dat (Airports only, high quality)

Download: airports-extended.dat (Airports, train stations and ferry terminals, including user contributions)

Bureau of Transportation:

Flightera.net seems to have a lot of good data for free. It has in-depth data on flights and doesn’t seem limited by date. I can’t speak on the validity of the data though.

flightradar24.com has lots of data, also historically, they might be willing to help you get it in a nice format.

 

2019 Crime statistics in the USA

Dataset with arrest in US by race and separate states. Download Excel here

Researchers from IBM, MIT and Harvard Announced The Release Of DARPA “Common Sense AI” Dataset Along With Two Machine Learning Models At ICML 2021

Building machines that can make decisions based on common sense is no easy feat. A machine must be able to do more than merely find patterns in data; it also needs a way of interpreting the intentions and beliefs behind people’s choices.

At the 2021 International Conference on Machine Learning (ICML), Researchers from IBM, MIT, and Harvard University have come together to release a DARPA “Common Sense AI” dataset for benchmarking AI intuition. They are also releasing two machine learning models that represent different approaches to the problem that relies on testing techniques psychologists use to study infants’ behavior to accelerate the development of AI exhibiting common sense.

Source – Summary – Paper – IBM Blog

100 million protein structures Dataset by DeepMind

DeepMind creates ‘transformative’ map of human proteins drawn by AI. By the end of the year, DeepMind hopes to release predictions for 100 million protein structures, a dataset that will be “transformative for our understanding of how life works,

Here’s a good article about this topic

Google Dataset Search

Google Dataset Search

Malware traffic dataset

Comprises 1914081 records created from all malware traffic analysis .net PCAP files, from 2013 to 2021. The logs are generated using Suricata and Zeek.

Originator: ali_alwashali

Percent of “foreign-born” population in each US and EU state or country.

For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state 🇺🇸🇪🇺

Author: Here

Percent of “foreign-born” population in each US and EU state or country. For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state.

Examples of “foreign-born” in this context:

  • Person born in Spain and living in France is NOT “foreign-born”

  • Person born in Turkey and living in France is “foreign-born”

  • Person born in Florida and living in Texas is NOT “foreign-born”

  • Person born in Mexico and living in Texas is “foreign-born”

  • Person born in Florida and living in France is “foreign-born”

  • Person born in France and living in Florida is “foreign-born”

🇺🇸🇪🇺🗺️

Note: Poland, Ireland, Germany, Greece, Cyprus, Malta, Portugal uses Eurostat 2010 Migration data and Croatia has no data at all

Link1

Link2

Link3

Tools: MS Office

Source: Here

35% of “entry-level” jobs on LinkedIn require 3+ years of experience

r/dataisbeautiful - [OC] 35% of "entry-level" jobs on LinkedIn require 3+ years of experience

Source: LinkedIn data  (see original post)

Tool: Photoshop from my colleague

Latest complete Netflix movie dataset

Created from 4 APIs. 11K+ rows and 30+ attributes of Netflix (Ratings, earnings, actors, language, availability, movie trailers, and many more)

Dataset on Kaggle.

Explore this dataset using FlixGem.com (this dataset is powering this webapp)

Dataset on Google Sheets.

Common Crawl

A corpus of web crawl data composed of over 50 billion web pages. The Common Crawl corpus contains petabytes of data collected since 2008. It contains raw web page data, extracted metadata and text extractions.

AWS CLI Access (No AWS account required)

aws s3 ls s3://commoncrawl/ --no-sign-request

s3://commoncrawl/crawl-data/CC-MAIN-2021-17 – April 2021

 Dataset on protein prices

Data on Primary Commodity Prices are updated monthly based on the IMF’s Primary Commodity Price System.

Excel Database

 CPOST dataset on suicide attacks over four decades

The University of Chicago Project on Security and Threats presents the updated and expanded Database on Suicide Attacks (DSAT), which now links to Uppsala Conflict Data Program data on armed conflicts and includes a new dataset measuring the alliance and rivalry relationships among militant groups with connections to suicide attack groups. Access it here.

Credit Card Dataset – Survey of Consumer Finances (SCF) Combined Extract Data 1989-2019

You can do a lot of aggregated analysis in a pretty straightforward way there.

Drone imagery with annotations for small object detection and tracking dataset

11 TB dataset of drone imagery with annotations for small object detection and tracking

Download and more information are available here

Dataset License: CDLA-Sharing-1.0

Helper scripts for accessing the dataset: DATASET.md

Dataset Exploration: Colab

NOAA High-Resolution Rapid Refresh (HRRR) Model

The HRRR is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model, initialized by 3km grids with 3km radar assimilation. Radar data is assimilated in the HRRR every 15 min over a 1-h period adding further detail to that provided by the hourly data assimilation from the 13km radar-enhanced Rapid Refresh.

Registry of Open Data on AWS

This registry exists to help people discover and share datasets that are available via AWS resources. Learn more about sharing data on AWS.

See all usage examples for datasets listed in this registry.

See datasets from Digital Earth AfricaFacebook Data for GoodNASA Space Act AgreementNIH STRIDESNOAA Big Data ProgramSpace Telescope Science Institute, and Amazon Sustainability Data Initiative.

Textbook Question Answering (TQA)

1,076 textbook lessons, 26,260 questions, 6229 images

Documentation: allenai.org/data/tqa

Download

Harmonized Cancer Datasets: Genomic Data Commons Data Portal

The GDC Data Portal is a robust data-driven platform that allows cancer
researchers and bioinformaticians to search and download cancer data for analysis.

Genomic Data Commons Data Portal
Genomic Data Commons Data Portal

The Cancer Genome Atlas

The Cancer Genome Atlas (TCGA), a collaboration between the National Cancer Institute (NCI) and National Human Genome Research Institute (NHGRI), aims to generate comprehensive, multi-dimensional maps of the key genomic changes in major types and subtypes of cancer.

AWS CLI Access (No AWS account required)

aws s3 ls s3://tcga-2-open/ --no-sign-request

Therapeutically Applicable Research to Generate Effective Treatments (TARGET)

The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) program applies a comprehensive genomic approach to determine molecular changes that drive childhood cancers. The goal of the program is to use data to guide the development of effective, less toxic therapies. TARGET is organized into a collaborative network of disease-specific project teams.  TARGET projects provide comprehensive molecular characterization to determine the genetic changes that drive the initiation and progression of childhood cancers. The dataset contains open Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-Seq Isoform Expression Quantification, miRNA-Seq miRNA Expression Quantification data from Genomic Data Commons (GDC), and open data from GDC Legacy Archive. Access it here.

Genome Aggregation Database (gnomAD)

The Genome Aggregation Database (gnomAD) is a resource developed by an international coalition of investigators that aggregates and harmonizes both exome and genome data from a wide range of large-scale human sequencing projects. The summary data provided here are released for the benefit of the wider scientific community without restriction on use. Downloads

SQuAD (Stanford Question Answering Dataset)

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. Access it here.

PubMed Diabetes Dataset

The Pubmed Diabetes dataset consists of 19717 scientific publications from PubMed database pertaining to diabetes classified into one of three classes. The citation network consists of 44338 links. Each publication in the dataset is described by a TF/IDF weighted word vector from a dictionary which consists of 500 unique words. The README file in the dataset provides more details.

Download Link

Drug-Target Interaction Dataset

This dataset contains interactions between drugs and targets collected from DrugBank, KEGG Drug, DCDB, and Matador. It was originally collected by Perlman et al. It contains 315 drugs, 250 targets, 1,306 drug-target interactions, 5 types of drug-drug similarities, and 3 types of target-target similarities. Drug-drug similarities include Chemical-based, Ligand-based, Expression-based, Side-effect-based, and Annotation-based similarities. Target-target similarities include Sequence-based, Protein-protein interaction network-based, and Gene Ontology-based similarities. The original task on the dataset is to predict new interactions between drugs and targets based on different types of similarities in the network. Download link

Pharmacogenomics Datasets

PharmGKB data and knowledge is available as downloads. It is often critical to check with their curators at feedback@pharmgkb.org before embarking on a large project using these data, to be sure that the files and data they make available are being interpreted correctly. PharmGKB generally does NOT need to be a co-author on such analyses; They just want to make sure that there is a correct understanding of our data before lots of resources are spent.

Pancreatic Cancer Organoid Profiling

The dataset contains open RNA-Seq Gene Expression Quantification data and controlled WGS/WXS/RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic Mutation, and RNA-Seq Splice Junction Quantification. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://gdc-organoid-pancreatic-phs001611-2-open/ --no-sign-request

Africa Soil Information Service (AfSIS) Soil Chemistry

This dataset contains soil infrared spectral data and paired soil property reference measurements for georeferenced soil samples that were collected through the Africa Soil Information Service (AfSIS) project, which lasted from 2009 through 2018. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://afsis/ --no-sign-request

Dataset for Affective States in E-Environments

DAiSEE is the first multi-label video classification dataset comprising of 9068 video snippets captured from 112 users for recognizing the user affective states of boredom, confusion, engagement, and frustration “in the wild”. The dataset has four levels of labels namely – very low, low, high, and very high for each of the affective states, which are crowd annotated and correlated with a gold standard annotation created using a team of expert psychologists. Download it here.

NatureServe Explorer Dataset

NatureServe Explorer provides conservation status, taxonomy, distribution, and life history information for more than 95,000 plants and animals in the United States and Canada, and more than 10,000 vegetation communities and ecological systems in the Western Hemisphere.

The data available through NatureServe Explorer represents data managed in the NatureServe Central Databases. These databases are dynamic, being continually enhanced and refined through the input of hundreds of natural heritage program scientists and other collaborators. NatureServe Explorer is updated from these central databases to reflect information from new field surveys, the latest taxonomic treatments and other scientific publications, and new conservation status assessments. Explore Data here

Flight Records in the US

Airline On-Time Performance and Causes of Flight Delays – On_Time Data.

This database contains scheduled and actual departure and arrival times, reason of delay. reported by certified U.S. air carriers that account for at least one percent of domestic scheduled passenger revenues. The data is collected by the Office of Airline Information, Bureau of Transportation Statistics (BTS).

FlightAware.com has data but you need to pay for a full dataset.

The anyflights package supplies a set of functions to generate air travel data (and data packages!) similar to nycflights13. With a user-defined year and airport, the anyflights function will grab data on:

  • flights: all flights that departed a given airport in a given year and month
  • weather: hourly meterological data for a given airport in a given year and month
  • airports: airport names, FAA codes, and locations
  • airlines: translation between two letter carrier (airline) codes and names
  • planes: construction information about each plane found in flights

Airline On-Time Statistics and Delay Causes

The U.S. Department of Transportation’s (DOT) Bureau of Transportation Statistics (BTS) tracks the on-time performance of domestic flights operated by large air carriers. Summary information on the number of on-time, delayed, canceled and diverted flights appears in DOT’s monthly Air Travel Consumer Report, published about 30 days after the month’s end, as well as in summary tables posted on this website. BTS began collecting details on the causes of flight delays in June 2003. Summary statistics and raw data are made available to the public at the time the Air Travel Consumer Report is released. Access it here

Worldwide flight data

Open flights: As of January 2017, the OpenFlights Airports Database contains over 10,000 airports, train stations and ferry terminals spanning the globe

Download: airports.dat (Airports only, high quality)

Download: airports-extended.dat (Airports, train stations and ferry terminals, including user contributions)

Bureau of Transportation:

Flightera.net seems to have a lot of good data for free. It has in-depth data on flights and doesn’t seem limited by date. I can’t speak on the validity of the data though.

flightradar24.com has lots of data, also historically, they might be willing to help you get it in a nice format.

 

2019 Crime statistics in the USA

Dataset with arrest in US by race and separate states. Download Excel here

  • Flask – Python framework.
  • Docker
  • Vagrant – Automation virtual machine environment.
  • Pyramid – Python framework.
  • Play1 Framework
  • CakePHP – PHP framework.
  • Symfony – PHP framework.
  • Laravel – PHP framework.
    • Education
    • TALL Stack – Full-stack development solution featuring libraries built by the Laravel community.
  • Rails – Web app framework for Ruby.
  • Phalcon – PHP framework.
  • Useful .htaccess Snippets
  • nginx – Web server.
  • Dropwizard – Java framework.
  • Kubernetes – Open-source platform that automates Linux container operations.
  • Lumen – PHP micro-framework.
  • Serverless Framework – Serverless computing and serverless architectures.
  • Apache Wicket – Java web app framework.
  • Vert.x – Toolkit for building reactive apps on the JVM.
  • Terraform – Tool for building, changing, and versioning infrastructure.
  • Vapor – Server-side development in Swift.
  • Dash – Python web app framework.
  • FastAPI – Python web app framework.
  • CDK – Open-source software development framework for defining cloud infrastructure in code.
  • IAM – User accounts, authentication and authorization.
  • Chalice – Python framework for serverless app development on AWS Lambda.

Researchers from IBM, MIT and Harvard Announced The Release Of DARPA “Common Sense AI” Dataset Along With Two Machine Learning Models At ICML 2021

Building machines that can make decisions based on common sense is no easy feat. A machine must be able to do more than merely find patterns in data; it also needs a way of interpreting the intentions and beliefs behind people’s choices.

At the 2021 International Conference on Machine Learning (ICML), Researchers from IBM, MIT, and Harvard University have come together to release a DARPA “Common Sense AI” dataset for benchmarking AI intuition. They are also releasing two machine learning models that represent different approaches to the problem that relies on testing techniques psychologists use to study infants’ behavior to accelerate the development of AI exhibiting common sense.

Source – Summary – Paper – IBM Blog

100 million protein structures Dataset by DeepMind

DeepMind creates ‘transformative’ map of human proteins drawn by AI. By the end of the year, DeepMind hopes to release predictions for 100 million protein structures, a dataset that will be “transformative for our understanding of how life works,

Here’s a good article about this topic

Google Dataset Search

Google Dataset Search

Malware traffic dataset

Comprises 1914081 records created from all malware traffic analysis .net PCAP files, from 2013 to 2021. The logs are generated using Suricata and Zeek.

Originator: ali_alwashali

Percent of “foreign-born” population in each US and EU state or country.

For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state 🇺🇸🇪🇺

Author: Here

Percent of “foreign-born” population in each US and EU state or country. For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state.

Examples of “foreign-born” in this context:

  • Person born in Spain and living in France is NOT “foreign-born”

  • Person born in Turkey and living in France is “foreign-born”

  • Person born in Florida and living in Texas is NOT “foreign-born”

  • Person born in Mexico and living in Texas is “foreign-born”

  • Person born in Florida and living in France is “foreign-born”

  • Person born in France and living in Florida is “foreign-born”

🇺🇸🇪🇺🗺️

Note: Poland, Ireland, Germany, Greece, Cyprus, Malta, Portugal uses Eurostat 2010 Migration data and Croatia has no data at all

Link1

Link2

Link3

Tools: MS Office

Source: Here

35% of “entry-level” jobs on LinkedIn require 3+ years of experience

r/dataisbeautiful - [OC] 35% of "entry-level" jobs on LinkedIn require 3+ years of experience

Source: LinkedIn data  (see original post)

Tool: Photoshop from my colleague

Latest complete Netflix movie dataset

Created from 4 APIs. 11K+ rows and 30+ attributes of Netflix (Ratings, earnings, actors, language, availability, movie trailers, and many more)

Dataset on Kaggle.

Explore this dataset using FlixGem.com (this dataset is powering this webapp)

Dataset on Google Sheets.

Common Crawl

A corpus of web crawl data composed of over 50 billion web pages. The Common Crawl corpus contains petabytes of data collected since 2008. It contains raw web page data, extracted metadata and text extractions.

AWS CLI Access (No AWS account required)

aws s3 ls s3://commoncrawl/ --no-sign-request

s3://commoncrawl/crawl-data/CC-MAIN-2021-17 – April 2021

 Dataset on protein prices

Data on Primary Commodity Prices are updated monthly based on the IMF’s Primary Commodity Price System.

Excel Database

 CPOST dataset on suicide attacks over four decades

The University of Chicago Project on Security and Threats presents the updated and expanded Database on Suicide Attacks (DSAT), which now links to Uppsala Conflict Data Program data on armed conflicts and includes a new dataset measuring the alliance and rivalry relationships among militant groups with connections to suicide attack groups. Access it here.

Credit Card Dataset – Survey of Consumer Finances (SCF) Combined Extract Data 1989-2019

You can do a lot of aggregated analysis in a pretty straightforward way there.

Drone imagery with annotations for small object detection and tracking dataset

11 TB dataset of drone imagery with annotations for small object detection and tracking

Download and more information are available here

Dataset License: CDLA-Sharing-1.0

Helper scripts for accessing the dataset: DATASET.md

Dataset Exploration: Colab

NOAA High-Resolution Rapid Refresh (HRRR) Model

The HRRR is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model, initialized by 3km grids with 3km radar assimilation. Radar data is assimilated in the HRRR every 15 min over a 1-h period adding further detail to that provided by the hourly data assimilation from the 13km radar-enhanced Rapid Refresh.

Registry of Open Data on AWS

This registry exists to help people discover and share datasets that are available via AWS resources. Learn more about sharing data on AWS.

See all usage examples for datasets listed in this registry.

See datasets from Digital Earth AfricaFacebook Data for GoodNASA Space Act AgreementNIH STRIDESNOAA Big Data ProgramSpace Telescope Science Institute, and Amazon Sustainability Data Initiative.

Textbook Question Answering (TQA)

1,076 textbook lessons, 26,260 questions, 6229 images

Documentation: allenai.org/data/tqa

Download

Harmonized Cancer Datasets: Genomic Data Commons Data Portal

The GDC Data Portal is a robust data-driven platform that allows cancer
researchers and bioinformaticians to search and download cancer data for analysis.

Genomic Data Commons Data Portal
Genomic Data Commons Data Portal

The Cancer Genome Atlas

The Cancer Genome Atlas (TCGA), a collaboration between the National Cancer Institute (NCI) and National Human Genome Research Institute (NHGRI), aims to generate comprehensive, multi-dimensional maps of the key genomic changes in major types and subtypes of cancer.

AWS CLI Access (No AWS account required)

aws s3 ls s3://tcga-2-open/ --no-sign-request

Therapeutically Applicable Research to Generate Effective Treatments (TARGET)

The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) program applies a comprehensive genomic approach to determine molecular changes that drive childhood cancers. The goal of the program is to use data to guide the development of effective, less toxic therapies. TARGET is organized into a collaborative network of disease-specific project teams.  TARGET projects provide comprehensive molecular characterization to determine the genetic changes that drive the initiation and progression of childhood cancers. The dataset contains open Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-Seq Isoform Expression Quantification, miRNA-Seq miRNA Expression Quantification data from Genomic Data Commons (GDC), and open data from GDC Legacy Archive. Access it here.

Genome Aggregation Database (gnomAD)

The Genome Aggregation Database (gnomAD) is a resource developed by an international coalition of investigators that aggregates and harmonizes both exome and genome data from a wide range of large-scale human sequencing projects. The summary data provided here are released for the benefit of the wider scientific community without restriction on use. Downloads

SQuAD (Stanford Question Answering Dataset)

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. Access it here.

PubMed Diabetes Dataset

The Pubmed Diabetes dataset consists of 19717 scientific publications from PubMed database pertaining to diabetes classified into one of three classes. The citation network consists of 44338 links. Each publication in the dataset is described by a TF/IDF weighted word vector from a dictionary which consists of 500 unique words. The README file in the dataset provides more details.

Download Link

Drug-Target Interaction Dataset

This dataset contains interactions between drugs and targets collected from DrugBank, KEGG Drug, DCDB, and Matador. It was originally collected by Perlman et al. It contains 315 drugs, 250 targets, 1,306 drug-target interactions, 5 types of drug-drug similarities, and 3 types of target-target similarities. Drug-drug similarities include Chemical-based, Ligand-based, Expression-based, Side-effect-based, and Annotation-based similarities. Target-target similarities include Sequence-based, Protein-protein interaction network-based, and Gene Ontology-based similarities. The original task on the dataset is to predict new interactions between drugs and targets based on different types of similarities in the network. Download link

Pharmacogenomics Datasets

PharmGKB data and knowledge is available as downloads. It is often critical to check with their curators at feedback@pharmgkb.org before embarking on a large project using these data, to be sure that the files and data they make available are being interpreted correctly. PharmGKB generally does NOT need to be a co-author on such analyses; They just want to make sure that there is a correct understanding of our data before lots of resources are spent.

Pancreatic Cancer Organoid Profiling

The dataset contains open RNA-Seq Gene Expression Quantification data and controlled WGS/WXS/RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic Mutation, and RNA-Seq Splice Junction Quantification. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://gdc-organoid-pancreatic-phs001611-2-open/ --no-sign-request

Africa Soil Information Service (AfSIS) Soil Chemistry

This dataset contains soil infrared spectral data and paired soil property reference measurements for georeferenced soil samples that were collected through the Africa Soil Information Service (AfSIS) project, which lasted from 2009 through 2018. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://afsis/ --no-sign-request

Dataset for Affective States in E-Environments

DAiSEE is the first multi-label video classification dataset comprising of 9068 video snippets captured from 112 users for recognizing the user affective states of boredom, confusion, engagement, and frustration “in the wild”. The dataset has four levels of labels namely – very low, low, high, and very high for each of the affective states, which are crowd annotated and correlated with a gold standard annotation created using a team of expert psychologists. Download it here.

NatureServe Explorer Dataset

NatureServe Explorer provides conservation status, taxonomy, distribution, and life history information for more than 95,000 plants and animals in the United States and Canada, and more than 10,000 vegetation communities and ecological systems in the Western Hemisphere.

The data available through NatureServe Explorer represents data managed in the NatureServe Central Databases. These databases are dynamic, being continually enhanced and refined through the input of hundreds of natural heritage program scientists and other collaborators. NatureServe Explorer is updated from these central databases to reflect information from new field surveys, the latest taxonomic treatments and other scientific publications, and new conservation status assessments. Explore Data here

Flight Records in the US

Airline On-Time Performance and Causes of Flight Delays – On_Time Data.

This database contains scheduled and actual departure and arrival times, reason of delay. reported by certified U.S. air carriers that account for at least one percent of domestic scheduled passenger revenues. The data is collected by the Office of Airline Information, Bureau of Transportation Statistics (BTS).

FlightAware.com has data but you need to pay for a full dataset.

The anyflights package supplies a set of functions to generate air travel data (and data packages!) similar to nycflights13. With a user-defined year and airport, the anyflights function will grab data on:

  • flights: all flights that departed a given airport in a given year and month
  • weather: hourly meterological data for a given airport in a given year and month
  • airports: airport names, FAA codes, and locations
  • airlines: translation between two letter carrier (airline) codes and names
  • planes: construction information about each plane found in flights

Airline On-Time Statistics and Delay Causes

The U.S. Department of Transportation’s (DOT) Bureau of Transportation Statistics (BTS) tracks the on-time performance of domestic flights operated by large air carriers. Summary information on the number of on-time, delayed, canceled and diverted flights appears in DOT’s monthly Air Travel Consumer Report, published about 30 days after the month’s end, as well as in summary tables posted on this website. BTS began collecting details on the causes of flight delays in June 2003. Summary statistics and raw data are made available to the public at the time the Air Travel Consumer Report is released. Access it here

Worldwide flight data

Open flights: As of January 2017, the OpenFlights Airports Database contains over 10,000 airports, train stations and ferry terminals spanning the globe

Download: airports.dat (Airports only, high quality)

Download: airports-extended.dat (Airports, train stations and ferry terminals, including user contributions)

Bureau of Transportation:

Flightera.net seems to have a lot of good data for free. It has in-depth data on flights and doesn’t seem limited by date. I can’t speak on the validity of the data though.

flightradar24.com has lots of data, also historically, they might be willing to help you get it in a nice format.

 

2019 Crime statistics in the USA

Dataset with arrest in US by race and separate states. Download Excel here

Researchers from IBM, MIT and Harvard Announced The Release Of DARPA “Common Sense AI” Dataset Along With Two Machine Learning Models At ICML 2021

Building machines that can make decisions based on common sense is no easy feat. A machine must be able to do more than merely find patterns in data; it also needs a way of interpreting the intentions and beliefs behind people’s choices.

At the 2021 International Conference on Machine Learning (ICML), Researchers from IBM, MIT, and Harvard University have come together to release a DARPA “Common Sense AI” dataset for benchmarking AI intuition. They are also releasing two machine learning models that represent different approaches to the problem that relies on testing techniques psychologists use to study infants’ behavior to accelerate the development of AI exhibiting common sense.

Source – Summary – Paper – IBM Blog

100 million protein structures Dataset by DeepMind

DeepMind creates ‘transformative’ map of human proteins drawn by AI. By the end of the year, DeepMind hopes to release predictions for 100 million protein structures, a dataset that will be “transformative for our understanding of how life works,

Here’s a good article about this topic

Google Dataset Search

Google Dataset Search

Malware traffic dataset

Comprises 1914081 records created from all malware traffic analysis .net PCAP files, from 2013 to 2021. The logs are generated using Suricata and Zeek.

Originator: ali_alwashali

Percent of “foreign-born” population in each US and EU state or country.

For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state 🇺🇸🇪🇺

Author: Here

Percent of “foreign-born” population in each US and EU state or country. For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state.

Examples of “foreign-born” in this context:

  • Person born in Spain and living in France is NOT “foreign-born”

  • Person born in Turkey and living in France is “foreign-born”

  • Person born in Florida and living in Texas is NOT “foreign-born”

  • Person born in Mexico and living in Texas is “foreign-born”

  • Person born in Florida and living in France is “foreign-born”

  • Person born in France and living in Florida is “foreign-born”

🇺🇸🇪🇺🗺️

Note: Poland, Ireland, Germany, Greece, Cyprus, Malta, Portugal uses Eurostat 2010 Migration data and Croatia has no data at all

Link1

Link2

Link3

Tools: MS Office

Source: Here

35% of “entry-level” jobs on LinkedIn require 3+ years of experience

r/dataisbeautiful - [OC] 35% of "entry-level" jobs on LinkedIn require 3+ years of experience

Source: LinkedIn data  (see original post)

Tool: Photoshop from my colleague

Latest complete Netflix movie dataset

Created from 4 APIs. 11K+ rows and 30+ attributes of Netflix (Ratings, earnings, actors, language, availability, movie trailers, and many more)

Dataset on Kaggle.

Explore this dataset using FlixGem.com (this dataset is powering this webapp)

Dataset on Google Sheets.

Common Crawl

A corpus of web crawl data composed of over 50 billion web pages. The Common Crawl corpus contains petabytes of data collected since 2008. It contains raw web page data, extracted metadata and text extractions.

AWS CLI Access (No AWS account required)

aws s3 ls s3://commoncrawl/ --no-sign-request

s3://commoncrawl/crawl-data/CC-MAIN-2021-17 – April 2021

 Dataset on protein prices

Data on Primary Commodity Prices are updated monthly based on the IMF’s Primary Commodity Price System.

Excel Database

 CPOST dataset on suicide attacks over four decades

The University of Chicago Project on Security and Threats presents the updated and expanded Database on Suicide Attacks (DSAT), which now links to Uppsala Conflict Data Program data on armed conflicts and includes a new dataset measuring the alliance and rivalry relationships among militant groups with connections to suicide attack groups. Access it here.

Credit Card Dataset – Survey of Consumer Finances (SCF) Combined Extract Data 1989-2019

You can do a lot of aggregated analysis in a pretty straightforward way there.

Drone imagery with annotations for small object detection and tracking dataset

11 TB dataset of drone imagery with annotations for small object detection and tracking

Download and more information are available here

Dataset License: CDLA-Sharing-1.0

Helper scripts for accessing the dataset: DATASET.md

Dataset Exploration: Colab

NOAA High-Resolution Rapid Refresh (HRRR) Model

The HRRR is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model, initialized by 3km grids with 3km radar assimilation. Radar data is assimilated in the HRRR every 15 min over a 1-h period adding further detail to that provided by the hourly data assimilation from the 13km radar-enhanced Rapid Refresh.

Registry of Open Data on AWS

This registry exists to help people discover and share datasets that are available via AWS resources. Learn more about sharing data on AWS.

See all usage examples for datasets listed in this registry.

See datasets from Digital Earth AfricaFacebook Data for GoodNASA Space Act AgreementNIH STRIDESNOAA Big Data ProgramSpace Telescope Science Institute, and Amazon Sustainability Data Initiative.

Textbook Question Answering (TQA)

1,076 textbook lessons, 26,260 questions, 6229 images

Documentation: allenai.org/data/tqa

Download

Harmonized Cancer Datasets: Genomic Data Commons Data Portal

The GDC Data Portal is a robust data-driven platform that allows cancer
researchers and bioinformaticians to search and download cancer data for analysis.

Genomic Data Commons Data Portal
Genomic Data Commons Data Portal

The Cancer Genome Atlas

The Cancer Genome Atlas (TCGA), a collaboration between the National Cancer Institute (NCI) and National Human Genome Research Institute (NHGRI), aims to generate comprehensive, multi-dimensional maps of the key genomic changes in major types and subtypes of cancer.

AWS CLI Access (No AWS account required)

aws s3 ls s3://tcga-2-open/ --no-sign-request

Therapeutically Applicable Research to Generate Effective Treatments (TARGET)

The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) program applies a comprehensive genomic approach to determine molecular changes that drive childhood cancers. The goal of the program is to use data to guide the development of effective, less toxic therapies. TARGET is organized into a collaborative network of disease-specific project teams.  TARGET projects provide comprehensive molecular characterization to determine the genetic changes that drive the initiation and progression of childhood cancers. The dataset contains open Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-Seq Isoform Expression Quantification, miRNA-Seq miRNA Expression Quantification data from Genomic Data Commons (GDC), and open data from GDC Legacy Archive. Access it here.

Genome Aggregation Database (gnomAD)

The Genome Aggregation Database (gnomAD) is a resource developed by an international coalition of investigators that aggregates and harmonizes both exome and genome data from a wide range of large-scale human sequencing projects. The summary data provided here are released for the benefit of the wider scientific community without restriction on use. Downloads

SQuAD (Stanford Question Answering Dataset)

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. Access it here.

PubMed Diabetes Dataset

The Pubmed Diabetes dataset consists of 19717 scientific publications from PubMed database pertaining to diabetes classified into one of three classes. The citation network consists of 44338 links. Each publication in the dataset is described by a TF/IDF weighted word vector from a dictionary which consists of 500 unique words. The README file in the dataset provides more details.

Download Link

Drug-Target Interaction Dataset

This dataset contains interactions between drugs and targets collected from DrugBank, KEGG Drug, DCDB, and Matador. It was originally collected by Perlman et al. It contains 315 drugs, 250 targets, 1,306 drug-target interactions, 5 types of drug-drug similarities, and 3 types of target-target similarities. Drug-drug similarities include Chemical-based, Ligand-based, Expression-based, Side-effect-based, and Annotation-based similarities. Target-target similarities include Sequence-based, Protein-protein interaction network-based, and Gene Ontology-based similarities. The original task on the dataset is to predict new interactions between drugs and targets based on different types of similarities in the network. Download link

Pharmacogenomics Datasets

PharmGKB data and knowledge is available as downloads. It is often critical to check with their curators at feedback@pharmgkb.org before embarking on a large project using these data, to be sure that the files and data they make available are being interpreted correctly. PharmGKB generally does NOT need to be a co-author on such analyses; They just want to make sure that there is a correct understanding of our data before lots of resources are spent.

Pancreatic Cancer Organoid Profiling

The dataset contains open RNA-Seq Gene Expression Quantification data and controlled WGS/WXS/RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic Mutation, and RNA-Seq Splice Junction Quantification. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://gdc-organoid-pancreatic-phs001611-2-open/ --no-sign-request

Africa Soil Information Service (AfSIS) Soil Chemistry

This dataset contains soil infrared spectral data and paired soil property reference measurements for georeferenced soil samples that were collected through the Africa Soil Information Service (AfSIS) project, which lasted from 2009 through 2018. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://afsis/ --no-sign-request

Dataset for Affective States in E-Environments

DAiSEE is the first multi-label video classification dataset comprising of 9068 video snippets captured from 112 users for recognizing the user affective states of boredom, confusion, engagement, and frustration “in the wild”. The dataset has four levels of labels namely – very low, low, high, and very high for each of the affective states, which are crowd annotated and correlated with a gold standard annotation created using a team of expert psychologists. Download it here.

NatureServe Explorer Dataset

NatureServe Explorer provides conservation status, taxonomy, distribution, and life history information for more than 95,000 plants and animals in the United States and Canada, and more than 10,000 vegetation communities and ecological systems in the Western Hemisphere.

The data available through NatureServe Explorer represents data managed in the NatureServe Central Databases. These databases are dynamic, being continually enhanced and refined through the input of hundreds of natural heritage program scientists and other collaborators. NatureServe Explorer is updated from these central databases to reflect information from new field surveys, the latest taxonomic treatments and other scientific publications, and new conservation status assessments. Explore Data here

Flight Records in the US

Airline On-Time Performance and Causes of Flight Delays – On_Time Data.

This database contains scheduled and actual departure and arrival times, reason of delay. reported by certified U.S. air carriers that account for at least one percent of domestic scheduled passenger revenues. The data is collected by the Office of Airline Information, Bureau of Transportation Statistics (BTS).

FlightAware.com has data but you need to pay for a full dataset.

The anyflights package supplies a set of functions to generate air travel data (and data packages!) similar to nycflights13. With a user-defined year and airport, the anyflights function will grab data on:

  • flights: all flights that departed a given airport in a given year and month
  • weather: hourly meterological data for a given airport in a given year and month
  • airports: airport names, FAA codes, and locations
  • airlines: translation between two letter carrier (airline) codes and names
  • planes: construction information about each plane found in flights

Airline On-Time Statistics and Delay Causes

The U.S. Department of Transportation’s (DOT) Bureau of Transportation Statistics (BTS) tracks the on-time performance of domestic flights operated by large air carriers. Summary information on the number of on-time, delayed, canceled and diverted flights appears in DOT’s monthly Air Travel Consumer Report, published about 30 days after the month’s end, as well as in summary tables posted on this website. BTS began collecting details on the causes of flight delays in June 2003. Summary statistics and raw data are made available to the public at the time the Air Travel Consumer Report is released. Access it here

Worldwide flight data

Open flights: As of January 2017, the OpenFlights Airports Database contains over 10,000 airports, train stations and ferry terminals spanning the globe

Download: airports.dat (Airports only, high quality)

Download: airports-extended.dat (Airports, train stations and ferry terminals, including user contributions)

Bureau of Transportation:

Flightera.net seems to have a lot of good data for free. It has in-depth data on flights and doesn’t seem limited by date. I can’t speak on the validity of the data though.

flightradar24.com has lots of data, also historically, they might be willing to help you get it in a nice format.

 

2019 Crime statistics in the USA

Dataset with arrest in US by race and separate states. Download Excel here

  • Big Data
  • Public Datasets
  • Hadoop – Framework for distributed storage and processing of very large data sets.
  • Data Engineering
  • Streaming
  • Apache Spark – Unified engine for large-scale data processing.
  • Qlik – Business intelligence platform for data visualization, analytics, and reporting apps.
  • Splunk – Platform for searching, monitoring, and analyzing structured and unstructured machine-generated big data in real-time.

Researchers from IBM, MIT and Harvard Announced The Release Of DARPA “Common Sense AI” Dataset Along With Two Machine Learning Models At ICML 2021

Building machines that can make decisions based on common sense is no easy feat. A machine must be able to do more than merely find patterns in data; it also needs a way of interpreting the intentions and beliefs behind people’s choices.

At the 2021 International Conference on Machine Learning (ICML), Researchers from IBM, MIT, and Harvard University have come together to release a DARPA “Common Sense AI” dataset for benchmarking AI intuition. They are also releasing two machine learning models that represent different approaches to the problem that relies on testing techniques psychologists use to study infants’ behavior to accelerate the development of AI exhibiting common sense.

Source – Summary – Paper – IBM Blog

100 million protein structures Dataset by DeepMind

DeepMind creates ‘transformative’ map of human proteins drawn by AI. By the end of the year, DeepMind hopes to release predictions for 100 million protein structures, a dataset that will be “transformative for our understanding of how life works,

Here’s a good article about this topic

Google Dataset Search

Google Dataset Search

Malware traffic dataset

Comprises 1914081 records created from all malware traffic analysis .net PCAP files, from 2013 to 2021. The logs are generated using Suricata and Zeek.

Originator: ali_alwashali

Percent of “foreign-born” population in each US and EU state or country.

For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state 🇺🇸🇪🇺

Author: Here

Percent of “foreign-born” population in each US and EU state or country. For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state.

Examples of “foreign-born” in this context:

  • Person born in Spain and living in France is NOT “foreign-born”

  • Person born in Turkey and living in France is “foreign-born”

  • Person born in Florida and living in Texas is NOT “foreign-born”

  • Person born in Mexico and living in Texas is “foreign-born”

  • Person born in Florida and living in France is “foreign-born”

  • Person born in France and living in Florida is “foreign-born”

🇺🇸🇪🇺🗺️

Note: Poland, Ireland, Germany, Greece, Cyprus, Malta, Portugal uses Eurostat 2010 Migration data and Croatia has no data at all

Link1

Link2

Link3

Tools: MS Office

Source: Here

35% of “entry-level” jobs on LinkedIn require 3+ years of experience

r/dataisbeautiful - [OC] 35% of "entry-level" jobs on LinkedIn require 3+ years of experience

Source: LinkedIn data  (see original post)

Tool: Photoshop from my colleague

Latest complete Netflix movie dataset

Created from 4 APIs. 11K+ rows and 30+ attributes of Netflix (Ratings, earnings, actors, language, availability, movie trailers, and many more)

Dataset on Kaggle.

Explore this dataset using FlixGem.com (this dataset is powering this webapp)

Dataset on Google Sheets.

Common Crawl

A corpus of web crawl data composed of over 50 billion web pages. The Common Crawl corpus contains petabytes of data collected since 2008. It contains raw web page data, extracted metadata and text extractions.

AWS CLI Access (No AWS account required)

aws s3 ls s3://commoncrawl/ --no-sign-request

s3://commoncrawl/crawl-data/CC-MAIN-2021-17 – April 2021

 Dataset on protein prices

Data on Primary Commodity Prices are updated monthly based on the IMF’s Primary Commodity Price System.

Excel Database

 CPOST dataset on suicide attacks over four decades

The University of Chicago Project on Security and Threats presents the updated and expanded Database on Suicide Attacks (DSAT), which now links to Uppsala Conflict Data Program data on armed conflicts and includes a new dataset measuring the alliance and rivalry relationships among militant groups with connections to suicide attack groups. Access it here.

Credit Card Dataset – Survey of Consumer Finances (SCF) Combined Extract Data 1989-2019

You can do a lot of aggregated analysis in a pretty straightforward way there.

Drone imagery with annotations for small object detection and tracking dataset

11 TB dataset of drone imagery with annotations for small object detection and tracking

Download and more information are available here

Dataset License: CDLA-Sharing-1.0

Helper scripts for accessing the dataset: DATASET.md

Dataset Exploration: Colab

NOAA High-Resolution Rapid Refresh (HRRR) Model

The HRRR is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model, initialized by 3km grids with 3km radar assimilation. Radar data is assimilated in the HRRR every 15 min over a 1-h period adding further detail to that provided by the hourly data assimilation from the 13km radar-enhanced Rapid Refresh.

Registry of Open Data on AWS

This registry exists to help people discover and share datasets that are available via AWS resources. Learn more about sharing data on AWS.

See all usage examples for datasets listed in this registry.

See datasets from Digital Earth AfricaFacebook Data for GoodNASA Space Act AgreementNIH STRIDESNOAA Big Data ProgramSpace Telescope Science Institute, and Amazon Sustainability Data Initiative.

Textbook Question Answering (TQA)

1,076 textbook lessons, 26,260 questions, 6229 images

Documentation: allenai.org/data/tqa

Download

Harmonized Cancer Datasets: Genomic Data Commons Data Portal

The GDC Data Portal is a robust data-driven platform that allows cancer
researchers and bioinformaticians to search and download cancer data for analysis.

Genomic Data Commons Data Portal
Genomic Data Commons Data Portal

The Cancer Genome Atlas

The Cancer Genome Atlas (TCGA), a collaboration between the National Cancer Institute (NCI) and National Human Genome Research Institute (NHGRI), aims to generate comprehensive, multi-dimensional maps of the key genomic changes in major types and subtypes of cancer.

AWS CLI Access (No AWS account required)

aws s3 ls s3://tcga-2-open/ --no-sign-request

Therapeutically Applicable Research to Generate Effective Treatments (TARGET)

The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) program applies a comprehensive genomic approach to determine molecular changes that drive childhood cancers. The goal of the program is to use data to guide the development of effective, less toxic therapies. TARGET is organized into a collaborative network of disease-specific project teams.  TARGET projects provide comprehensive molecular characterization to determine the genetic changes that drive the initiation and progression of childhood cancers. The dataset contains open Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-Seq Isoform Expression Quantification, miRNA-Seq miRNA Expression Quantification data from Genomic Data Commons (GDC), and open data from GDC Legacy Archive. Access it here.

Genome Aggregation Database (gnomAD)

The Genome Aggregation Database (gnomAD) is a resource developed by an international coalition of investigators that aggregates and harmonizes both exome and genome data from a wide range of large-scale human sequencing projects. The summary data provided here are released for the benefit of the wider scientific community without restriction on use. Downloads

SQuAD (Stanford Question Answering Dataset)

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. Access it here.

PubMed Diabetes Dataset

The Pubmed Diabetes dataset consists of 19717 scientific publications from PubMed database pertaining to diabetes classified into one of three classes. The citation network consists of 44338 links. Each publication in the dataset is described by a TF/IDF weighted word vector from a dictionary which consists of 500 unique words. The README file in the dataset provides more details.

Download Link

Drug-Target Interaction Dataset

This dataset contains interactions between drugs and targets collected from DrugBank, KEGG Drug, DCDB, and Matador. It was originally collected by Perlman et al. It contains 315 drugs, 250 targets, 1,306 drug-target interactions, 5 types of drug-drug similarities, and 3 types of target-target similarities. Drug-drug similarities include Chemical-based, Ligand-based, Expression-based, Side-effect-based, and Annotation-based similarities. Target-target similarities include Sequence-based, Protein-protein interaction network-based, and Gene Ontology-based similarities. The original task on the dataset is to predict new interactions between drugs and targets based on different types of similarities in the network. Download link

Pharmacogenomics Datasets

PharmGKB data and knowledge is available as downloads. It is often critical to check with their curators at feedback@pharmgkb.org before embarking on a large project using these data, to be sure that the files and data they make available are being interpreted correctly. PharmGKB generally does NOT need to be a co-author on such analyses; They just want to make sure that there is a correct understanding of our data before lots of resources are spent.

Pancreatic Cancer Organoid Profiling

The dataset contains open RNA-Seq Gene Expression Quantification data and controlled WGS/WXS/RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic Mutation, and RNA-Seq Splice Junction Quantification. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://gdc-organoid-pancreatic-phs001611-2-open/ --no-sign-request

Africa Soil Information Service (AfSIS) Soil Chemistry

This dataset contains soil infrared spectral data and paired soil property reference measurements for georeferenced soil samples that were collected through the Africa Soil Information Service (AfSIS) project, which lasted from 2009 through 2018. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://afsis/ --no-sign-request

Dataset for Affective States in E-Environments

DAiSEE is the first multi-label video classification dataset comprising of 9068 video snippets captured from 112 users for recognizing the user affective states of boredom, confusion, engagement, and frustration “in the wild”. The dataset has four levels of labels namely – very low, low, high, and very high for each of the affective states, which are crowd annotated and correlated with a gold standard annotation created using a team of expert psychologists. Download it here.

NatureServe Explorer Dataset

NatureServe Explorer provides conservation status, taxonomy, distribution, and life history information for more than 95,000 plants and animals in the United States and Canada, and more than 10,000 vegetation communities and ecological systems in the Western Hemisphere.

The data available through NatureServe Explorer represents data managed in the NatureServe Central Databases. These databases are dynamic, being continually enhanced and refined through the input of hundreds of natural heritage program scientists and other collaborators. NatureServe Explorer is updated from these central databases to reflect information from new field surveys, the latest taxonomic treatments and other scientific publications, and new conservation status assessments. Explore Data here

Flight Records in the US

Airline On-Time Performance and Causes of Flight Delays – On_Time Data.

This database contains scheduled and actual departure and arrival times, reason of delay. reported by certified U.S. air carriers that account for at least one percent of domestic scheduled passenger revenues. The data is collected by the Office of Airline Information, Bureau of Transportation Statistics (BTS).

FlightAware.com has data but you need to pay for a full dataset.

The anyflights package supplies a set of functions to generate air travel data (and data packages!) similar to nycflights13. With a user-defined year and airport, the anyflights function will grab data on:

  • flights: all flights that departed a given airport in a given year and month
  • weather: hourly meterological data for a given airport in a given year and month
  • airports: airport names, FAA codes, and locations
  • airlines: translation between two letter carrier (airline) codes and names
  • planes: construction information about each plane found in flights

Airline On-Time Statistics and Delay Causes

The U.S. Department of Transportation’s (DOT) Bureau of Transportation Statistics (BTS) tracks the on-time performance of domestic flights operated by large air carriers. Summary information on the number of on-time, delayed, canceled and diverted flights appears in DOT’s monthly Air Travel Consumer Report, published about 30 days after the month’s end, as well as in summary tables posted on this website. BTS began collecting details on the causes of flight delays in June 2003. Summary statistics and raw data are made available to the public at the time the Air Travel Consumer Report is released. Access it here

Worldwide flight data

Open flights: As of January 2017, the OpenFlights Airports Database contains over 10,000 airports, train stations and ferry terminals spanning the globe

Download: airports.dat (Airports only, high quality)

Download: airports-extended.dat (Airports, train stations and ferry terminals, including user contributions)

Bureau of Transportation:

Flightera.net seems to have a lot of good data for free. It has in-depth data on flights and doesn’t seem limited by date. I can’t speak on the validity of the data though.

flightradar24.com has lots of data, also historically, they might be willing to help you get it in a nice format.

 

2019 Crime statistics in the USA

Dataset with arrest in US by race and separate states. Download Excel here

Researchers from IBM, MIT and Harvard Announced The Release Of DARPA “Common Sense AI” Dataset Along With Two Machine Learning Models At ICML 2021

Building machines that can make decisions based on common sense is no easy feat. A machine must be able to do more than merely find patterns in data; it also needs a way of interpreting the intentions and beliefs behind people’s choices.

At the 2021 International Conference on Machine Learning (ICML), Researchers from IBM, MIT, and Harvard University have come together to release a DARPA “Common Sense AI” dataset for benchmarking AI intuition. They are also releasing two machine learning models that represent different approaches to the problem that relies on testing techniques psychologists use to study infants’ behavior to accelerate the development of AI exhibiting common sense.

Source – Summary – Paper – IBM Blog

100 million protein structures Dataset by DeepMind

DeepMind creates ‘transformative’ map of human proteins drawn by AI. By the end of the year, DeepMind hopes to release predictions for 100 million protein structures, a dataset that will be “transformative for our understanding of how life works,

Here’s a good article about this topic

Google Dataset Search

Google Dataset Search

Malware traffic dataset

Comprises 1914081 records created from all malware traffic analysis .net PCAP files, from 2013 to 2021. The logs are generated using Suricata and Zeek.

Originator: ali_alwashali

Percent of “foreign-born” population in each US and EU state or country.

For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state 🇺🇸🇪🇺

Author: Here

Percent of “foreign-born” population in each US and EU state or country. For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state.

Examples of “foreign-born” in this context:

  • Person born in Spain and living in France is NOT “foreign-born”

  • Person born in Turkey and living in France is “foreign-born”

  • Person born in Florida and living in Texas is NOT “foreign-born”

  • Person born in Mexico and living in Texas is “foreign-born”

  • Person born in Florida and living in France is “foreign-born”

  • Person born in France and living in Florida is “foreign-born”

🇺🇸🇪🇺🗺️

Note: Poland, Ireland, Germany, Greece, Cyprus, Malta, Portugal uses Eurostat 2010 Migration data and Croatia has no data at all

Link1

Link2

Link3

Tools: MS Office

Source: Here

35% of “entry-level” jobs on LinkedIn require 3+ years of experience

r/dataisbeautiful - [OC] 35% of "entry-level" jobs on LinkedIn require 3+ years of experience

Source: LinkedIn data  (see original post)

Tool: Photoshop from my colleague

Latest complete Netflix movie dataset

Created from 4 APIs. 11K+ rows and 30+ attributes of Netflix (Ratings, earnings, actors, language, availability, movie trailers, and many more)

Dataset on Kaggle.

Explore this dataset using FlixGem.com (this dataset is powering this webapp)

Dataset on Google Sheets.

Common Crawl

A corpus of web crawl data composed of over 50 billion web pages. The Common Crawl corpus contains petabytes of data collected since 2008. It contains raw web page data, extracted metadata and text extractions.

AWS CLI Access (No AWS account required)

aws s3 ls s3://commoncrawl/ --no-sign-request

s3://commoncrawl/crawl-data/CC-MAIN-2021-17 – April 2021

 Dataset on protein prices

Data on Primary Commodity Prices are updated monthly based on the IMF’s Primary Commodity Price System.

Excel Database

 CPOST dataset on suicide attacks over four decades

The University of Chicago Project on Security and Threats presents the updated and expanded Database on Suicide Attacks (DSAT), which now links to Uppsala Conflict Data Program data on armed conflicts and includes a new dataset measuring the alliance and rivalry relationships among militant groups with connections to suicide attack groups. Access it here.

Credit Card Dataset – Survey of Consumer Finances (SCF) Combined Extract Data 1989-2019

You can do a lot of aggregated analysis in a pretty straightforward way there.

Drone imagery with annotations for small object detection and tracking dataset

11 TB dataset of drone imagery with annotations for small object detection and tracking

Download and more information are available here

Dataset License: CDLA-Sharing-1.0

Helper scripts for accessing the dataset: DATASET.md

Dataset Exploration: Colab

NOAA High-Resolution Rapid Refresh (HRRR) Model

The HRRR is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model, initialized by 3km grids with 3km radar assimilation. Radar data is assimilated in the HRRR every 15 min over a 1-h period adding further detail to that provided by the hourly data assimilation from the 13km radar-enhanced Rapid Refresh.

Registry of Open Data on AWS

This registry exists to help people discover and share datasets that are available via AWS resources. Learn more about sharing data on AWS.

See all usage examples for datasets listed in this registry.

See datasets from Digital Earth AfricaFacebook Data for GoodNASA Space Act AgreementNIH STRIDESNOAA Big Data ProgramSpace Telescope Science Institute, and Amazon Sustainability Data Initiative.

Textbook Question Answering (TQA)

1,076 textbook lessons, 26,260 questions, 6229 images

Documentation: allenai.org/data/tqa

Download

Harmonized Cancer Datasets: Genomic Data Commons Data Portal

The GDC Data Portal is a robust data-driven platform that allows cancer
researchers and bioinformaticians to search and download cancer data for analysis.

Genomic Data Commons Data Portal
Genomic Data Commons Data Portal

The Cancer Genome Atlas

The Cancer Genome Atlas (TCGA), a collaboration between the National Cancer Institute (NCI) and National Human Genome Research Institute (NHGRI), aims to generate comprehensive, multi-dimensional maps of the key genomic changes in major types and subtypes of cancer.

AWS CLI Access (No AWS account required)

aws s3 ls s3://tcga-2-open/ --no-sign-request

Therapeutically Applicable Research to Generate Effective Treatments (TARGET)

The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) program applies a comprehensive genomic approach to determine molecular changes that drive childhood cancers. The goal of the program is to use data to guide the development of effective, less toxic therapies. TARGET is organized into a collaborative network of disease-specific project teams.  TARGET projects provide comprehensive molecular characterization to determine the genetic changes that drive the initiation and progression of childhood cancers. The dataset contains open Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-Seq Isoform Expression Quantification, miRNA-Seq miRNA Expression Quantification data from Genomic Data Commons (GDC), and open data from GDC Legacy Archive. Access it here.

Genome Aggregation Database (gnomAD)

The Genome Aggregation Database (gnomAD) is a resource developed by an international coalition of investigators that aggregates and harmonizes both exome and genome data from a wide range of large-scale human sequencing projects. The summary data provided here are released for the benefit of the wider scientific community without restriction on use. Downloads

SQuAD (Stanford Question Answering Dataset)

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. Access it here.

PubMed Diabetes Dataset

The Pubmed Diabetes dataset consists of 19717 scientific publications from PubMed database pertaining to diabetes classified into one of three classes. The citation network consists of 44338 links. Each publication in the dataset is described by a TF/IDF weighted word vector from a dictionary which consists of 500 unique words. The README file in the dataset provides more details.

Download Link

Drug-Target Interaction Dataset

This dataset contains interactions between drugs and targets collected from DrugBank, KEGG Drug, DCDB, and Matador. It was originally collected by Perlman et al. It contains 315 drugs, 250 targets, 1,306 drug-target interactions, 5 types of drug-drug similarities, and 3 types of target-target similarities. Drug-drug similarities include Chemical-based, Ligand-based, Expression-based, Side-effect-based, and Annotation-based similarities. Target-target similarities include Sequence-based, Protein-protein interaction network-based, and Gene Ontology-based similarities. The original task on the dataset is to predict new interactions between drugs and targets based on different types of similarities in the network. Download link

Pharmacogenomics Datasets

PharmGKB data and knowledge is available as downloads. It is often critical to check with their curators at feedback@pharmgkb.org before embarking on a large project using these data, to be sure that the files and data they make available are being interpreted correctly. PharmGKB generally does NOT need to be a co-author on such analyses; They just want to make sure that there is a correct understanding of our data before lots of resources are spent.

Pancreatic Cancer Organoid Profiling

The dataset contains open RNA-Seq Gene Expression Quantification data and controlled WGS/WXS/RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic Mutation, and RNA-Seq Splice Junction Quantification. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://gdc-organoid-pancreatic-phs001611-2-open/ --no-sign-request

Africa Soil Information Service (AfSIS) Soil Chemistry

This dataset contains soil infrared spectral data and paired soil property reference measurements for georeferenced soil samples that were collected through the Africa Soil Information Service (AfSIS) project, which lasted from 2009 through 2018. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://afsis/ --no-sign-request

Dataset for Affective States in E-Environments

DAiSEE is the first multi-label video classification dataset comprising of 9068 video snippets captured from 112 users for recognizing the user affective states of boredom, confusion, engagement, and frustration “in the wild”. The dataset has four levels of labels namely – very low, low, high, and very high for each of the affective states, which are crowd annotated and correlated with a gold standard annotation created using a team of expert psychologists. Download it here.

NatureServe Explorer Dataset

NatureServe Explorer provides conservation status, taxonomy, distribution, and life history information for more than 95,000 plants and animals in the United States and Canada, and more than 10,000 vegetation communities and ecological systems in the Western Hemisphere.

The data available through NatureServe Explorer represents data managed in the NatureServe Central Databases. These databases are dynamic, being continually enhanced and refined through the input of hundreds of natural heritage program scientists and other collaborators. NatureServe Explorer is updated from these central databases to reflect information from new field surveys, the latest taxonomic treatments and other scientific publications, and new conservation status assessments. Explore Data here

Flight Records in the US

Airline On-Time Performance and Causes of Flight Delays – On_Time Data.

This database contains scheduled and actual departure and arrival times, reason of delay. reported by certified U.S. air carriers that account for at least one percent of domestic scheduled passenger revenues. The data is collected by the Office of Airline Information, Bureau of Transportation Statistics (BTS).

FlightAware.com has data but you need to pay for a full dataset.

The anyflights package supplies a set of functions to generate air travel data (and data packages!) similar to nycflights13. With a user-defined year and airport, the anyflights function will grab data on:

  • flights: all flights that departed a given airport in a given year and month
  • weather: hourly meterological data for a given airport in a given year and month
  • airports: airport names, FAA codes, and locations
  • airlines: translation between two letter carrier (airline) codes and names
  • planes: construction information about each plane found in flights

Airline On-Time Statistics and Delay Causes

The U.S. Department of Transportation’s (DOT) Bureau of Transportation Statistics (BTS) tracks the on-time performance of domestic flights operated by large air carriers. Summary information on the number of on-time, delayed, canceled and diverted flights appears in DOT’s monthly Air Travel Consumer Report, published about 30 days after the month’s end, as well as in summary tables posted on this website. BTS began collecting details on the causes of flight delays in June 2003. Summary statistics and raw data are made available to the public at the time the Air Travel Consumer Report is released. Access it here

Worldwide flight data

Open flights: As of January 2017, the OpenFlights Airports Database contains over 10,000 airports, train stations and ferry terminals spanning the globe

Download: airports.dat (Airports only, high quality)

Download: airports-extended.dat (Airports, train stations and ferry terminals, including user contributions)

Bureau of Transportation:

Flightera.net seems to have a lot of good data for free. It has in-depth data on flights and doesn’t seem limited by date. I can’t speak on the validity of the data though.

flightradar24.com has lots of data, also historically, they might be willing to help you get it in a nice format.

 

2019 Crime statistics in the USA

Dataset with arrest in US by race and separate states. Download Excel here

Researchers from IBM, MIT and Harvard Announced The Release Of DARPA “Common Sense AI” Dataset Along With Two Machine Learning Models At ICML 2021

Building machines that can make decisions based on common sense is no easy feat. A machine must be able to do more than merely find patterns in data; it also needs a way of interpreting the intentions and beliefs behind people’s choices.

At the 2021 International Conference on Machine Learning (ICML), Researchers from IBM, MIT, and Harvard University have come together to release a DARPA “Common Sense AI” dataset for benchmarking AI intuition. They are also releasing two machine learning models that represent different approaches to the problem that relies on testing techniques psychologists use to study infants’ behavior to accelerate the development of AI exhibiting common sense.

Source – Summary – Paper – IBM Blog

100 million protein structures Dataset by DeepMind

DeepMind creates ‘transformative’ map of human proteins drawn by AI. By the end of the year, DeepMind hopes to release predictions for 100 million protein structures, a dataset that will be “transformative for our understanding of how life works,

Here’s a good article about this topic

Google Dataset Search

Google Dataset Search

Malware traffic dataset

Comprises 1914081 records created from all malware traffic analysis .net PCAP files, from 2013 to 2021. The logs are generated using Suricata and Zeek.

Originator: ali_alwashali

Percent of “foreign-born” population in each US and EU state or country.

For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state 🇺🇸🇪🇺

Author: Here

Percent of “foreign-born” population in each US and EU state or country. For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state.

Examples of “foreign-born” in this context:

  • Person born in Spain and living in France is NOT “foreign-born”

  • Person born in Turkey and living in France is “foreign-born”

  • Person born in Florida and living in Texas is NOT “foreign-born”

  • Person born in Mexico and living in Texas is “foreign-born”

  • Person born in Florida and living in France is “foreign-born”

  • Person born in France and living in Florida is “foreign-born”

🇺🇸🇪🇺🗺️

Note: Poland, Ireland, Germany, Greece, Cyprus, Malta, Portugal uses Eurostat 2010 Migration data and Croatia has no data at all

Link1

Link2

Link3

Tools: MS Office

Source: Here

35% of “entry-level” jobs on LinkedIn require 3+ years of experience

r/dataisbeautiful - [OC] 35% of "entry-level" jobs on LinkedIn require 3+ years of experience

Source: LinkedIn data  (see original post)

Tool: Photoshop from my colleague

Latest complete Netflix movie dataset

Created from 4 APIs. 11K+ rows and 30+ attributes of Netflix (Ratings, earnings, actors, language, availability, movie trailers, and many more)

Dataset on Kaggle.

Explore this dataset using FlixGem.com (this dataset is powering this webapp)

Dataset on Google Sheets.

Common Crawl

A corpus of web crawl data composed of over 50 billion web pages. The Common Crawl corpus contains petabytes of data collected since 2008. It contains raw web page data, extracted metadata and text extractions.

AWS CLI Access (No AWS account required)

aws s3 ls s3://commoncrawl/ --no-sign-request

s3://commoncrawl/crawl-data/CC-MAIN-2021-17 – April 2021

 Dataset on protein prices

Data on Primary Commodity Prices are updated monthly based on the IMF’s Primary Commodity Price System.

Excel Database

 CPOST dataset on suicide attacks over four decades

The University of Chicago Project on Security and Threats presents the updated and expanded Database on Suicide Attacks (DSAT), which now links to Uppsala Conflict Data Program data on armed conflicts and includes a new dataset measuring the alliance and rivalry relationships among militant groups with connections to suicide attack groups. Access it here.

Credit Card Dataset – Survey of Consumer Finances (SCF) Combined Extract Data 1989-2019

You can do a lot of aggregated analysis in a pretty straightforward way there.

Drone imagery with annotations for small object detection and tracking dataset

11 TB dataset of drone imagery with annotations for small object detection and tracking

Download and more information are available here

Dataset License: CDLA-Sharing-1.0

Helper scripts for accessing the dataset: DATASET.md

Dataset Exploration: Colab

NOAA High-Resolution Rapid Refresh (HRRR) Model

The HRRR is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model, initialized by 3km grids with 3km radar assimilation. Radar data is assimilated in the HRRR every 15 min over a 1-h period adding further detail to that provided by the hourly data assimilation from the 13km radar-enhanced Rapid Refresh.

Registry of Open Data on AWS

This registry exists to help people discover and share datasets that are available via AWS resources. Learn more about sharing data on AWS.

See all usage examples for datasets listed in this registry.

See datasets from Digital Earth AfricaFacebook Data for GoodNASA Space Act AgreementNIH STRIDESNOAA Big Data ProgramSpace Telescope Science Institute, and Amazon Sustainability Data Initiative.

Textbook Question Answering (TQA)

1,076 textbook lessons, 26,260 questions, 6229 images

Documentation: allenai.org/data/tqa

Download

Harmonized Cancer Datasets: Genomic Data Commons Data Portal

The GDC Data Portal is a robust data-driven platform that allows cancer
researchers and bioinformaticians to search and download cancer data for analysis.

Genomic Data Commons Data Portal
Genomic Data Commons Data Portal

The Cancer Genome Atlas

The Cancer Genome Atlas (TCGA), a collaboration between the National Cancer Institute (NCI) and National Human Genome Research Institute (NHGRI), aims to generate comprehensive, multi-dimensional maps of the key genomic changes in major types and subtypes of cancer.

AWS CLI Access (No AWS account required)

aws s3 ls s3://tcga-2-open/ --no-sign-request

Therapeutically Applicable Research to Generate Effective Treatments (TARGET)

The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) program applies a comprehensive genomic approach to determine molecular changes that drive childhood cancers. The goal of the program is to use data to guide the development of effective, less toxic therapies. TARGET is organized into a collaborative network of disease-specific project teams.  TARGET projects provide comprehensive molecular characterization to determine the genetic changes that drive the initiation and progression of childhood cancers. The dataset contains open Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-Seq Isoform Expression Quantification, miRNA-Seq miRNA Expression Quantification data from Genomic Data Commons (GDC), and open data from GDC Legacy Archive. Access it here.

Genome Aggregation Database (gnomAD)

The Genome Aggregation Database (gnomAD) is a resource developed by an international coalition of investigators that aggregates and harmonizes both exome and genome data from a wide range of large-scale human sequencing projects. The summary data provided here are released for the benefit of the wider scientific community without restriction on use. Downloads

SQuAD (Stanford Question Answering Dataset)

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. Access it here.

PubMed Diabetes Dataset

The Pubmed Diabetes dataset consists of 19717 scientific publications from PubMed database pertaining to diabetes classified into one of three classes. The citation network consists of 44338 links. Each publication in the dataset is described by a TF/IDF weighted word vector from a dictionary which consists of 500 unique words. The README file in the dataset provides more details.

Download Link

Drug-Target Interaction Dataset

This dataset contains interactions between drugs and targets collected from DrugBank, KEGG Drug, DCDB, and Matador. It was originally collected by Perlman et al. It contains 315 drugs, 250 targets, 1,306 drug-target interactions, 5 types of drug-drug similarities, and 3 types of target-target similarities. Drug-drug similarities include Chemical-based, Ligand-based, Expression-based, Side-effect-based, and Annotation-based similarities. Target-target similarities include Sequence-based, Protein-protein interaction network-based, and Gene Ontology-based similarities. The original task on the dataset is to predict new interactions between drugs and targets based on different types of similarities in the network. Download link

Pharmacogenomics Datasets

PharmGKB data and knowledge is available as downloads. It is often critical to check with their curators at feedback@pharmgkb.org before embarking on a large project using these data, to be sure that the files and data they make available are being interpreted correctly. PharmGKB generally does NOT need to be a co-author on such analyses; They just want to make sure that there is a correct understanding of our data before lots of resources are spent.

Pancreatic Cancer Organoid Profiling

The dataset contains open RNA-Seq Gene Expression Quantification data and controlled WGS/WXS/RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic Mutation, and RNA-Seq Splice Junction Quantification. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://gdc-organoid-pancreatic-phs001611-2-open/ --no-sign-request

Africa Soil Information Service (AfSIS) Soil Chemistry

This dataset contains soil infrared spectral data and paired soil property reference measurements for georeferenced soil samples that were collected through the Africa Soil Information Service (AfSIS) project, which lasted from 2009 through 2018. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://afsis/ --no-sign-request

Dataset for Affective States in E-Environments

DAiSEE is the first multi-label video classification dataset comprising of 9068 video snippets captured from 112 users for recognizing the user affective states of boredom, confusion, engagement, and frustration “in the wild”. The dataset has four levels of labels namely – very low, low, high, and very high for each of the affective states, which are crowd annotated and correlated with a gold standard annotation created using a team of expert psychologists. Download it here.

NatureServe Explorer Dataset

NatureServe Explorer provides conservation status, taxonomy, distribution, and life history information for more than 95,000 plants and animals in the United States and Canada, and more than 10,000 vegetation communities and ecological systems in the Western Hemisphere.

The data available through NatureServe Explorer represents data managed in the NatureServe Central Databases. These databases are dynamic, being continually enhanced and refined through the input of hundreds of natural heritage program scientists and other collaborators. NatureServe Explorer is updated from these central databases to reflect information from new field surveys, the latest taxonomic treatments and other scientific publications, and new conservation status assessments. Explore Data here

Flight Records in the US

Airline On-Time Performance and Causes of Flight Delays – On_Time Data.

This database contains scheduled and actual departure and arrival times, reason of delay. reported by certified U.S. air carriers that account for at least one percent of domestic scheduled passenger revenues. The data is collected by the Office of Airline Information, Bureau of Transportation Statistics (BTS).

FlightAware.com has data but you need to pay for a full dataset.

The anyflights package supplies a set of functions to generate air travel data (and data packages!) similar to nycflights13. With a user-defined year and airport, the anyflights function will grab data on:

  • flights: all flights that departed a given airport in a given year and month
  • weather: hourly meterological data for a given airport in a given year and month
  • airports: airport names, FAA codes, and locations
  • airlines: translation between two letter carrier (airline) codes and names
  • planes: construction information about each plane found in flights

Airline On-Time Statistics and Delay Causes

The U.S. Department of Transportation’s (DOT) Bureau of Transportation Statistics (BTS) tracks the on-time performance of domestic flights operated by large air carriers. Summary information on the number of on-time, delayed, canceled and diverted flights appears in DOT’s monthly Air Travel Consumer Report, published about 30 days after the month’s end, as well as in summary tables posted on this website. BTS began collecting details on the causes of flight delays in June 2003. Summary statistics and raw data are made available to the public at the time the Air Travel Consumer Report is released. Access it here

Worldwide flight data

Open flights: As of January 2017, the OpenFlights Airports Database contains over 10,000 airports, train stations and ferry terminals spanning the globe

Download: airports.dat (Airports only, high quality)

Download: airports-extended.dat (Airports, train stations and ferry terminals, including user contributions)

Bureau of Transportation:

Flightera.net seems to have a lot of good data for free. It has in-depth data on flights and doesn’t seem limited by date. I can’t speak on the validity of the data though.

flightradar24.com has lots of data, also historically, they might be willing to help you get it in a nice format.

 

2019 Crime statistics in the USA

Dataset with arrest in US by race and separate states. Download Excel here

Researchers from IBM, MIT and Harvard Announced The Release Of DARPA “Common Sense AI” Dataset Along With Two Machine Learning Models At ICML 2021

Building machines that can make decisions based on common sense is no easy feat. A machine must be able to do more than merely find patterns in data; it also needs a way of interpreting the intentions and beliefs behind people’s choices.

At the 2021 International Conference on Machine Learning (ICML), Researchers from IBM, MIT, and Harvard University have come together to release a DARPA “Common Sense AI” dataset for benchmarking AI intuition. They are also releasing two machine learning models that represent different approaches to the problem that relies on testing techniques psychologists use to study infants’ behavior to accelerate the development of AI exhibiting common sense.

Source – Summary – Paper – IBM Blog

100 million protein structures Dataset by DeepMind

DeepMind creates ‘transformative’ map of human proteins drawn by AI. By the end of the year, DeepMind hopes to release predictions for 100 million protein structures, a dataset that will be “transformative for our understanding of how life works,

Here’s a good article about this topic

Google Dataset Search

Google Dataset Search

Malware traffic dataset

Comprises 1914081 records created from all malware traffic analysis .net PCAP files, from 2013 to 2021. The logs are generated using Suricata and Zeek.

Originator: ali_alwashali

Percent of “foreign-born” population in each US and EU state or country.

For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state 🇺🇸🇪🇺

Author: Here

Percent of “foreign-born” population in each US and EU state or country. For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state.

Examples of “foreign-born” in this context:

  • Person born in Spain and living in France is NOT “foreign-born”

  • Person born in Turkey and living in France is “foreign-born”

  • Person born in Florida and living in Texas is NOT “foreign-born”

  • Person born in Mexico and living in Texas is “foreign-born”

  • Person born in Florida and living in France is “foreign-born”

  • Person born in France and living in Florida is “foreign-born”

🇺🇸🇪🇺🗺️

Note: Poland, Ireland, Germany, Greece, Cyprus, Malta, Portugal uses Eurostat 2010 Migration data and Croatia has no data at all

Link1

Link2

Link3

Tools: MS Office

Source: Here

35% of “entry-level” jobs on LinkedIn require 3+ years of experience

r/dataisbeautiful - [OC] 35% of "entry-level" jobs on LinkedIn require 3+ years of experience

Source: LinkedIn data  (see original post)

Tool: Photoshop from my colleague

Latest complete Netflix movie dataset

Created from 4 APIs. 11K+ rows and 30+ attributes of Netflix (Ratings, earnings, actors, language, availability, movie trailers, and many more)

Dataset on Kaggle.

Explore this dataset using FlixGem.com (this dataset is powering this webapp)

Dataset on Google Sheets.

Common Crawl

A corpus of web crawl data composed of over 50 billion web pages. The Common Crawl corpus contains petabytes of data collected since 2008. It contains raw web page data, extracted metadata and text extractions.

AWS CLI Access (No AWS account required)

aws s3 ls s3://commoncrawl/ --no-sign-request

s3://commoncrawl/crawl-data/CC-MAIN-2021-17 – April 2021

 Dataset on protein prices

Data on Primary Commodity Prices are updated monthly based on the IMF’s Primary Commodity Price System.

Excel Database

 CPOST dataset on suicide attacks over four decades

The University of Chicago Project on Security and Threats presents the updated and expanded Database on Suicide Attacks (DSAT), which now links to Uppsala Conflict Data Program data on armed conflicts and includes a new dataset measuring the alliance and rivalry relationships among militant groups with connections to suicide attack groups. Access it here.

Credit Card Dataset – Survey of Consumer Finances (SCF) Combined Extract Data 1989-2019

You can do a lot of aggregated analysis in a pretty straightforward way there.

Drone imagery with annotations for small object detection and tracking dataset

11 TB dataset of drone imagery with annotations for small object detection and tracking

Download and more information are available here

Dataset License: CDLA-Sharing-1.0

Helper scripts for accessing the dataset: DATASET.md

Dataset Exploration: Colab

NOAA High-Resolution Rapid Refresh (HRRR) Model

The HRRR is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model, initialized by 3km grids with 3km radar assimilation. Radar data is assimilated in the HRRR every 15 min over a 1-h period adding further detail to that provided by the hourly data assimilation from the 13km radar-enhanced Rapid Refresh.

Registry of Open Data on AWS

This registry exists to help people discover and share datasets that are available via AWS resources. Learn more about sharing data on AWS.

See all usage examples for datasets listed in this registry.

See datasets from Digital Earth AfricaFacebook Data for GoodNASA Space Act AgreementNIH STRIDESNOAA Big Data ProgramSpace Telescope Science Institute, and Amazon Sustainability Data Initiative.

Textbook Question Answering (TQA)

1,076 textbook lessons, 26,260 questions, 6229 images

Documentation: allenai.org/data/tqa

Download

Harmonized Cancer Datasets: Genomic Data Commons Data Portal

The GDC Data Portal is a robust data-driven platform that allows cancer
researchers and bioinformaticians to search and download cancer data for analysis.

Genomic Data Commons Data Portal
Genomic Data Commons Data Portal

The Cancer Genome Atlas

The Cancer Genome Atlas (TCGA), a collaboration between the National Cancer Institute (NCI) and National Human Genome Research Institute (NHGRI), aims to generate comprehensive, multi-dimensional maps of the key genomic changes in major types and subtypes of cancer.

AWS CLI Access (No AWS account required)

aws s3 ls s3://tcga-2-open/ --no-sign-request

Therapeutically Applicable Research to Generate Effective Treatments (TARGET)

The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) program applies a comprehensive genomic approach to determine molecular changes that drive childhood cancers. The goal of the program is to use data to guide the development of effective, less toxic therapies. TARGET is organized into a collaborative network of disease-specific project teams.  TARGET projects provide comprehensive molecular characterization to determine the genetic changes that drive the initiation and progression of childhood cancers. The dataset contains open Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-Seq Isoform Expression Quantification, miRNA-Seq miRNA Expression Quantification data from Genomic Data Commons (GDC), and open data from GDC Legacy Archive. Access it here.

Genome Aggregation Database (gnomAD)

The Genome Aggregation Database (gnomAD) is a resource developed by an international coalition of investigators that aggregates and harmonizes both exome and genome data from a wide range of large-scale human sequencing projects. The summary data provided here are released for the benefit of the wider scientific community without restriction on use. Downloads

SQuAD (Stanford Question Answering Dataset)

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. Access it here.

PubMed Diabetes Dataset

The Pubmed Diabetes dataset consists of 19717 scientific publications from PubMed database pertaining to diabetes classified into one of three classes. The citation network consists of 44338 links. Each publication in the dataset is described by a TF/IDF weighted word vector from a dictionary which consists of 500 unique words. The README file in the dataset provides more details.

Download Link

Drug-Target Interaction Dataset

This dataset contains interactions between drugs and targets collected from DrugBank, KEGG Drug, DCDB, and Matador. It was originally collected by Perlman et al. It contains 315 drugs, 250 targets, 1,306 drug-target interactions, 5 types of drug-drug similarities, and 3 types of target-target similarities. Drug-drug similarities include Chemical-based, Ligand-based, Expression-based, Side-effect-based, and Annotation-based similarities. Target-target similarities include Sequence-based, Protein-protein interaction network-based, and Gene Ontology-based similarities. The original task on the dataset is to predict new interactions between drugs and targets based on different types of similarities in the network. Download link

Pharmacogenomics Datasets

PharmGKB data and knowledge is available as downloads. It is often critical to check with their curators at feedback@pharmgkb.org before embarking on a large project using these data, to be sure that the files and data they make available are being interpreted correctly. PharmGKB generally does NOT need to be a co-author on such analyses; They just want to make sure that there is a correct understanding of our data before lots of resources are spent.

Pancreatic Cancer Organoid Profiling

The dataset contains open RNA-Seq Gene Expression Quantification data and controlled WGS/WXS/RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic Mutation, and RNA-Seq Splice Junction Quantification. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://gdc-organoid-pancreatic-phs001611-2-open/ --no-sign-request

Africa Soil Information Service (AfSIS) Soil Chemistry

This dataset contains soil infrared spectral data and paired soil property reference measurements for georeferenced soil samples that were collected through the Africa Soil Information Service (AfSIS) project, which lasted from 2009 through 2018. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://afsis/ --no-sign-request

Dataset for Affective States in E-Environments

DAiSEE is the first multi-label video classification dataset comprising of 9068 video snippets captured from 112 users for recognizing the user affective states of boredom, confusion, engagement, and frustration “in the wild”. The dataset has four levels of labels namely – very low, low, high, and very high for each of the affective states, which are crowd annotated and correlated with a gold standard annotation created using a team of expert psychologists. Download it here.

NatureServe Explorer Dataset

NatureServe Explorer provides conservation status, taxonomy, distribution, and life history information for more than 95,000 plants and animals in the United States and Canada, and more than 10,000 vegetation communities and ecological systems in the Western Hemisphere.

The data available through NatureServe Explorer represents data managed in the NatureServe Central Databases. These databases are dynamic, being continually enhanced and refined through the input of hundreds of natural heritage program scientists and other collaborators. NatureServe Explorer is updated from these central databases to reflect information from new field surveys, the latest taxonomic treatments and other scientific publications, and new conservation status assessments. Explore Data here

Flight Records in the US

Airline On-Time Performance and Causes of Flight Delays – On_Time Data.

This database contains scheduled and actual departure and arrival times, reason of delay. reported by certified U.S. air carriers that account for at least one percent of domestic scheduled passenger revenues. The data is collected by the Office of Airline Information, Bureau of Transportation Statistics (BTS).

FlightAware.com has data but you need to pay for a full dataset.

The anyflights package supplies a set of functions to generate air travel data (and data packages!) similar to nycflights13. With a user-defined year and airport, the anyflights function will grab data on:

  • flights: all flights that departed a given airport in a given year and month
  • weather: hourly meterological data for a given airport in a given year and month
  • airports: airport names, FAA codes, and locations
  • airlines: translation between two letter carrier (airline) codes and names
  • planes: construction information about each plane found in flights

Airline On-Time Statistics and Delay Causes

The U.S. Department of Transportation’s (DOT) Bureau of Transportation Statistics (BTS) tracks the on-time performance of domestic flights operated by large air carriers. Summary information on the number of on-time, delayed, canceled and diverted flights appears in DOT’s monthly Air Travel Consumer Report, published about 30 days after the month’s end, as well as in summary tables posted on this website. BTS began collecting details on the causes of flight delays in June 2003. Summary statistics and raw data are made available to the public at the time the Air Travel Consumer Report is released. Access it here

Worldwide flight data

Open flights: As of January 2017, the OpenFlights Airports Database contains over 10,000 airports, train stations and ferry terminals spanning the globe

Download: airports.dat (Airports only, high quality)

Download: airports-extended.dat (Airports, train stations and ferry terminals, including user contributions)

Bureau of Transportation:

Flightera.net seems to have a lot of good data for free. It has in-depth data on flights and doesn’t seem limited by date. I can’t speak on the validity of the data though.

flightradar24.com has lots of data, also historically, they might be willing to help you get it in a nice format.

 

2019 Crime statistics in the USA

Dataset with arrest in US by race and separate states. Download Excel here

Researchers from IBM, MIT and Harvard Announced The Release Of DARPA “Common Sense AI” Dataset Along With Two Machine Learning Models At ICML 2021

Building machines that can make decisions based on common sense is no easy feat. A machine must be able to do more than merely find patterns in data; it also needs a way of interpreting the intentions and beliefs behind people’s choices.

At the 2021 International Conference on Machine Learning (ICML), Researchers from IBM, MIT, and Harvard University have come together to release a DARPA “Common Sense AI” dataset for benchmarking AI intuition. They are also releasing two machine learning models that represent different approaches to the problem that relies on testing techniques psychologists use to study infants’ behavior to accelerate the development of AI exhibiting common sense.

Source – Summary – Paper – IBM Blog

100 million protein structures Dataset by DeepMind

DeepMind creates ‘transformative’ map of human proteins drawn by AI. By the end of the year, DeepMind hopes to release predictions for 100 million protein structures, a dataset that will be “transformative for our understanding of how life works,

Here’s a good article about this topic

Google Dataset Search

Google Dataset Search

Malware traffic dataset

Comprises 1914081 records created from all malware traffic analysis .net PCAP files, from 2013 to 2021. The logs are generated using Suricata and Zeek.

Originator: ali_alwashali

Percent of “foreign-born” population in each US and EU state or country.

For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state 🇺🇸🇪🇺

Author: Here

Percent of “foreign-born” population in each US and EU state or country. For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state.

Examples of “foreign-born” in this context:

  • Person born in Spain and living in France is NOT “foreign-born”

  • Person born in Turkey and living in France is “foreign-born”

  • Person born in Florida and living in Texas is NOT “foreign-born”

  • Person born in Mexico and living in Texas is “foreign-born”

  • Person born in Florida and living in France is “foreign-born”

  • Person born in France and living in Florida is “foreign-born”

🇺🇸🇪🇺🗺️

Note: Poland, Ireland, Germany, Greece, Cyprus, Malta, Portugal uses Eurostat 2010 Migration data and Croatia has no data at all

Link1

Link2

Link3

Tools: MS Office

Source: Here

35% of “entry-level” jobs on LinkedIn require 3+ years of experience

r/dataisbeautiful - [OC] 35% of "entry-level" jobs on LinkedIn require 3+ years of experience

Source: LinkedIn data  (see original post)

Tool: Photoshop from my colleague

Latest complete Netflix movie dataset

Created from 4 APIs. 11K+ rows and 30+ attributes of Netflix (Ratings, earnings, actors, language, availability, movie trailers, and many more)

Dataset on Kaggle.

Explore this dataset using FlixGem.com (this dataset is powering this webapp)

Dataset on Google Sheets.

Common Crawl

A corpus of web crawl data composed of over 50 billion web pages. The Common Crawl corpus contains petabytes of data collected since 2008. It contains raw web page data, extracted metadata and text extractions.

AWS CLI Access (No AWS account required)

aws s3 ls s3://commoncrawl/ --no-sign-request

s3://commoncrawl/crawl-data/CC-MAIN-2021-17 – April 2021

 Dataset on protein prices

Data on Primary Commodity Prices are updated monthly based on the IMF’s Primary Commodity Price System.

Excel Database

 CPOST dataset on suicide attacks over four decades

The University of Chicago Project on Security and Threats presents the updated and expanded Database on Suicide Attacks (DSAT), which now links to Uppsala Conflict Data Program data on armed conflicts and includes a new dataset measuring the alliance and rivalry relationships among militant groups with connections to suicide attack groups. Access it here.

Credit Card Dataset – Survey of Consumer Finances (SCF) Combined Extract Data 1989-2019

You can do a lot of aggregated analysis in a pretty straightforward way there.

Drone imagery with annotations for small object detection and tracking dataset

11 TB dataset of drone imagery with annotations for small object detection and tracking

Download and more information are available here

Dataset License: CDLA-Sharing-1.0

Helper scripts for accessing the dataset: DATASET.md

Dataset Exploration: Colab

NOAA High-Resolution Rapid Refresh (HRRR) Model

The HRRR is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model, initialized by 3km grids with 3km radar assimilation. Radar data is assimilated in the HRRR every 15 min over a 1-h period adding further detail to that provided by the hourly data assimilation from the 13km radar-enhanced Rapid Refresh.

Registry of Open Data on AWS

This registry exists to help people discover and share datasets that are available via AWS resources. Learn more about sharing data on AWS.

See all usage examples for datasets listed in this registry.

See datasets from Digital Earth AfricaFacebook Data for GoodNASA Space Act AgreementNIH STRIDESNOAA Big Data ProgramSpace Telescope Science Institute, and Amazon Sustainability Data Initiative.

Textbook Question Answering (TQA)

1,076 textbook lessons, 26,260 questions, 6229 images

Documentation: allenai.org/data/tqa

Download

Harmonized Cancer Datasets: Genomic Data Commons Data Portal

The GDC Data Portal is a robust data-driven platform that allows cancer
researchers and bioinformaticians to search and download cancer data for analysis.

Genomic Data Commons Data Portal
Genomic Data Commons Data Portal

The Cancer Genome Atlas

The Cancer Genome Atlas (TCGA), a collaboration between the National Cancer Institute (NCI) and National Human Genome Research Institute (NHGRI), aims to generate comprehensive, multi-dimensional maps of the key genomic changes in major types and subtypes of cancer.

AWS CLI Access (No AWS account required)

aws s3 ls s3://tcga-2-open/ --no-sign-request

Therapeutically Applicable Research to Generate Effective Treatments (TARGET)

The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) program applies a comprehensive genomic approach to determine molecular changes that drive childhood cancers. The goal of the program is to use data to guide the development of effective, less toxic therapies. TARGET is organized into a collaborative network of disease-specific project teams.  TARGET projects provide comprehensive molecular characterization to determine the genetic changes that drive the initiation and progression of childhood cancers. The dataset contains open Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-Seq Isoform Expression Quantification, miRNA-Seq miRNA Expression Quantification data from Genomic Data Commons (GDC), and open data from GDC Legacy Archive. Access it here.

Genome Aggregation Database (gnomAD)

The Genome Aggregation Database (gnomAD) is a resource developed by an international coalition of investigators that aggregates and harmonizes both exome and genome data from a wide range of large-scale human sequencing projects. The summary data provided here are released for the benefit of the wider scientific community without restriction on use. Downloads

SQuAD (Stanford Question Answering Dataset)

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. Access it here.

PubMed Diabetes Dataset

The Pubmed Diabetes dataset consists of 19717 scientific publications from PubMed database pertaining to diabetes classified into one of three classes. The citation network consists of 44338 links. Each publication in the dataset is described by a TF/IDF weighted word vector from a dictionary which consists of 500 unique words. The README file in the dataset provides more details.

Download Link

Drug-Target Interaction Dataset

This dataset contains interactions between drugs and targets collected from DrugBank, KEGG Drug, DCDB, and Matador. It was originally collected by Perlman et al. It contains 315 drugs, 250 targets, 1,306 drug-target interactions, 5 types of drug-drug similarities, and 3 types of target-target similarities. Drug-drug similarities include Chemical-based, Ligand-based, Expression-based, Side-effect-based, and Annotation-based similarities. Target-target similarities include Sequence-based, Protein-protein interaction network-based, and Gene Ontology-based similarities. The original task on the dataset is to predict new interactions between drugs and targets based on different types of similarities in the network. Download link

Pharmacogenomics Datasets

PharmGKB data and knowledge is available as downloads. It is often critical to check with their curators at feedback@pharmgkb.org before embarking on a large project using these data, to be sure that the files and data they make available are being interpreted correctly. PharmGKB generally does NOT need to be a co-author on such analyses; They just want to make sure that there is a correct understanding of our data before lots of resources are spent.

Pancreatic Cancer Organoid Profiling

The dataset contains open RNA-Seq Gene Expression Quantification data and controlled WGS/WXS/RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic Mutation, and RNA-Seq Splice Junction Quantification. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://gdc-organoid-pancreatic-phs001611-2-open/ --no-sign-request

Africa Soil Information Service (AfSIS) Soil Chemistry

This dataset contains soil infrared spectral data and paired soil property reference measurements for georeferenced soil samples that were collected through the Africa Soil Information Service (AfSIS) project, which lasted from 2009 through 2018. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://afsis/ --no-sign-request

Dataset for Affective States in E-Environments

DAiSEE is the first multi-label video classification dataset comprising of 9068 video snippets captured from 112 users for recognizing the user affective states of boredom, confusion, engagement, and frustration “in the wild”. The dataset has four levels of labels namely – very low, low, high, and very high for each of the affective states, which are crowd annotated and correlated with a gold standard annotation created using a team of expert psychologists. Download it here.

NatureServe Explorer Dataset

NatureServe Explorer provides conservation status, taxonomy, distribution, and life history information for more than 95,000 plants and animals in the United States and Canada, and more than 10,000 vegetation communities and ecological systems in the Western Hemisphere.

The data available through NatureServe Explorer represents data managed in the NatureServe Central Databases. These databases are dynamic, being continually enhanced and refined through the input of hundreds of natural heritage program scientists and other collaborators. NatureServe Explorer is updated from these central databases to reflect information from new field surveys, the latest taxonomic treatments and other scientific publications, and new conservation status assessments. Explore Data here

Flight Records in the US

Airline On-Time Performance and Causes of Flight Delays – On_Time Data.

This database contains scheduled and actual departure and arrival times, reason of delay. reported by certified U.S. air carriers that account for at least one percent of domestic scheduled passenger revenues. The data is collected by the Office of Airline Information, Bureau of Transportation Statistics (BTS).

FlightAware.com has data but you need to pay for a full dataset.

The anyflights package supplies a set of functions to generate air travel data (and data packages!) similar to nycflights13. With a user-defined year and airport, the anyflights function will grab data on:

  • flights: all flights that departed a given airport in a given year and month
  • weather: hourly meterological data for a given airport in a given year and month
  • airports: airport names, FAA codes, and locations
  • airlines: translation between two letter carrier (airline) codes and names
  • planes: construction information about each plane found in flights

Airline On-Time Statistics and Delay Causes

The U.S. Department of Transportation’s (DOT) Bureau of Transportation Statistics (BTS) tracks the on-time performance of domestic flights operated by large air carriers. Summary information on the number of on-time, delayed, canceled and diverted flights appears in DOT’s monthly Air Travel Consumer Report, published about 30 days after the month’s end, as well as in summary tables posted on this website. BTS began collecting details on the causes of flight delays in June 2003. Summary statistics and raw data are made available to the public at the time the Air Travel Consumer Report is released. Access it here

Worldwide flight data

Open flights: As of January 2017, the OpenFlights Airports Database contains over 10,000 airports, train stations and ferry terminals spanning the globe

Download: airports.dat (Airports only, high quality)

Download: airports-extended.dat (Airports, train stations and ferry terminals, including user contributions)

Bureau of Transportation:

Flightera.net seems to have a lot of good data for free. It has in-depth data on flights and doesn’t seem limited by date. I can’t speak on the validity of the data though.

flightradar24.com has lots of data, also historically, they might be willing to help you get it in a nice format.

 

2019 Crime statistics in the USA

Dataset with arrest in US by race and separate states. Download Excel here

Researchers from IBM, MIT and Harvard Announced The Release Of DARPA “Common Sense AI” Dataset Along With Two Machine Learning Models At ICML 2021

Building machines that can make decisions based on common sense is no easy feat. A machine must be able to do more than merely find patterns in data; it also needs a way of interpreting the intentions and beliefs behind people’s choices.

At the 2021 International Conference on Machine Learning (ICML), Researchers from IBM, MIT, and Harvard University have come together to release a DARPA “Common Sense AI” dataset for benchmarking AI intuition. They are also releasing two machine learning models that represent different approaches to the problem that relies on testing techniques psychologists use to study infants’ behavior to accelerate the development of AI exhibiting common sense.

Source – Summary – Paper – IBM Blog

100 million protein structures Dataset by DeepMind

DeepMind creates ‘transformative’ map of human proteins drawn by AI. By the end of the year, DeepMind hopes to release predictions for 100 million protein structures, a dataset that will be “transformative for our understanding of how life works,

Here’s a good article about this topic

Google Dataset Search

Google Dataset Search

Malware traffic dataset

Comprises 1914081 records created from all malware traffic analysis .net PCAP files, from 2013 to 2021. The logs are generated using Suricata and Zeek.

Originator: ali_alwashali

Percent of “foreign-born” population in each US and EU state or country.

For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state 🇺🇸🇪🇺

Author: Here

Percent of “foreign-born” population in each US and EU state or country. For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state.

Examples of “foreign-born” in this context:

  • Person born in Spain and living in France is NOT “foreign-born”

  • Person born in Turkey and living in France is “foreign-born”

  • Person born in Florida and living in Texas is NOT “foreign-born”

  • Person born in Mexico and living in Texas is “foreign-born”

  • Person born in Florida and living in France is “foreign-born”

  • Person born in France and living in Florida is “foreign-born”

🇺🇸🇪🇺🗺️

Note: Poland, Ireland, Germany, Greece, Cyprus, Malta, Portugal uses Eurostat 2010 Migration data and Croatia has no data at all

Link1

Link2

Link3

Tools: MS Office

Source: Here

35% of “entry-level” jobs on LinkedIn require 3+ years of experience

r/dataisbeautiful - [OC] 35% of "entry-level" jobs on LinkedIn require 3+ years of experience

Source: LinkedIn data  (see original post)

Tool: Photoshop from my colleague

Latest complete Netflix movie dataset

Created from 4 APIs. 11K+ rows and 30+ attributes of Netflix (Ratings, earnings, actors, language, availability, movie trailers, and many more)

Dataset on Kaggle.

Explore this dataset using FlixGem.com (this dataset is powering this webapp)

Dataset on Google Sheets.

Common Crawl

A corpus of web crawl data composed of over 50 billion web pages. The Common Crawl corpus contains petabytes of data collected since 2008. It contains raw web page data, extracted metadata and text extractions.

AWS CLI Access (No AWS account required)

aws s3 ls s3://commoncrawl/ --no-sign-request

s3://commoncrawl/crawl-data/CC-MAIN-2021-17 – April 2021

 Dataset on protein prices

Data on Primary Commodity Prices are updated monthly based on the IMF’s Primary Commodity Price System.

Excel Database

 CPOST dataset on suicide attacks over four decades

The University of Chicago Project on Security and Threats presents the updated and expanded Database on Suicide Attacks (DSAT), which now links to Uppsala Conflict Data Program data on armed conflicts and includes a new dataset measuring the alliance and rivalry relationships among militant groups with connections to suicide attack groups. Access it here.

Credit Card Dataset – Survey of Consumer Finances (SCF) Combined Extract Data 1989-2019

You can do a lot of aggregated analysis in a pretty straightforward way there.

Drone imagery with annotations for small object detection and tracking dataset

11 TB dataset of drone imagery with annotations for small object detection and tracking

Download and more information are available here

Dataset License: CDLA-Sharing-1.0

Helper scripts for accessing the dataset: DATASET.md

Dataset Exploration: Colab

NOAA High-Resolution Rapid Refresh (HRRR) Model

The HRRR is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model, initialized by 3km grids with 3km radar assimilation. Radar data is assimilated in the HRRR every 15 min over a 1-h period adding further detail to that provided by the hourly data assimilation from the 13km radar-enhanced Rapid Refresh.

Registry of Open Data on AWS

This registry exists to help people discover and share datasets that are available via AWS resources. Learn more about sharing data on AWS.

See all usage examples for datasets listed in this registry.

See datasets from Digital Earth AfricaFacebook Data for GoodNASA Space Act AgreementNIH STRIDESNOAA Big Data ProgramSpace Telescope Science Institute, and Amazon Sustainability Data Initiative.

Textbook Question Answering (TQA)

1,076 textbook lessons, 26,260 questions, 6229 images

Documentation: allenai.org/data/tqa

Download

Harmonized Cancer Datasets: Genomic Data Commons Data Portal

The GDC Data Portal is a robust data-driven platform that allows cancer
researchers and bioinformaticians to search and download cancer data for analysis.

Genomic Data Commons Data Portal
Genomic Data Commons Data Portal

The Cancer Genome Atlas

The Cancer Genome Atlas (TCGA), a collaboration between the National Cancer Institute (NCI) and National Human Genome Research Institute (NHGRI), aims to generate comprehensive, multi-dimensional maps of the key genomic changes in major types and subtypes of cancer.

AWS CLI Access (No AWS account required)

aws s3 ls s3://tcga-2-open/ --no-sign-request

Therapeutically Applicable Research to Generate Effective Treatments (TARGET)

The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) program applies a comprehensive genomic approach to determine molecular changes that drive childhood cancers. The goal of the program is to use data to guide the development of effective, less toxic therapies. TARGET is organized into a collaborative network of disease-specific project teams.  TARGET projects provide comprehensive molecular characterization to determine the genetic changes that drive the initiation and progression of childhood cancers. The dataset contains open Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-Seq Isoform Expression Quantification, miRNA-Seq miRNA Expression Quantification data from Genomic Data Commons (GDC), and open data from GDC Legacy Archive. Access it here.

Genome Aggregation Database (gnomAD)

The Genome Aggregation Database (gnomAD) is a resource developed by an international coalition of investigators that aggregates and harmonizes both exome and genome data from a wide range of large-scale human sequencing projects. The summary data provided here are released for the benefit of the wider scientific community without restriction on use. Downloads

SQuAD (Stanford Question Answering Dataset)

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. Access it here.

PubMed Diabetes Dataset

The Pubmed Diabetes dataset consists of 19717 scientific publications from PubMed database pertaining to diabetes classified into one of three classes. The citation network consists of 44338 links. Each publication in the dataset is described by a TF/IDF weighted word vector from a dictionary which consists of 500 unique words. The README file in the dataset provides more details.

Download Link

Drug-Target Interaction Dataset

This dataset contains interactions between drugs and targets collected from DrugBank, KEGG Drug, DCDB, and Matador. It was originally collected by Perlman et al. It contains 315 drugs, 250 targets, 1,306 drug-target interactions, 5 types of drug-drug similarities, and 3 types of target-target similarities. Drug-drug similarities include Chemical-based, Ligand-based, Expression-based, Side-effect-based, and Annotation-based similarities. Target-target similarities include Sequence-based, Protein-protein interaction network-based, and Gene Ontology-based similarities. The original task on the dataset is to predict new interactions between drugs and targets based on different types of similarities in the network. Download link

Pharmacogenomics Datasets

PharmGKB data and knowledge is available as downloads. It is often critical to check with their curators at feedback@pharmgkb.org before embarking on a large project using these data, to be sure that the files and data they make available are being interpreted correctly. PharmGKB generally does NOT need to be a co-author on such analyses; They just want to make sure that there is a correct understanding of our data before lots of resources are spent.

Pancreatic Cancer Organoid Profiling

The dataset contains open RNA-Seq Gene Expression Quantification data and controlled WGS/WXS/RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic Mutation, and RNA-Seq Splice Junction Quantification. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://gdc-organoid-pancreatic-phs001611-2-open/ --no-sign-request

Africa Soil Information Service (AfSIS) Soil Chemistry

This dataset contains soil infrared spectral data and paired soil property reference measurements for georeferenced soil samples that were collected through the Africa Soil Information Service (AfSIS) project, which lasted from 2009 through 2018. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://afsis/ --no-sign-request

Dataset for Affective States in E-Environments

DAiSEE is the first multi-label video classification dataset comprising of 9068 video snippets captured from 112 users for recognizing the user affective states of boredom, confusion, engagement, and frustration “in the wild”. The dataset has four levels of labels namely – very low, low, high, and very high for each of the affective states, which are crowd annotated and correlated with a gold standard annotation created using a team of expert psychologists. Download it here.

NatureServe Explorer Dataset

NatureServe Explorer provides conservation status, taxonomy, distribution, and life history information for more than 95,000 plants and animals in the United States and Canada, and more than 10,000 vegetation communities and ecological systems in the Western Hemisphere.

The data available through NatureServe Explorer represents data managed in the NatureServe Central Databases. These databases are dynamic, being continually enhanced and refined through the input of hundreds of natural heritage program scientists and other collaborators. NatureServe Explorer is updated from these central databases to reflect information from new field surveys, the latest taxonomic treatments and other scientific publications, and new conservation status assessments. Explore Data here

Flight Records in the US

Airline On-Time Performance and Causes of Flight Delays – On_Time Data.

This database contains scheduled and actual departure and arrival times, reason of delay. reported by certified U.S. air carriers that account for at least one percent of domestic scheduled passenger revenues. The data is collected by the Office of Airline Information, Bureau of Transportation Statistics (BTS).

FlightAware.com has data but you need to pay for a full dataset.

The anyflights package supplies a set of functions to generate air travel data (and data packages!) similar to nycflights13. With a user-defined year and airport, the anyflights function will grab data on:

  • flights: all flights that departed a given airport in a given year and month
  • weather: hourly meterological data for a given airport in a given year and month
  • airports: airport names, FAA codes, and locations
  • airlines: translation between two letter carrier (airline) codes and names
  • planes: construction information about each plane found in flights

Airline On-Time Statistics and Delay Causes

The U.S. Department of Transportation’s (DOT) Bureau of Transportation Statistics (BTS) tracks the on-time performance of domestic flights operated by large air carriers. Summary information on the number of on-time, delayed, canceled and diverted flights appears in DOT’s monthly Air Travel Consumer Report, published about 30 days after the month’s end, as well as in summary tables posted on this website. BTS began collecting details on the causes of flight delays in June 2003. Summary statistics and raw data are made available to the public at the time the Air Travel Consumer Report is released. Access it here

Worldwide flight data

Open flights: As of January 2017, the OpenFlights Airports Database contains over 10,000 airports, train stations and ferry terminals spanning the globe

Download: airports.dat (Airports only, high quality)

Download: airports-extended.dat (Airports, train stations and ferry terminals, including user contributions)

Bureau of Transportation:

Flightera.net seems to have a lot of good data for free. It has in-depth data on flights and doesn’t seem limited by date. I can’t speak on the validity of the data though.

flightradar24.com has lots of data, also historically, they might be willing to help you get it in a nice format.

 

2019 Crime statistics in the USA

Dataset with arrest in US by race and separate states. Download Excel here

Researchers from IBM, MIT and Harvard Announced The Release Of DARPA “Common Sense AI” Dataset Along With Two Machine Learning Models At ICML 2021

Building machines that can make decisions based on common sense is no easy feat. A machine must be able to do more than merely find patterns in data; it also needs a way of interpreting the intentions and beliefs behind people’s choices.

At the 2021 International Conference on Machine Learning (ICML), Researchers from IBM, MIT, and Harvard University have come together to release a DARPA “Common Sense AI” dataset for benchmarking AI intuition. They are also releasing two machine learning models that represent different approaches to the problem that relies on testing techniques psychologists use to study infants’ behavior to accelerate the development of AI exhibiting common sense.

Source – Summary – Paper – IBM Blog

100 million protein structures Dataset by DeepMind

DeepMind creates ‘transformative’ map of human proteins drawn by AI. By the end of the year, DeepMind hopes to release predictions for 100 million protein structures, a dataset that will be “transformative for our understanding of how life works,

Here’s a good article about this topic

Google Dataset Search

Google Dataset Search

Malware traffic dataset

Comprises 1914081 records created from all malware traffic analysis .net PCAP files, from 2013 to 2021. The logs are generated using Suricata and Zeek.

Originator: ali_alwashali

Percent of “foreign-born” population in each US and EU state or country.

For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state 🇺🇸🇪🇺

Author: Here

Percent of “foreign-born” population in each US and EU state or country. For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state.

Examples of “foreign-born” in this context:

  • Person born in Spain and living in France is NOT “foreign-born”

  • Person born in Turkey and living in France is “foreign-born”

  • Person born in Florida and living in Texas is NOT “foreign-born”

  • Person born in Mexico and living in Texas is “foreign-born”

  • Person born in Florida and living in France is “foreign-born”

  • Person born in France and living in Florida is “foreign-born”

🇺🇸🇪🇺🗺️

Note: Poland, Ireland, Germany, Greece, Cyprus, Malta, Portugal uses Eurostat 2010 Migration data and Croatia has no data at all

Link1

Link2

Link3

Tools: MS Office

Source: Here

35% of “entry-level” jobs on LinkedIn require 3+ years of experience

r/dataisbeautiful - [OC] 35% of "entry-level" jobs on LinkedIn require 3+ years of experience

Source: LinkedIn data  (see original post)

Tool: Photoshop from my colleague

Latest complete Netflix movie dataset

Created from 4 APIs. 11K+ rows and 30+ attributes of Netflix (Ratings, earnings, actors, language, availability, movie trailers, and many more)

Dataset on Kaggle.

Explore this dataset using FlixGem.com (this dataset is powering this webapp)

Dataset on Google Sheets.

Common Crawl

A corpus of web crawl data composed of over 50 billion web pages. The Common Crawl corpus contains petabytes of data collected since 2008. It contains raw web page data, extracted metadata and text extractions.

AWS CLI Access (No AWS account required)

aws s3 ls s3://commoncrawl/ --no-sign-request

s3://commoncrawl/crawl-data/CC-MAIN-2021-17 – April 2021

 Dataset on protein prices

Data on Primary Commodity Prices are updated monthly based on the IMF’s Primary Commodity Price System.

Excel Database

 CPOST dataset on suicide attacks over four decades

The University of Chicago Project on Security and Threats presents the updated and expanded Database on Suicide Attacks (DSAT), which now links to Uppsala Conflict Data Program data on armed conflicts and includes a new dataset measuring the alliance and rivalry relationships among militant groups with connections to suicide attack groups. Access it here.

Credit Card Dataset – Survey of Consumer Finances (SCF) Combined Extract Data 1989-2019

You can do a lot of aggregated analysis in a pretty straightforward way there.

Drone imagery with annotations for small object detection and tracking dataset

11 TB dataset of drone imagery with annotations for small object detection and tracking

Download and more information are available here

Dataset License: CDLA-Sharing-1.0

Helper scripts for accessing the dataset: DATASET.md

Dataset Exploration: Colab

NOAA High-Resolution Rapid Refresh (HRRR) Model

The HRRR is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model, initialized by 3km grids with 3km radar assimilation. Radar data is assimilated in the HRRR every 15 min over a 1-h period adding further detail to that provided by the hourly data assimilation from the 13km radar-enhanced Rapid Refresh.

Registry of Open Data on AWS

This registry exists to help people discover and share datasets that are available via AWS resources. Learn more about sharing data on AWS.

See all usage examples for datasets listed in this registry.

See datasets from Digital Earth AfricaFacebook Data for GoodNASA Space Act AgreementNIH STRIDESNOAA Big Data ProgramSpace Telescope Science Institute, and Amazon Sustainability Data Initiative.

Textbook Question Answering (TQA)

1,076 textbook lessons, 26,260 questions, 6229 images

Documentation: allenai.org/data/tqa

Download

Harmonized Cancer Datasets: Genomic Data Commons Data Portal

The GDC Data Portal is a robust data-driven platform that allows cancer
researchers and bioinformaticians to search and download cancer data for analysis.

Genomic Data Commons Data Portal
Genomic Data Commons Data Portal

The Cancer Genome Atlas

The Cancer Genome Atlas (TCGA), a collaboration between the National Cancer Institute (NCI) and National Human Genome Research Institute (NHGRI), aims to generate comprehensive, multi-dimensional maps of the key genomic changes in major types and subtypes of cancer.

AWS CLI Access (No AWS account required)

aws s3 ls s3://tcga-2-open/ --no-sign-request

Therapeutically Applicable Research to Generate Effective Treatments (TARGET)

The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) program applies a comprehensive genomic approach to determine molecular changes that drive childhood cancers. The goal of the program is to use data to guide the development of effective, less toxic therapies. TARGET is organized into a collaborative network of disease-specific project teams.  TARGET projects provide comprehensive molecular characterization to determine the genetic changes that drive the initiation and progression of childhood cancers. The dataset contains open Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-Seq Isoform Expression Quantification, miRNA-Seq miRNA Expression Quantification data from Genomic Data Commons (GDC), and open data from GDC Legacy Archive. Access it here.

Genome Aggregation Database (gnomAD)

The Genome Aggregation Database (gnomAD) is a resource developed by an international coalition of investigators that aggregates and harmonizes both exome and genome data from a wide range of large-scale human sequencing projects. The summary data provided here are released for the benefit of the wider scientific community without restriction on use. Downloads

SQuAD (Stanford Question Answering Dataset)

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. Access it here.

PubMed Diabetes Dataset

The Pubmed Diabetes dataset consists of 19717 scientific publications from PubMed database pertaining to diabetes classified into one of three classes. The citation network consists of 44338 links. Each publication in the dataset is described by a TF/IDF weighted word vector from a dictionary which consists of 500 unique words. The README file in the dataset provides more details.

Download Link

Drug-Target Interaction Dataset

This dataset contains interactions between drugs and targets collected from DrugBank, KEGG Drug, DCDB, and Matador. It was originally collected by Perlman et al. It contains 315 drugs, 250 targets, 1,306 drug-target interactions, 5 types of drug-drug similarities, and 3 types of target-target similarities. Drug-drug similarities include Chemical-based, Ligand-based, Expression-based, Side-effect-based, and Annotation-based similarities. Target-target similarities include Sequence-based, Protein-protein interaction network-based, and Gene Ontology-based similarities. The original task on the dataset is to predict new interactions between drugs and targets based on different types of similarities in the network. Download link

Pharmacogenomics Datasets

PharmGKB data and knowledge is available as downloads. It is often critical to check with their curators at feedback@pharmgkb.org before embarking on a large project using these data, to be sure that the files and data they make available are being interpreted correctly. PharmGKB generally does NOT need to be a co-author on such analyses; They just want to make sure that there is a correct understanding of our data before lots of resources are spent.

Pancreatic Cancer Organoid Profiling

The dataset contains open RNA-Seq Gene Expression Quantification data and controlled WGS/WXS/RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic Mutation, and RNA-Seq Splice Junction Quantification. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://gdc-organoid-pancreatic-phs001611-2-open/ --no-sign-request

Africa Soil Information Service (AfSIS) Soil Chemistry

This dataset contains soil infrared spectral data and paired soil property reference measurements for georeferenced soil samples that were collected through the Africa Soil Information Service (AfSIS) project, which lasted from 2009 through 2018. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://afsis/ --no-sign-request

Dataset for Affective States in E-Environments

DAiSEE is the first multi-label video classification dataset comprising of 9068 video snippets captured from 112 users for recognizing the user affective states of boredom, confusion, engagement, and frustration “in the wild”. The dataset has four levels of labels namely – very low, low, high, and very high for each of the affective states, which are crowd annotated and correlated with a gold standard annotation created using a team of expert psychologists. Download it here.

NatureServe Explorer Dataset

NatureServe Explorer provides conservation status, taxonomy, distribution, and life history information for more than 95,000 plants and animals in the United States and Canada, and more than 10,000 vegetation communities and ecological systems in the Western Hemisphere.

The data available through NatureServe Explorer represents data managed in the NatureServe Central Databases. These databases are dynamic, being continually enhanced and refined through the input of hundreds of natural heritage program scientists and other collaborators. NatureServe Explorer is updated from these central databases to reflect information from new field surveys, the latest taxonomic treatments and other scientific publications, and new conservation status assessments. Explore Data here

Flight Records in the US

Airline On-Time Performance and Causes of Flight Delays – On_Time Data.

This database contains scheduled and actual departure and arrival times, reason of delay. reported by certified U.S. air carriers that account for at least one percent of domestic scheduled passenger revenues. The data is collected by the Office of Airline Information, Bureau of Transportation Statistics (BTS).

FlightAware.com has data but you need to pay for a full dataset.

The anyflights package supplies a set of functions to generate air travel data (and data packages!) similar to nycflights13. With a user-defined year and airport, the anyflights function will grab data on:

  • flights: all flights that departed a given airport in a given year and month
  • weather: hourly meterological data for a given airport in a given year and month
  • airports: airport names, FAA codes, and locations
  • airlines: translation between two letter carrier (airline) codes and names
  • planes: construction information about each plane found in flights

Airline On-Time Statistics and Delay Causes

The U.S. Department of Transportation’s (DOT) Bureau of Transportation Statistics (BTS) tracks the on-time performance of domestic flights operated by large air carriers. Summary information on the number of on-time, delayed, canceled and diverted flights appears in DOT’s monthly Air Travel Consumer Report, published about 30 days after the month’s end, as well as in summary tables posted on this website. BTS began collecting details on the causes of flight delays in June 2003. Summary statistics and raw data are made available to the public at the time the Air Travel Consumer Report is released. Access it here

Worldwide flight data

Open flights: As of January 2017, the OpenFlights Airports Database contains over 10,000 airports, train stations and ferry terminals spanning the globe

Download: airports.dat (Airports only, high quality)

Download: airports-extended.dat (Airports, train stations and ferry terminals, including user contributions)

Bureau of Transportation:

Flightera.net seems to have a lot of good data for free. It has in-depth data on flights and doesn’t seem limited by date. I can’t speak on the validity of the data though.

flightradar24.com has lots of data, also historically, they might be willing to help you get it in a nice format.

 

2019 Crime statistics in the USA

Dataset with arrest in US by race and separate states. Download Excel here

  • Database
  • MySQL
  • SQLAlchemy
  • InfluxDB
  • Neo4j
  • MongoDB – NoSQL database.
  • RethinkDB
  • TinkerPop – Graph computing framework.
  • PostgreSQL – Object-relational database.
  • CouchDB – Document-oriented NoSQL database.
  • HBase – Distributed, scalable, big data store.
  • NoSQL Guides – Help on using non-relational, distributed, open-source, and horizontally scalable databases.
  • Contexture – Abstracts queries/filters and results/aggregations from different backing data stores like ElasticSearch and MongoDB.
  • Database Tools – Everything that makes working with databases easier.
  • Grakn – Logical database to organize large and complex networks of data as one body of knowledge.

Researchers from IBM, MIT and Harvard Announced The Release Of DARPA “Common Sense AI” Dataset Along With Two Machine Learning Models At ICML 2021

Building machines that can make decisions based on common sense is no easy feat. A machine must be able to do more than merely find patterns in data; it also needs a way of interpreting the intentions and beliefs behind people’s choices.

At the 2021 International Conference on Machine Learning (ICML), Researchers from IBM, MIT, and Harvard University have come together to release a DARPA “Common Sense AI” dataset for benchmarking AI intuition. They are also releasing two machine learning models that represent different approaches to the problem that relies on testing techniques psychologists use to study infants’ behavior to accelerate the development of AI exhibiting common sense.

Source – Summary – Paper – IBM Blog

100 million protein structures Dataset by DeepMind

DeepMind creates ‘transformative’ map of human proteins drawn by AI. By the end of the year, DeepMind hopes to release predictions for 100 million protein structures, a dataset that will be “transformative for our understanding of how life works,

Here’s a good article about this topic

Google Dataset Search

Google Dataset Search

Malware traffic dataset

Comprises 1914081 records created from all malware traffic analysis .net PCAP files, from 2013 to 2021. The logs are generated using Suricata and Zeek.

Originator: ali_alwashali

Percent of “foreign-born” population in each US and EU state or country.

For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state 🇺🇸🇪🇺

Author: Here

Percent of “foreign-born” population in each US and EU state or country. For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state.

Examples of “foreign-born” in this context:

  • Person born in Spain and living in France is NOT “foreign-born”

  • Person born in Turkey and living in France is “foreign-born”

  • Person born in Florida and living in Texas is NOT “foreign-born”

  • Person born in Mexico and living in Texas is “foreign-born”

  • Person born in Florida and living in France is “foreign-born”

  • Person born in France and living in Florida is “foreign-born”

🇺🇸🇪🇺🗺️

Note: Poland, Ireland, Germany, Greece, Cyprus, Malta, Portugal uses Eurostat 2010 Migration data and Croatia has no data at all

Link1

Link2

Link3

Tools: MS Office

Source: Here

35% of “entry-level” jobs on LinkedIn require 3+ years of experience

r/dataisbeautiful - [OC] 35% of "entry-level" jobs on LinkedIn require 3+ years of experience

Source: LinkedIn data  (see original post)

Tool: Photoshop from my colleague

Latest complete Netflix movie dataset

Created from 4 APIs. 11K+ rows and 30+ attributes of Netflix (Ratings, earnings, actors, language, availability, movie trailers, and many more)

Dataset on Kaggle.

Explore this dataset using FlixGem.com (this dataset is powering this webapp)

Dataset on Google Sheets.

Common Crawl

A corpus of web crawl data composed of over 50 billion web pages. The Common Crawl corpus contains petabytes of data collected since 2008. It contains raw web page data, extracted metadata and text extractions.

AWS CLI Access (No AWS account required)

aws s3 ls s3://commoncrawl/ --no-sign-request

s3://commoncrawl/crawl-data/CC-MAIN-2021-17 – April 2021

 Dataset on protein prices

Data on Primary Commodity Prices are updated monthly based on the IMF’s Primary Commodity Price System.

Excel Database

 CPOST dataset on suicide attacks over four decades

The University of Chicago Project on Security and Threats presents the updated and expanded Database on Suicide Attacks (DSAT), which now links to Uppsala Conflict Data Program data on armed conflicts and includes a new dataset measuring the alliance and rivalry relationships among militant groups with connections to suicide attack groups. Access it here.

Credit Card Dataset – Survey of Consumer Finances (SCF) Combined Extract Data 1989-2019

You can do a lot of aggregated analysis in a pretty straightforward way there.

Drone imagery with annotations for small object detection and tracking dataset

11 TB dataset of drone imagery with annotations for small object detection and tracking

Download and more information are available here

Dataset License: CDLA-Sharing-1.0

Helper scripts for accessing the dataset: DATASET.md

Dataset Exploration: Colab

NOAA High-Resolution Rapid Refresh (HRRR) Model

The HRRR is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model, initialized by 3km grids with 3km radar assimilation. Radar data is assimilated in the HRRR every 15 min over a 1-h period adding further detail to that provided by the hourly data assimilation from the 13km radar-enhanced Rapid Refresh.

Registry of Open Data on AWS

This registry exists to help people discover and share datasets that are available via AWS resources. Learn more about sharing data on AWS.

See all usage examples for datasets listed in this registry.

See datasets from Digital Earth AfricaFacebook Data for GoodNASA Space Act AgreementNIH STRIDESNOAA Big Data ProgramSpace Telescope Science Institute, and Amazon Sustainability Data Initiative.

Textbook Question Answering (TQA)

1,076 textbook lessons, 26,260 questions, 6229 images

Documentation: allenai.org/data/tqa

Download

Harmonized Cancer Datasets: Genomic Data Commons Data Portal

The GDC Data Portal is a robust data-driven platform that allows cancer
researchers and bioinformaticians to search and download cancer data for analysis.

Genomic Data Commons Data Portal
Genomic Data Commons Data Portal

The Cancer Genome Atlas

The Cancer Genome Atlas (TCGA), a collaboration between the National Cancer Institute (NCI) and National Human Genome Research Institute (NHGRI), aims to generate comprehensive, multi-dimensional maps of the key genomic changes in major types and subtypes of cancer.

AWS CLI Access (No AWS account required)

aws s3 ls s3://tcga-2-open/ --no-sign-request

Therapeutically Applicable Research to Generate Effective Treatments (TARGET)

The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) program applies a comprehensive genomic approach to determine molecular changes that drive childhood cancers. The goal of the program is to use data to guide the development of effective, less toxic therapies. TARGET is organized into a collaborative network of disease-specific project teams.  TARGET projects provide comprehensive molecular characterization to determine the genetic changes that drive the initiation and progression of childhood cancers. The dataset contains open Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-Seq Isoform Expression Quantification, miRNA-Seq miRNA Expression Quantification data from Genomic Data Commons (GDC), and open data from GDC Legacy Archive. Access it here.

Genome Aggregation Database (gnomAD)

The Genome Aggregation Database (gnomAD) is a resource developed by an international coalition of investigators that aggregates and harmonizes both exome and genome data from a wide range of large-scale human sequencing projects. The summary data provided here are released for the benefit of the wider scientific community without restriction on use. Downloads

SQuAD (Stanford Question Answering Dataset)

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. Access it here.

PubMed Diabetes Dataset

The Pubmed Diabetes dataset consists of 19717 scientific publications from PubMed database pertaining to diabetes classified into one of three classes. The citation network consists of 44338 links. Each publication in the dataset is described by a TF/IDF weighted word vector from a dictionary which consists of 500 unique words. The README file in the dataset provides more details.

Download Link

Drug-Target Interaction Dataset

This dataset contains interactions between drugs and targets collected from DrugBank, KEGG Drug, DCDB, and Matador. It was originally collected by Perlman et al. It contains 315 drugs, 250 targets, 1,306 drug-target interactions, 5 types of drug-drug similarities, and 3 types of target-target similarities. Drug-drug similarities include Chemical-based, Ligand-based, Expression-based, Side-effect-based, and Annotation-based similarities. Target-target similarities include Sequence-based, Protein-protein interaction network-based, and Gene Ontology-based similarities. The original task on the dataset is to predict new interactions between drugs and targets based on different types of similarities in the network. Download link

Pharmacogenomics Datasets

PharmGKB data and knowledge is available as downloads. It is often critical to check with their curators at feedback@pharmgkb.org before embarking on a large project using these data, to be sure that the files and data they make available are being interpreted correctly. PharmGKB generally does NOT need to be a co-author on such analyses; They just want to make sure that there is a correct understanding of our data before lots of resources are spent.

Pancreatic Cancer Organoid Profiling

The dataset contains open RNA-Seq Gene Expression Quantification data and controlled WGS/WXS/RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic Mutation, and RNA-Seq Splice Junction Quantification. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://gdc-organoid-pancreatic-phs001611-2-open/ --no-sign-request

Africa Soil Information Service (AfSIS) Soil Chemistry

This dataset contains soil infrared spectral data and paired soil property reference measurements for georeferenced soil samples that were collected through the Africa Soil Information Service (AfSIS) project, which lasted from 2009 through 2018. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://afsis/ --no-sign-request

Dataset for Affective States in E-Environments

DAiSEE is the first multi-label video classification dataset comprising of 9068 video snippets captured from 112 users for recognizing the user affective states of boredom, confusion, engagement, and frustration “in the wild”. The dataset has four levels of labels namely – very low, low, high, and very high for each of the affective states, which are crowd annotated and correlated with a gold standard annotation created using a team of expert psychologists. Download it here.

NatureServe Explorer Dataset

NatureServe Explorer provides conservation status, taxonomy, distribution, and life history information for more than 95,000 plants and animals in the United States and Canada, and more than 10,000 vegetation communities and ecological systems in the Western Hemisphere.

The data available through NatureServe Explorer represents data managed in the NatureServe Central Databases. These databases are dynamic, being continually enhanced and refined through the input of hundreds of natural heritage program scientists and other collaborators. NatureServe Explorer is updated from these central databases to reflect information from new field surveys, the latest taxonomic treatments and other scientific publications, and new conservation status assessments. Explore Data here

Flight Records in the US

Airline On-Time Performance and Causes of Flight Delays – On_Time Data.

This database contains scheduled and actual departure and arrival times, reason of delay. reported by certified U.S. air carriers that account for at least one percent of domestic scheduled passenger revenues. The data is collected by the Office of Airline Information, Bureau of Transportation Statistics (BTS).

FlightAware.com has data but you need to pay for a full dataset.

The anyflights package supplies a set of functions to generate air travel data (and data packages!) similar to nycflights13. With a user-defined year and airport, the anyflights function will grab data on:

  • flights: all flights that departed a given airport in a given year and month
  • weather: hourly meterological data for a given airport in a given year and month
  • airports: airport names, FAA codes, and locations
  • airlines: translation between two letter carrier (airline) codes and names
  • planes: construction information about each plane found in flights

Airline On-Time Statistics and Delay Causes

The U.S. Department of Transportation’s (DOT) Bureau of Transportation Statistics (BTS) tracks the on-time performance of domestic flights operated by large air carriers. Summary information on the number of on-time, delayed, canceled and diverted flights appears in DOT’s monthly Air Travel Consumer Report, published about 30 days after the month’s end, as well as in summary tables posted on this website. BTS began collecting details on the causes of flight delays in June 2003. Summary statistics and raw data are made available to the public at the time the Air Travel Consumer Report is released. Access it here

Worldwide flight data

Open flights: As of January 2017, the OpenFlights Airports Database contains over 10,000 airports, train stations and ferry terminals spanning the globe

Download: airports.dat (Airports only, high quality)

Download: airports-extended.dat (Airports, train stations and ferry terminals, including user contributions)

Bureau of Transportation:

Flightera.net seems to have a lot of good data for free. It has in-depth data on flights and doesn’t seem limited by date. I can’t speak on the validity of the data though.

flightradar24.com has lots of data, also historically, they might be willing to help you get it in a nice format.

 

2019 Crime statistics in the USA

Dataset with arrest in US by race and separate states. Download Excel here

Researchers from IBM, MIT and Harvard Announced The Release Of DARPA “Common Sense AI” Dataset Along With Two Machine Learning Models At ICML 2021

Building machines that can make decisions based on common sense is no easy feat. A machine must be able to do more than merely find patterns in data; it also needs a way of interpreting the intentions and beliefs behind people’s choices.

At the 2021 International Conference on Machine Learning (ICML), Researchers from IBM, MIT, and Harvard University have come together to release a DARPA “Common Sense AI” dataset for benchmarking AI intuition. They are also releasing two machine learning models that represent different approaches to the problem that relies on testing techniques psychologists use to study infants’ behavior to accelerate the development of AI exhibiting common sense.

Source – Summary – Paper – IBM Blog

100 million protein structures Dataset by DeepMind

DeepMind creates ‘transformative’ map of human proteins drawn by AI. By the end of the year, DeepMind hopes to release predictions for 100 million protein structures, a dataset that will be “transformative for our understanding of how life works,

Here’s a good article about this topic

Google Dataset Search

Google Dataset Search

Malware traffic dataset

Comprises 1914081 records created from all malware traffic analysis .net PCAP files, from 2013 to 2021. The logs are generated using Suricata and Zeek.

Originator: ali_alwashali

Percent of “foreign-born” population in each US and EU state or country.

For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state 🇺🇸🇪🇺

Author: Here

Percent of “foreign-born” population in each US and EU state or country. For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state.

Examples of “foreign-born” in this context:

  • Person born in Spain and living in France is NOT “foreign-born”

  • Person born in Turkey and living in France is “foreign-born”

  • Person born in Florida and living in Texas is NOT “foreign-born”

  • Person born in Mexico and living in Texas is “foreign-born”

  • Person born in Florida and living in France is “foreign-born”

  • Person born in France and living in Florida is “foreign-born”

🇺🇸🇪🇺🗺️

Note: Poland, Ireland, Germany, Greece, Cyprus, Malta, Portugal uses Eurostat 2010 Migration data and Croatia has no data at all

Link1

Link2

Link3

Tools: MS Office

Source: Here

35% of “entry-level” jobs on LinkedIn require 3+ years of experience

r/dataisbeautiful - [OC] 35% of "entry-level" jobs on LinkedIn require 3+ years of experience

Source: LinkedIn data  (see original post)

Tool: Photoshop from my colleague

Latest complete Netflix movie dataset

Created from 4 APIs. 11K+ rows and 30+ attributes of Netflix (Ratings, earnings, actors, language, availability, movie trailers, and many more)

Dataset on Kaggle.

Explore this dataset using FlixGem.com (this dataset is powering this webapp)

Dataset on Google Sheets.

Common Crawl

A corpus of web crawl data composed of over 50 billion web pages. The Common Crawl corpus contains petabytes of data collected since 2008. It contains raw web page data, extracted metadata and text extractions.

AWS CLI Access (No AWS account required)

aws s3 ls s3://commoncrawl/ --no-sign-request

s3://commoncrawl/crawl-data/CC-MAIN-2021-17 – April 2021

 Dataset on protein prices

Data on Primary Commodity Prices are updated monthly based on the IMF’s Primary Commodity Price System.

Excel Database

 CPOST dataset on suicide attacks over four decades

The University of Chicago Project on Security and Threats presents the updated and expanded Database on Suicide Attacks (DSAT), which now links to Uppsala Conflict Data Program data on armed conflicts and includes a new dataset measuring the alliance and rivalry relationships among militant groups with connections to suicide attack groups. Access it here.

Credit Card Dataset – Survey of Consumer Finances (SCF) Combined Extract Data 1989-2019

You can do a lot of aggregated analysis in a pretty straightforward way there.

Drone imagery with annotations for small object detection and tracking dataset

11 TB dataset of drone imagery with annotations for small object detection and tracking

Download and more information are available here

Dataset License: CDLA-Sharing-1.0

Helper scripts for accessing the dataset: DATASET.md

Dataset Exploration: Colab

NOAA High-Resolution Rapid Refresh (HRRR) Model

The HRRR is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model, initialized by 3km grids with 3km radar assimilation. Radar data is assimilated in the HRRR every 15 min over a 1-h period adding further detail to that provided by the hourly data assimilation from the 13km radar-enhanced Rapid Refresh.

Registry of Open Data on AWS

This registry exists to help people discover and share datasets that are available via AWS resources. Learn more about sharing data on AWS.

See all usage examples for datasets listed in this registry.

See datasets from Digital Earth AfricaFacebook Data for GoodNASA Space Act AgreementNIH STRIDESNOAA Big Data ProgramSpace Telescope Science Institute, and Amazon Sustainability Data Initiative.

Textbook Question Answering (TQA)

1,076 textbook lessons, 26,260 questions, 6229 images

Documentation: allenai.org/data/tqa

Download

Harmonized Cancer Datasets: Genomic Data Commons Data Portal

The GDC Data Portal is a robust data-driven platform that allows cancer
researchers and bioinformaticians to search and download cancer data for analysis.

Genomic Data Commons Data Portal
Genomic Data Commons Data Portal

The Cancer Genome Atlas

The Cancer Genome Atlas (TCGA), a collaboration between the National Cancer Institute (NCI) and National Human Genome Research Institute (NHGRI), aims to generate comprehensive, multi-dimensional maps of the key genomic changes in major types and subtypes of cancer.

AWS CLI Access (No AWS account required)

aws s3 ls s3://tcga-2-open/ --no-sign-request

Therapeutically Applicable Research to Generate Effective Treatments (TARGET)

The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) program applies a comprehensive genomic approach to determine molecular changes that drive childhood cancers. The goal of the program is to use data to guide the development of effective, less toxic therapies. TARGET is organized into a collaborative network of disease-specific project teams.  TARGET projects provide comprehensive molecular characterization to determine the genetic changes that drive the initiation and progression of childhood cancers. The dataset contains open Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-Seq Isoform Expression Quantification, miRNA-Seq miRNA Expression Quantification data from Genomic Data Commons (GDC), and open data from GDC Legacy Archive. Access it here.

Genome Aggregation Database (gnomAD)

The Genome Aggregation Database (gnomAD) is a resource developed by an international coalition of investigators that aggregates and harmonizes both exome and genome data from a wide range of large-scale human sequencing projects. The summary data provided here are released for the benefit of the wider scientific community without restriction on use. Downloads

SQuAD (Stanford Question Answering Dataset)

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. Access it here.

PubMed Diabetes Dataset

The Pubmed Diabetes dataset consists of 19717 scientific publications from PubMed database pertaining to diabetes classified into one of three classes. The citation network consists of 44338 links. Each publication in the dataset is described by a TF/IDF weighted word vector from a dictionary which consists of 500 unique words. The README file in the dataset provides more details.

Download Link

Drug-Target Interaction Dataset

This dataset contains interactions between drugs and targets collected from DrugBank, KEGG Drug, DCDB, and Matador. It was originally collected by Perlman et al. It contains 315 drugs, 250 targets, 1,306 drug-target interactions, 5 types of drug-drug similarities, and 3 types of target-target similarities. Drug-drug similarities include Chemical-based, Ligand-based, Expression-based, Side-effect-based, and Annotation-based similarities. Target-target similarities include Sequence-based, Protein-protein interaction network-based, and Gene Ontology-based similarities. The original task on the dataset is to predict new interactions between drugs and targets based on different types of similarities in the network. Download link

Pharmacogenomics Datasets

PharmGKB data and knowledge is available as downloads. It is often critical to check with their curators at feedback@pharmgkb.org before embarking on a large project using these data, to be sure that the files and data they make available are being interpreted correctly. PharmGKB generally does NOT need to be a co-author on such analyses; They just want to make sure that there is a correct understanding of our data before lots of resources are spent.

Pancreatic Cancer Organoid Profiling

The dataset contains open RNA-Seq Gene Expression Quantification data and controlled WGS/WXS/RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic Mutation, and RNA-Seq Splice Junction Quantification. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://gdc-organoid-pancreatic-phs001611-2-open/ --no-sign-request

Africa Soil Information Service (AfSIS) Soil Chemistry

This dataset contains soil infrared spectral data and paired soil property reference measurements for georeferenced soil samples that were collected through the Africa Soil Information Service (AfSIS) project, which lasted from 2009 through 2018. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://afsis/ --no-sign-request

Dataset for Affective States in E-Environments

DAiSEE is the first multi-label video classification dataset comprising of 9068 video snippets captured from 112 users for recognizing the user affective states of boredom, confusion, engagement, and frustration “in the wild”. The dataset has four levels of labels namely – very low, low, high, and very high for each of the affective states, which are crowd annotated and correlated with a gold standard annotation created using a team of expert psychologists. Download it here.

NatureServe Explorer Dataset

NatureServe Explorer provides conservation status, taxonomy, distribution, and life history information for more than 95,000 plants and animals in the United States and Canada, and more than 10,000 vegetation communities and ecological systems in the Western Hemisphere.

The data available through NatureServe Explorer represents data managed in the NatureServe Central Databases. These databases are dynamic, being continually enhanced and refined through the input of hundreds of natural heritage program scientists and other collaborators. NatureServe Explorer is updated from these central databases to reflect information from new field surveys, the latest taxonomic treatments and other scientific publications, and new conservation status assessments. Explore Data here

Flight Records in the US

Airline On-Time Performance and Causes of Flight Delays – On_Time Data.

This database contains scheduled and actual departure and arrival times, reason of delay. reported by certified U.S. air carriers that account for at least one percent of domestic scheduled passenger revenues. The data is collected by the Office of Airline Information, Bureau of Transportation Statistics (BTS).

FlightAware.com has data but you need to pay for a full dataset.

The anyflights package supplies a set of functions to generate air travel data (and data packages!) similar to nycflights13. With a user-defined year and airport, the anyflights function will grab data on:

  • flights: all flights that departed a given airport in a given year and month
  • weather: hourly meterological data for a given airport in a given year and month
  • airports: airport names, FAA codes, and locations
  • airlines: translation between two letter carrier (airline) codes and names
  • planes: construction information about each plane found in flights

Airline On-Time Statistics and Delay Causes

The U.S. Department of Transportation’s (DOT) Bureau of Transportation Statistics (BTS) tracks the on-time performance of domestic flights operated by large air carriers. Summary information on the number of on-time, delayed, canceled and diverted flights appears in DOT’s monthly Air Travel Consumer Report, published about 30 days after the month’s end, as well as in summary tables posted on this website. BTS began collecting details on the causes of flight delays in June 2003. Summary statistics and raw data are made available to the public at the time the Air Travel Consumer Report is released. Access it here

Worldwide flight data

Open flights: As of January 2017, the OpenFlights Airports Database contains over 10,000 airports, train stations and ferry terminals spanning the globe

Download: airports.dat (Airports only, high quality)

Download: airports-extended.dat (Airports, train stations and ferry terminals, including user contributions)

Bureau of Transportation:

Flightera.net seems to have a lot of good data for free. It has in-depth data on flights and doesn’t seem limited by date. I can’t speak on the validity of the data though.

flightradar24.com has lots of data, also historically, they might be willing to help you get it in a nice format.

 

2019 Crime statistics in the USA

Dataset with arrest in US by race and separate states. Download Excel here

Researchers from IBM, MIT and Harvard Announced The Release Of DARPA “Common Sense AI” Dataset Along With Two Machine Learning Models At ICML 2021

Building machines that can make decisions based on common sense is no easy feat. A machine must be able to do more than merely find patterns in data; it also needs a way of interpreting the intentions and beliefs behind people’s choices.

At the 2021 International Conference on Machine Learning (ICML), Researchers from IBM, MIT, and Harvard University have come together to release a DARPA “Common Sense AI” dataset for benchmarking AI intuition. They are also releasing two machine learning models that represent different approaches to the problem that relies on testing techniques psychologists use to study infants’ behavior to accelerate the development of AI exhibiting common sense.

Source – Summary – Paper – IBM Blog

100 million protein structures Dataset by DeepMind

DeepMind creates ‘transformative’ map of human proteins drawn by AI. By the end of the year, DeepMind hopes to release predictions for 100 million protein structures, a dataset that will be “transformative for our understanding of how life works,

Here’s a good article about this topic

Google Dataset Search

Google Dataset Search

Malware traffic dataset

Comprises 1914081 records created from all malware traffic analysis .net PCAP files, from 2013 to 2021. The logs are generated using Suricata and Zeek.

Originator: ali_alwashali

Percent of “foreign-born” population in each US and EU state or country.

For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state 🇺🇸🇪🇺

Author: Here

Percent of “foreign-born” population in each US and EU state or country. For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state.

Examples of “foreign-born” in this context:

  • Person born in Spain and living in France is NOT “foreign-born”

  • Person born in Turkey and living in France is “foreign-born”

  • Person born in Florida and living in Texas is NOT “foreign-born”

  • Person born in Mexico and living in Texas is “foreign-born”

  • Person born in Florida and living in France is “foreign-born”

  • Person born in France and living in Florida is “foreign-born”

🇺🇸🇪🇺🗺️

Note: Poland, Ireland, Germany, Greece, Cyprus, Malta, Portugal uses Eurostat 2010 Migration data and Croatia has no data at all

Link1

Link2

Link3

Tools: MS Office

Source: Here

35% of “entry-level” jobs on LinkedIn require 3+ years of experience

r/dataisbeautiful - [OC] 35% of "entry-level" jobs on LinkedIn require 3+ years of experience

Source: LinkedIn data  (see original post)

Tool: Photoshop from my colleague

Latest complete Netflix movie dataset

Created from 4 APIs. 11K+ rows and 30+ attributes of Netflix (Ratings, earnings, actors, language, availability, movie trailers, and many more)

Dataset on Kaggle.

Explore this dataset using FlixGem.com (this dataset is powering this webapp)

Dataset on Google Sheets.

Common Crawl

A corpus of web crawl data composed of over 50 billion web pages. The Common Crawl corpus contains petabytes of data collected since 2008. It contains raw web page data, extracted metadata and text extractions.

AWS CLI Access (No AWS account required)

aws s3 ls s3://commoncrawl/ --no-sign-request

s3://commoncrawl/crawl-data/CC-MAIN-2021-17 – April 2021

 Dataset on protein prices

Data on Primary Commodity Prices are updated monthly based on the IMF’s Primary Commodity Price System.

Excel Database

 CPOST dataset on suicide attacks over four decades

The University of Chicago Project on Security and Threats presents the updated and expanded Database on Suicide Attacks (DSAT), which now links to Uppsala Conflict Data Program data on armed conflicts and includes a new dataset measuring the alliance and rivalry relationships among militant groups with connections to suicide attack groups. Access it here.

Credit Card Dataset – Survey of Consumer Finances (SCF) Combined Extract Data 1989-2019

You can do a lot of aggregated analysis in a pretty straightforward way there.

Drone imagery with annotations for small object detection and tracking dataset

11 TB dataset of drone imagery with annotations for small object detection and tracking

Download and more information are available here

Dataset License: CDLA-Sharing-1.0

Helper scripts for accessing the dataset: DATASET.md

Dataset Exploration: Colab

NOAA High-Resolution Rapid Refresh (HRRR) Model

The HRRR is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model, initialized by 3km grids with 3km radar assimilation. Radar data is assimilated in the HRRR every 15 min over a 1-h period adding further detail to that provided by the hourly data assimilation from the 13km radar-enhanced Rapid Refresh.

Registry of Open Data on AWS

This registry exists to help people discover and share datasets that are available via AWS resources. Learn more about sharing data on AWS.

See all usage examples for datasets listed in this registry.

See datasets from Digital Earth AfricaFacebook Data for GoodNASA Space Act AgreementNIH STRIDESNOAA Big Data ProgramSpace Telescope Science Institute, and Amazon Sustainability Data Initiative.

Textbook Question Answering (TQA)

1,076 textbook lessons, 26,260 questions, 6229 images

Documentation: allenai.org/data/tqa

Download

Harmonized Cancer Datasets: Genomic Data Commons Data Portal

The GDC Data Portal is a robust data-driven platform that allows cancer
researchers and bioinformaticians to search and download cancer data for analysis.

Genomic Data Commons Data Portal
Genomic Data Commons Data Portal

The Cancer Genome Atlas

The Cancer Genome Atlas (TCGA), a collaboration between the National Cancer Institute (NCI) and National Human Genome Research Institute (NHGRI), aims to generate comprehensive, multi-dimensional maps of the key genomic changes in major types and subtypes of cancer.

AWS CLI Access (No AWS account required)

aws s3 ls s3://tcga-2-open/ --no-sign-request

Therapeutically Applicable Research to Generate Effective Treatments (TARGET)

The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) program applies a comprehensive genomic approach to determine molecular changes that drive childhood cancers. The goal of the program is to use data to guide the development of effective, less toxic therapies. TARGET is organized into a collaborative network of disease-specific project teams.  TARGET projects provide comprehensive molecular characterization to determine the genetic changes that drive the initiation and progression of childhood cancers. The dataset contains open Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-Seq Isoform Expression Quantification, miRNA-Seq miRNA Expression Quantification data from Genomic Data Commons (GDC), and open data from GDC Legacy Archive. Access it here.

Genome Aggregation Database (gnomAD)

The Genome Aggregation Database (gnomAD) is a resource developed by an international coalition of investigators that aggregates and harmonizes both exome and genome data from a wide range of large-scale human sequencing projects. The summary data provided here are released for the benefit of the wider scientific community without restriction on use. Downloads

SQuAD (Stanford Question Answering Dataset)

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. Access it here.

PubMed Diabetes Dataset

The Pubmed Diabetes dataset consists of 19717 scientific publications from PubMed database pertaining to diabetes classified into one of three classes. The citation network consists of 44338 links. Each publication in the dataset is described by a TF/IDF weighted word vector from a dictionary which consists of 500 unique words. The README file in the dataset provides more details.

Download Link

Drug-Target Interaction Dataset

This dataset contains interactions between drugs and targets collected from DrugBank, KEGG Drug, DCDB, and Matador. It was originally collected by Perlman et al. It contains 315 drugs, 250 targets, 1,306 drug-target interactions, 5 types of drug-drug similarities, and 3 types of target-target similarities. Drug-drug similarities include Chemical-based, Ligand-based, Expression-based, Side-effect-based, and Annotation-based similarities. Target-target similarities include Sequence-based, Protein-protein interaction network-based, and Gene Ontology-based similarities. The original task on the dataset is to predict new interactions between drugs and targets based on different types of similarities in the network. Download link

Pharmacogenomics Datasets

PharmGKB data and knowledge is available as downloads. It is often critical to check with their curators at feedback@pharmgkb.org before embarking on a large project using these data, to be sure that the files and data they make available are being interpreted correctly. PharmGKB generally does NOT need to be a co-author on such analyses; They just want to make sure that there is a correct understanding of our data before lots of resources are spent.

Pancreatic Cancer Organoid Profiling

The dataset contains open RNA-Seq Gene Expression Quantification data and controlled WGS/WXS/RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic Mutation, and RNA-Seq Splice Junction Quantification. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://gdc-organoid-pancreatic-phs001611-2-open/ --no-sign-request

Africa Soil Information Service (AfSIS) Soil Chemistry

This dataset contains soil infrared spectral data and paired soil property reference measurements for georeferenced soil samples that were collected through the Africa Soil Information Service (AfSIS) project, which lasted from 2009 through 2018. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://afsis/ --no-sign-request

Dataset for Affective States in E-Environments

DAiSEE is the first multi-label video classification dataset comprising of 9068 video snippets captured from 112 users for recognizing the user affective states of boredom, confusion, engagement, and frustration “in the wild”. The dataset has four levels of labels namely – very low, low, high, and very high for each of the affective states, which are crowd annotated and correlated with a gold standard annotation created using a team of expert psychologists. Download it here.

NatureServe Explorer Dataset

NatureServe Explorer provides conservation status, taxonomy, distribution, and life history information for more than 95,000 plants and animals in the United States and Canada, and more than 10,000 vegetation communities and ecological systems in the Western Hemisphere.

The data available through NatureServe Explorer represents data managed in the NatureServe Central Databases. These databases are dynamic, being continually enhanced and refined through the input of hundreds of natural heritage program scientists and other collaborators. NatureServe Explorer is updated from these central databases to reflect information from new field surveys, the latest taxonomic treatments and other scientific publications, and new conservation status assessments. Explore Data here

Flight Records in the US

Airline On-Time Performance and Causes of Flight Delays – On_Time Data.

This database contains scheduled and actual departure and arrival times, reason of delay. reported by certified U.S. air carriers that account for at least one percent of domestic scheduled passenger revenues. The data is collected by the Office of Airline Information, Bureau of Transportation Statistics (BTS).

FlightAware.com has data but you need to pay for a full dataset.

The anyflights package supplies a set of functions to generate air travel data (and data packages!) similar to nycflights13. With a user-defined year and airport, the anyflights function will grab data on:

  • flights: all flights that departed a given airport in a given year and month
  • weather: hourly meterological data for a given airport in a given year and month
  • airports: airport names, FAA codes, and locations
  • airlines: translation between two letter carrier (airline) codes and names
  • planes: construction information about each plane found in flights

Airline On-Time Statistics and Delay Causes

The U.S. Department of Transportation’s (DOT) Bureau of Transportation Statistics (BTS) tracks the on-time performance of domestic flights operated by large air carriers. Summary information on the number of on-time, delayed, canceled and diverted flights appears in DOT’s monthly Air Travel Consumer Report, published about 30 days after the month’s end, as well as in summary tables posted on this website. BTS began collecting details on the causes of flight delays in June 2003. Summary statistics and raw data are made available to the public at the time the Air Travel Consumer Report is released. Access it here

Worldwide flight data

Open flights: As of January 2017, the OpenFlights Airports Database contains over 10,000 airports, train stations and ferry terminals spanning the globe

Download: airports.dat (Airports only, high quality)

Download: airports-extended.dat (Airports, train stations and ferry terminals, including user contributions)

Bureau of Transportation:

Flightera.net seems to have a lot of good data for free. It has in-depth data on flights and doesn’t seem limited by date. I can’t speak on the validity of the data though.

flightradar24.com has lots of data, also historically, they might be willing to help you get it in a nice format.

 

2019 Crime statistics in the USA

Dataset with arrest in US by race and separate states. Download Excel here

Researchers from IBM, MIT and Harvard Announced The Release Of DARPA “Common Sense AI” Dataset Along With Two Machine Learning Models At ICML 2021

Building machines that can make decisions based on common sense is no easy feat. A machine must be able to do more than merely find patterns in data; it also needs a way of interpreting the intentions and beliefs behind people’s choices.

At the 2021 International Conference on Machine Learning (ICML), Researchers from IBM, MIT, and Harvard University have come together to release a DARPA “Common Sense AI” dataset for benchmarking AI intuition. They are also releasing two machine learning models that represent different approaches to the problem that relies on testing techniques psychologists use to study infants’ behavior to accelerate the development of AI exhibiting common sense.

Source – Summary – Paper – IBM Blog

100 million protein structures Dataset by DeepMind

DeepMind creates ‘transformative’ map of human proteins drawn by AI. By the end of the year, DeepMind hopes to release predictions for 100 million protein structures, a dataset that will be “transformative for our understanding of how life works,

Here’s a good article about this topic

Google Dataset Search

Google Dataset Search

Malware traffic dataset

Comprises 1914081 records created from all malware traffic analysis .net PCAP files, from 2013 to 2021. The logs are generated using Suricata and Zeek.

Originator: ali_alwashali

Percent of “foreign-born” population in each US and EU state or country.

For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state 🇺🇸🇪🇺

Author: Here

Percent of “foreign-born” population in each US and EU state or country. For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state.

Examples of “foreign-born” in this context:

  • Person born in Spain and living in France is NOT “foreign-born”

  • Person born in Turkey and living in France is “foreign-born”

  • Person born in Florida and living in Texas is NOT “foreign-born”

  • Person born in Mexico and living in Texas is “foreign-born”

  • Person born in Florida and living in France is “foreign-born”

  • Person born in France and living in Florida is “foreign-born”

🇺🇸🇪🇺🗺️

Note: Poland, Ireland, Germany, Greece, Cyprus, Malta, Portugal uses Eurostat 2010 Migration data and Croatia has no data at all

Link1

Link2

Link3

Tools: MS Office

Source: Here

35% of “entry-level” jobs on LinkedIn require 3+ years of experience

r/dataisbeautiful - [OC] 35% of "entry-level" jobs on LinkedIn require 3+ years of experience

Source: LinkedIn data  (see original post)

Tool: Photoshop from my colleague

Latest complete Netflix movie dataset

Created from 4 APIs. 11K+ rows and 30+ attributes of Netflix (Ratings, earnings, actors, language, availability, movie trailers, and many more)

Dataset on Kaggle.

Explore this dataset using FlixGem.com (this dataset is powering this webapp)

Dataset on Google Sheets.

Common Crawl

A corpus of web crawl data composed of over 50 billion web pages. The Common Crawl corpus contains petabytes of data collected since 2008. It contains raw web page data, extracted metadata and text extractions.

AWS CLI Access (No AWS account required)

aws s3 ls s3://commoncrawl/ --no-sign-request

s3://commoncrawl/crawl-data/CC-MAIN-2021-17 – April 2021

 Dataset on protein prices

Data on Primary Commodity Prices are updated monthly based on the IMF’s Primary Commodity Price System.

Excel Database

 CPOST dataset on suicide attacks over four decades

The University of Chicago Project on Security and Threats presents the updated and expanded Database on Suicide Attacks (DSAT), which now links to Uppsala Conflict Data Program data on armed conflicts and includes a new dataset measuring the alliance and rivalry relationships among militant groups with connections to suicide attack groups. Access it here.

Credit Card Dataset – Survey of Consumer Finances (SCF) Combined Extract Data 1989-2019

You can do a lot of aggregated analysis in a pretty straightforward way there.

Drone imagery with annotations for small object detection and tracking dataset

11 TB dataset of drone imagery with annotations for small object detection and tracking

Download and more information are available here

Dataset License: CDLA-Sharing-1.0

Helper scripts for accessing the dataset: DATASET.md

Dataset Exploration: Colab

NOAA High-Resolution Rapid Refresh (HRRR) Model

The HRRR is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model, initialized by 3km grids with 3km radar assimilation. Radar data is assimilated in the HRRR every 15 min over a 1-h period adding further detail to that provided by the hourly data assimilation from the 13km radar-enhanced Rapid Refresh.

Registry of Open Data on AWS

This registry exists to help people discover and share datasets that are available via AWS resources. Learn more about sharing data on AWS.

See all usage examples for datasets listed in this registry.

See datasets from Digital Earth AfricaFacebook Data for GoodNASA Space Act AgreementNIH STRIDESNOAA Big Data ProgramSpace Telescope Science Institute, and Amazon Sustainability Data Initiative.

Textbook Question Answering (TQA)

1,076 textbook lessons, 26,260 questions, 6229 images

Documentation: allenai.org/data/tqa

Download

Harmonized Cancer Datasets: Genomic Data Commons Data Portal

The GDC Data Portal is a robust data-driven platform that allows cancer
researchers and bioinformaticians to search and download cancer data for analysis.

Genomic Data Commons Data Portal
Genomic Data Commons Data Portal

The Cancer Genome Atlas

The Cancer Genome Atlas (TCGA), a collaboration between the National Cancer Institute (NCI) and National Human Genome Research Institute (NHGRI), aims to generate comprehensive, multi-dimensional maps of the key genomic changes in major types and subtypes of cancer.

AWS CLI Access (No AWS account required)

aws s3 ls s3://tcga-2-open/ --no-sign-request

Therapeutically Applicable Research to Generate Effective Treatments (TARGET)

The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) program applies a comprehensive genomic approach to determine molecular changes that drive childhood cancers. The goal of the program is to use data to guide the development of effective, less toxic therapies. TARGET is organized into a collaborative network of disease-specific project teams.  TARGET projects provide comprehensive molecular characterization to determine the genetic changes that drive the initiation and progression of childhood cancers. The dataset contains open Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-Seq Isoform Expression Quantification, miRNA-Seq miRNA Expression Quantification data from Genomic Data Commons (GDC), and open data from GDC Legacy Archive. Access it here.

Genome Aggregation Database (gnomAD)

The Genome Aggregation Database (gnomAD) is a resource developed by an international coalition of investigators that aggregates and harmonizes both exome and genome data from a wide range of large-scale human sequencing projects. The summary data provided here are released for the benefit of the wider scientific community without restriction on use. Downloads

SQuAD (Stanford Question Answering Dataset)

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. Access it here.

PubMed Diabetes Dataset

The Pubmed Diabetes dataset consists of 19717 scientific publications from PubMed database pertaining to diabetes classified into one of three classes. The citation network consists of 44338 links. Each publication in the dataset is described by a TF/IDF weighted word vector from a dictionary which consists of 500 unique words. The README file in the dataset provides more details.

Download Link

Drug-Target Interaction Dataset

This dataset contains interactions between drugs and targets collected from DrugBank, KEGG Drug, DCDB, and Matador. It was originally collected by Perlman et al. It contains 315 drugs, 250 targets, 1,306 drug-target interactions, 5 types of drug-drug similarities, and 3 types of target-target similarities. Drug-drug similarities include Chemical-based, Ligand-based, Expression-based, Side-effect-based, and Annotation-based similarities. Target-target similarities include Sequence-based, Protein-protein interaction network-based, and Gene Ontology-based similarities. The original task on the dataset is to predict new interactions between drugs and targets based on different types of similarities in the network. Download link

Pharmacogenomics Datasets

PharmGKB data and knowledge is available as downloads. It is often critical to check with their curators at feedback@pharmgkb.org before embarking on a large project using these data, to be sure that the files and data they make available are being interpreted correctly. PharmGKB generally does NOT need to be a co-author on such analyses; They just want to make sure that there is a correct understanding of our data before lots of resources are spent.

Pancreatic Cancer Organoid Profiling

The dataset contains open RNA-Seq Gene Expression Quantification data and controlled WGS/WXS/RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic Mutation, and RNA-Seq Splice Junction Quantification. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://gdc-organoid-pancreatic-phs001611-2-open/ --no-sign-request

Africa Soil Information Service (AfSIS) Soil Chemistry

This dataset contains soil infrared spectral data and paired soil property reference measurements for georeferenced soil samples that were collected through the Africa Soil Information Service (AfSIS) project, which lasted from 2009 through 2018. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://afsis/ --no-sign-request

Dataset for Affective States in E-Environments

DAiSEE is the first multi-label video classification dataset comprising of 9068 video snippets captured from 112 users for recognizing the user affective states of boredom, confusion, engagement, and frustration “in the wild”. The dataset has four levels of labels namely – very low, low, high, and very high for each of the affective states, which are crowd annotated and correlated with a gold standard annotation created using a team of expert psychologists. Download it here.

NatureServe Explorer Dataset

NatureServe Explorer provides conservation status, taxonomy, distribution, and life history information for more than 95,000 plants and animals in the United States and Canada, and more than 10,000 vegetation communities and ecological systems in the Western Hemisphere.

The data available through NatureServe Explorer represents data managed in the NatureServe Central Databases. These databases are dynamic, being continually enhanced and refined through the input of hundreds of natural heritage program scientists and other collaborators. NatureServe Explorer is updated from these central databases to reflect information from new field surveys, the latest taxonomic treatments and other scientific publications, and new conservation status assessments. Explore Data here

Flight Records in the US

Airline On-Time Performance and Causes of Flight Delays – On_Time Data.

This database contains scheduled and actual departure and arrival times, reason of delay. reported by certified U.S. air carriers that account for at least one percent of domestic scheduled passenger revenues. The data is collected by the Office of Airline Information, Bureau of Transportation Statistics (BTS).

FlightAware.com has data but you need to pay for a full dataset.

The anyflights package supplies a set of functions to generate air travel data (and data packages!) similar to nycflights13. With a user-defined year and airport, the anyflights function will grab data on:

  • flights: all flights that departed a given airport in a given year and month
  • weather: hourly meterological data for a given airport in a given year and month
  • airports: airport names, FAA codes, and locations
  • airlines: translation between two letter carrier (airline) codes and names
  • planes: construction information about each plane found in flights

Airline On-Time Statistics and Delay Causes

The U.S. Department of Transportation’s (DOT) Bureau of Transportation Statistics (BTS) tracks the on-time performance of domestic flights operated by large air carriers. Summary information on the number of on-time, delayed, canceled and diverted flights appears in DOT’s monthly Air Travel Consumer Report, published about 30 days after the month’s end, as well as in summary tables posted on this website. BTS began collecting details on the causes of flight delays in June 2003. Summary statistics and raw data are made available to the public at the time the Air Travel Consumer Report is released. Access it here

Worldwide flight data

Open flights: As of January 2017, the OpenFlights Airports Database contains over 10,000 airports, train stations and ferry terminals spanning the globe

Download: airports.dat (Airports only, high quality)

Download: airports-extended.dat (Airports, train stations and ferry terminals, including user contributions)

Bureau of Transportation:

Flightera.net seems to have a lot of good data for free. It has in-depth data on flights and doesn’t seem limited by date. I can’t speak on the validity of the data though.

flightradar24.com has lots of data, also historically, they might be willing to help you get it in a nice format.

 

2019 Crime statistics in the USA

Dataset with arrest in US by race and separate states. Download Excel here

  • Umbraco
  • Refinery CMS – Ruby on Rails CMS.
  • Wagtail – Django CMS focused on flexibility and user experience.
  • Textpattern – Lightweight PHP-based CMS.
  • Drupal – Extensible PHP-based CMS.
  • Craft CMS – Content-first CMS.
  • Sitecore – .NET digital marketing platform that combines CMS with tools for managing multiple websites.
  • Silverstripe CMS – PHP MVC framework that serves as a classic or headless CMS.

Researchers from IBM, MIT and Harvard Announced The Release Of DARPA “Common Sense AI” Dataset Along With Two Machine Learning Models At ICML 2021

Building machines that can make decisions based on common sense is no easy feat. A machine must be able to do more than merely find patterns in data; it also needs a way of interpreting the intentions and beliefs behind people’s choices.

At the 2021 International Conference on Machine Learning (ICML), Researchers from IBM, MIT, and Harvard University have come together to release a DARPA “Common Sense AI” dataset for benchmarking AI intuition. They are also releasing two machine learning models that represent different approaches to the problem that relies on testing techniques psychologists use to study infants’ behavior to accelerate the development of AI exhibiting common sense.

Source – Summary – Paper – IBM Blog

100 million protein structures Dataset by DeepMind

DeepMind creates ‘transformative’ map of human proteins drawn by AI. By the end of the year, DeepMind hopes to release predictions for 100 million protein structures, a dataset that will be “transformative for our understanding of how life works,

Here’s a good article about this topic

Google Dataset Search

Google Dataset Search

Malware traffic dataset

Comprises 1914081 records created from all malware traffic analysis .net PCAP files, from 2013 to 2021. The logs are generated using Suricata and Zeek.

Originator: ali_alwashali

Percent of “foreign-born” population in each US and EU state or country.

For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state 🇺🇸🇪🇺

Author: Here

Percent of “foreign-born” population in each US and EU state or country. For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state.

Examples of “foreign-born” in this context:

  • Person born in Spain and living in France is NOT “foreign-born”

  • Person born in Turkey and living in France is “foreign-born”

  • Person born in Florida and living in Texas is NOT “foreign-born”

  • Person born in Mexico and living in Texas is “foreign-born”

  • Person born in Florida and living in France is “foreign-born”

  • Person born in France and living in Florida is “foreign-born”

🇺🇸🇪🇺🗺️

Note: Poland, Ireland, Germany, Greece, Cyprus, Malta, Portugal uses Eurostat 2010 Migration data and Croatia has no data at all

Link1

Link2

Link3

Tools: MS Office

Source: Here

35% of “entry-level” jobs on LinkedIn require 3+ years of experience

r/dataisbeautiful - [OC] 35% of "entry-level" jobs on LinkedIn require 3+ years of experience

Source: LinkedIn data  (see original post)

Tool: Photoshop from my colleague

Latest complete Netflix movie dataset

Created from 4 APIs. 11K+ rows and 30+ attributes of Netflix (Ratings, earnings, actors, language, availability, movie trailers, and many more)

Dataset on Kaggle.

Explore this dataset using FlixGem.com (this dataset is powering this webapp)

Dataset on Google Sheets.

Common Crawl

A corpus of web crawl data composed of over 50 billion web pages. The Common Crawl corpus contains petabytes of data collected since 2008. It contains raw web page data, extracted metadata and text extractions.

AWS CLI Access (No AWS account required)

aws s3 ls s3://commoncrawl/ --no-sign-request

s3://commoncrawl/crawl-data/CC-MAIN-2021-17 – April 2021

 Dataset on protein prices

Data on Primary Commodity Prices are updated monthly based on the IMF’s Primary Commodity Price System.

Excel Database

 CPOST dataset on suicide attacks over four decades

The University of Chicago Project on Security and Threats presents the updated and expanded Database on Suicide Attacks (DSAT), which now links to Uppsala Conflict Data Program data on armed conflicts and includes a new dataset measuring the alliance and rivalry relationships among militant groups with connections to suicide attack groups. Access it here.

Credit Card Dataset – Survey of Consumer Finances (SCF) Combined Extract Data 1989-2019

You can do a lot of aggregated analysis in a pretty straightforward way there.

Drone imagery with annotations for small object detection and tracking dataset

11 TB dataset of drone imagery with annotations for small object detection and tracking

Download and more information are available here

Dataset License: CDLA-Sharing-1.0

Helper scripts for accessing the dataset: DATASET.md

Dataset Exploration: Colab

NOAA High-Resolution Rapid Refresh (HRRR) Model

The HRRR is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model, initialized by 3km grids with 3km radar assimilation. Radar data is assimilated in the HRRR every 15 min over a 1-h period adding further detail to that provided by the hourly data assimilation from the 13km radar-enhanced Rapid Refresh.

Registry of Open Data on AWS

This registry exists to help people discover and share datasets that are available via AWS resources. Learn more about sharing data on AWS.

See all usage examples for datasets listed in this registry.

See datasets from Digital Earth AfricaFacebook Data for GoodNASA Space Act AgreementNIH STRIDESNOAA Big Data ProgramSpace Telescope Science Institute, and Amazon Sustainability Data Initiative.

Textbook Question Answering (TQA)

1,076 textbook lessons, 26,260 questions, 6229 images

Documentation: allenai.org/data/tqa

Download

Harmonized Cancer Datasets: Genomic Data Commons Data Portal

The GDC Data Portal is a robust data-driven platform that allows cancer
researchers and bioinformaticians to search and download cancer data for analysis.

Genomic Data Commons Data Portal
Genomic Data Commons Data Portal

The Cancer Genome Atlas

The Cancer Genome Atlas (TCGA), a collaboration between the National Cancer Institute (NCI) and National Human Genome Research Institute (NHGRI), aims to generate comprehensive, multi-dimensional maps of the key genomic changes in major types and subtypes of cancer.

AWS CLI Access (No AWS account required)

aws s3 ls s3://tcga-2-open/ --no-sign-request

Therapeutically Applicable Research to Generate Effective Treatments (TARGET)

The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) program applies a comprehensive genomic approach to determine molecular changes that drive childhood cancers. The goal of the program is to use data to guide the development of effective, less toxic therapies. TARGET is organized into a collaborative network of disease-specific project teams.  TARGET projects provide comprehensive molecular characterization to determine the genetic changes that drive the initiation and progression of childhood cancers. The dataset contains open Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-Seq Isoform Expression Quantification, miRNA-Seq miRNA Expression Quantification data from Genomic Data Commons (GDC), and open data from GDC Legacy Archive. Access it here.

Genome Aggregation Database (gnomAD)

The Genome Aggregation Database (gnomAD) is a resource developed by an international coalition of investigators that aggregates and harmonizes both exome and genome data from a wide range of large-scale human sequencing projects. The summary data provided here are released for the benefit of the wider scientific community without restriction on use. Downloads

SQuAD (Stanford Question Answering Dataset)

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. Access it here.

PubMed Diabetes Dataset

The Pubmed Diabetes dataset consists of 19717 scientific publications from PubMed database pertaining to diabetes classified into one of three classes. The citation network consists of 44338 links. Each publication in the dataset is described by a TF/IDF weighted word vector from a dictionary which consists of 500 unique words. The README file in the dataset provides more details.

Download Link

Drug-Target Interaction Dataset

This dataset contains interactions between drugs and targets collected from DrugBank, KEGG Drug, DCDB, and Matador. It was originally collected by Perlman et al. It contains 315 drugs, 250 targets, 1,306 drug-target interactions, 5 types of drug-drug similarities, and 3 types of target-target similarities. Drug-drug similarities include Chemical-based, Ligand-based, Expression-based, Side-effect-based, and Annotation-based similarities. Target-target similarities include Sequence-based, Protein-protein interaction network-based, and Gene Ontology-based similarities. The original task on the dataset is to predict new interactions between drugs and targets based on different types of similarities in the network. Download link

Pharmacogenomics Datasets

PharmGKB data and knowledge is available as downloads. It is often critical to check with their curators at feedback@pharmgkb.org before embarking on a large project using these data, to be sure that the files and data they make available are being interpreted correctly. PharmGKB generally does NOT need to be a co-author on such analyses; They just want to make sure that there is a correct understanding of our data before lots of resources are spent.

Pancreatic Cancer Organoid Profiling

The dataset contains open RNA-Seq Gene Expression Quantification data and controlled WGS/WXS/RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic Mutation, and RNA-Seq Splice Junction Quantification. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://gdc-organoid-pancreatic-phs001611-2-open/ --no-sign-request

Africa Soil Information Service (AfSIS) Soil Chemistry

This dataset contains soil infrared spectral data and paired soil property reference measurements for georeferenced soil samples that were collected through the Africa Soil Information Service (AfSIS) project, which lasted from 2009 through 2018. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://afsis/ --no-sign-request

Dataset for Affective States in E-Environments

DAiSEE is the first multi-label video classification dataset comprising of 9068 video snippets captured from 112 users for recognizing the user affective states of boredom, confusion, engagement, and frustration “in the wild”. The dataset has four levels of labels namely – very low, low, high, and very high for each of the affective states, which are crowd annotated and correlated with a gold standard annotation created using a team of expert psychologists. Download it here.

NatureServe Explorer Dataset

NatureServe Explorer provides conservation status, taxonomy, distribution, and life history information for more than 95,000 plants and animals in the United States and Canada, and more than 10,000 vegetation communities and ecological systems in the Western Hemisphere.

The data available through NatureServe Explorer represents data managed in the NatureServe Central Databases. These databases are dynamic, being continually enhanced and refined through the input of hundreds of natural heritage program scientists and other collaborators. NatureServe Explorer is updated from these central databases to reflect information from new field surveys, the latest taxonomic treatments and other scientific publications, and new conservation status assessments. Explore Data here

Flight Records in the US

Airline On-Time Performance and Causes of Flight Delays – On_Time Data.

This database contains scheduled and actual departure and arrival times, reason of delay. reported by certified U.S. air carriers that account for at least one percent of domestic scheduled passenger revenues. The data is collected by the Office of Airline Information, Bureau of Transportation Statistics (BTS).

FlightAware.com has data but you need to pay for a full dataset.

The anyflights package supplies a set of functions to generate air travel data (and data packages!) similar to nycflights13. With a user-defined year and airport, the anyflights function will grab data on:

  • flights: all flights that departed a given airport in a given year and month
  • weather: hourly meterological data for a given airport in a given year and month
  • airports: airport names, FAA codes, and locations
  • airlines: translation between two letter carrier (airline) codes and names
  • planes: construction information about each plane found in flights

Airline On-Time Statistics and Delay Causes

The U.S. Department of Transportation’s (DOT) Bureau of Transportation Statistics (BTS) tracks the on-time performance of domestic flights operated by large air carriers. Summary information on the number of on-time, delayed, canceled and diverted flights appears in DOT’s monthly Air Travel Consumer Report, published about 30 days after the month’s end, as well as in summary tables posted on this website. BTS began collecting details on the causes of flight delays in June 2003. Summary statistics and raw data are made available to the public at the time the Air Travel Consumer Report is released. Access it here

Worldwide flight data

Open flights: As of January 2017, the OpenFlights Airports Database contains over 10,000 airports, train stations and ferry terminals spanning the globe

Download: airports.dat (Airports only, high quality)

Download: airports-extended.dat (Airports, train stations and ferry terminals, including user contributions)

Bureau of Transportation:

Flightera.net seems to have a lot of good data for free. It has in-depth data on flights and doesn’t seem limited by date. I can’t speak on the validity of the data though.

flightradar24.com has lots of data, also historically, they might be willing to help you get it in a nice format.

 

2019 Crime statistics in the USA

Dataset with arrest in US by race and separate states. Download Excel here

Researchers from IBM, MIT and Harvard Announced The Release Of DARPA “Common Sense AI” Dataset Along With Two Machine Learning Models At ICML 2021

Building machines that can make decisions based on common sense is no easy feat. A machine must be able to do more than merely find patterns in data; it also needs a way of interpreting the intentions and beliefs behind people’s choices.

At the 2021 International Conference on Machine Learning (ICML), Researchers from IBM, MIT, and Harvard University have come together to release a DARPA “Common Sense AI” dataset for benchmarking AI intuition. They are also releasing two machine learning models that represent different approaches to the problem that relies on testing techniques psychologists use to study infants’ behavior to accelerate the development of AI exhibiting common sense.

Source – Summary – Paper – IBM Blog

100 million protein structures Dataset by DeepMind

DeepMind creates ‘transformative’ map of human proteins drawn by AI. By the end of the year, DeepMind hopes to release predictions for 100 million protein structures, a dataset that will be “transformative for our understanding of how life works,

Here’s a good article about this topic

Google Dataset Search

Google Dataset Search

Malware traffic dataset

Comprises 1914081 records created from all malware traffic analysis .net PCAP files, from 2013 to 2021. The logs are generated using Suricata and Zeek.

Originator: ali_alwashali

Percent of “foreign-born” population in each US and EU state or country.

For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state 🇺🇸🇪🇺

Author: Here

Percent of “foreign-born” population in each US and EU state or country. For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state.

Examples of “foreign-born” in this context:

  • Person born in Spain and living in France is NOT “foreign-born”

  • Person born in Turkey and living in France is “foreign-born”

  • Person born in Florida and living in Texas is NOT “foreign-born”

  • Person born in Mexico and living in Texas is “foreign-born”

  • Person born in Florida and living in France is “foreign-born”

  • Person born in France and living in Florida is “foreign-born”

🇺🇸🇪🇺🗺️

Note: Poland, Ireland, Germany, Greece, Cyprus, Malta, Portugal uses Eurostat 2010 Migration data and Croatia has no data at all

Link1

Link2

Link3

Tools: MS Office

Source: Here

35% of “entry-level” jobs on LinkedIn require 3+ years of experience

r/dataisbeautiful - [OC] 35% of "entry-level" jobs on LinkedIn require 3+ years of experience

Source: LinkedIn data  (see original post)

Tool: Photoshop from my colleague

Latest complete Netflix movie dataset

Created from 4 APIs. 11K+ rows and 30+ attributes of Netflix (Ratings, earnings, actors, language, availability, movie trailers, and many more)

Dataset on Kaggle.

Explore this dataset using FlixGem.com (this dataset is powering this webapp)

Dataset on Google Sheets.

Common Crawl

A corpus of web crawl data composed of over 50 billion web pages. The Common Crawl corpus contains petabytes of data collected since 2008. It contains raw web page data, extracted metadata and text extractions.

AWS CLI Access (No AWS account required)

aws s3 ls s3://commoncrawl/ --no-sign-request

s3://commoncrawl/crawl-data/CC-MAIN-2021-17 – April 2021

 Dataset on protein prices

Data on Primary Commodity Prices are updated monthly based on the IMF’s Primary Commodity Price System.

Excel Database

 CPOST dataset on suicide attacks over four decades

The University of Chicago Project on Security and Threats presents the updated and expanded Database on Suicide Attacks (DSAT), which now links to Uppsala Conflict Data Program data on armed conflicts and includes a new dataset measuring the alliance and rivalry relationships among militant groups with connections to suicide attack groups. Access it here.

Credit Card Dataset – Survey of Consumer Finances (SCF) Combined Extract Data 1989-2019

You can do a lot of aggregated analysis in a pretty straightforward way there.

Drone imagery with annotations for small object detection and tracking dataset

11 TB dataset of drone imagery with annotations for small object detection and tracking

Download and more information are available here

Dataset License: CDLA-Sharing-1.0

Helper scripts for accessing the dataset: DATASET.md

Dataset Exploration: Colab

NOAA High-Resolution Rapid Refresh (HRRR) Model

The HRRR is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model, initialized by 3km grids with 3km radar assimilation. Radar data is assimilated in the HRRR every 15 min over a 1-h period adding further detail to that provided by the hourly data assimilation from the 13km radar-enhanced Rapid Refresh.

Registry of Open Data on AWS

This registry exists to help people discover and share datasets that are available via AWS resources. Learn more about sharing data on AWS.

See all usage examples for datasets listed in this registry.

See datasets from Digital Earth AfricaFacebook Data for GoodNASA Space Act AgreementNIH STRIDESNOAA Big Data ProgramSpace Telescope Science Institute, and Amazon Sustainability Data Initiative.

Textbook Question Answering (TQA)

1,076 textbook lessons, 26,260 questions, 6229 images

Documentation: allenai.org/data/tqa

Download

Harmonized Cancer Datasets: Genomic Data Commons Data Portal

The GDC Data Portal is a robust data-driven platform that allows cancer
researchers and bioinformaticians to search and download cancer data for analysis.

Genomic Data Commons Data Portal
Genomic Data Commons Data Portal

The Cancer Genome Atlas

The Cancer Genome Atlas (TCGA), a collaboration between the National Cancer Institute (NCI) and National Human Genome Research Institute (NHGRI), aims to generate comprehensive, multi-dimensional maps of the key genomic changes in major types and subtypes of cancer.

AWS CLI Access (No AWS account required)

aws s3 ls s3://tcga-2-open/ --no-sign-request

Therapeutically Applicable Research to Generate Effective Treatments (TARGET)

The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) program applies a comprehensive genomic approach to determine molecular changes that drive childhood cancers. The goal of the program is to use data to guide the development of effective, less toxic therapies. TARGET is organized into a collaborative network of disease-specific project teams.  TARGET projects provide comprehensive molecular characterization to determine the genetic changes that drive the initiation and progression of childhood cancers. The dataset contains open Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-Seq Isoform Expression Quantification, miRNA-Seq miRNA Expression Quantification data from Genomic Data Commons (GDC), and open data from GDC Legacy Archive. Access it here.

Genome Aggregation Database (gnomAD)

The Genome Aggregation Database (gnomAD) is a resource developed by an international coalition of investigators that aggregates and harmonizes both exome and genome data from a wide range of large-scale human sequencing projects. The summary data provided here are released for the benefit of the wider scientific community without restriction on use. Downloads

SQuAD (Stanford Question Answering Dataset)

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. Access it here.

PubMed Diabetes Dataset

The Pubmed Diabetes dataset consists of 19717 scientific publications from PubMed database pertaining to diabetes classified into one of three classes. The citation network consists of 44338 links. Each publication in the dataset is described by a TF/IDF weighted word vector from a dictionary which consists of 500 unique words. The README file in the dataset provides more details.

Download Link

Drug-Target Interaction Dataset

This dataset contains interactions between drugs and targets collected from DrugBank, KEGG Drug, DCDB, and Matador. It was originally collected by Perlman et al. It contains 315 drugs, 250 targets, 1,306 drug-target interactions, 5 types of drug-drug similarities, and 3 types of target-target similarities. Drug-drug similarities include Chemical-based, Ligand-based, Expression-based, Side-effect-based, and Annotation-based similarities. Target-target similarities include Sequence-based, Protein-protein interaction network-based, and Gene Ontology-based similarities. The original task on the dataset is to predict new interactions between drugs and targets based on different types of similarities in the network. Download link

Pharmacogenomics Datasets

PharmGKB data and knowledge is available as downloads. It is often critical to check with their curators at feedback@pharmgkb.org before embarking on a large project using these data, to be sure that the files and data they make available are being interpreted correctly. PharmGKB generally does NOT need to be a co-author on such analyses; They just want to make sure that there is a correct understanding of our data before lots of resources are spent.

Pancreatic Cancer Organoid Profiling

The dataset contains open RNA-Seq Gene Expression Quantification data and controlled WGS/WXS/RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic Mutation, and RNA-Seq Splice Junction Quantification. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://gdc-organoid-pancreatic-phs001611-2-open/ --no-sign-request

Africa Soil Information Service (AfSIS) Soil Chemistry

This dataset contains soil infrared spectral data and paired soil property reference measurements for georeferenced soil samples that were collected through the Africa Soil Information Service (AfSIS) project, which lasted from 2009 through 2018. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://afsis/ --no-sign-request

Dataset for Affective States in E-Environments

DAiSEE is the first multi-label video classification dataset comprising of 9068 video snippets captured from 112 users for recognizing the user affective states of boredom, confusion, engagement, and frustration “in the wild”. The dataset has four levels of labels namely – very low, low, high, and very high for each of the affective states, which are crowd annotated and correlated with a gold standard annotation created using a team of expert psychologists. Download it here.

NatureServe Explorer Dataset

NatureServe Explorer provides conservation status, taxonomy, distribution, and life history information for more than 95,000 plants and animals in the United States and Canada, and more than 10,000 vegetation communities and ecological systems in the Western Hemisphere.

The data available through NatureServe Explorer represents data managed in the NatureServe Central Databases. These databases are dynamic, being continually enhanced and refined through the input of hundreds of natural heritage program scientists and other collaborators. NatureServe Explorer is updated from these central databases to reflect information from new field surveys, the latest taxonomic treatments and other scientific publications, and new conservation status assessments. Explore Data here

Flight Records in the US

Airline On-Time Performance and Causes of Flight Delays – On_Time Data.

This database contains scheduled and actual departure and arrival times, reason of delay. reported by certified U.S. air carriers that account for at least one percent of domestic scheduled passenger revenues. The data is collected by the Office of Airline Information, Bureau of Transportation Statistics (BTS).

FlightAware.com has data but you need to pay for a full dataset.

The anyflights package supplies a set of functions to generate air travel data (and data packages!) similar to nycflights13. With a user-defined year and airport, the anyflights function will grab data on:

  • flights: all flights that departed a given airport in a given year and month
  • weather: hourly meterological data for a given airport in a given year and month
  • airports: airport names, FAA codes, and locations
  • airlines: translation between two letter carrier (airline) codes and names
  • planes: construction information about each plane found in flights

Airline On-Time Statistics and Delay Causes

The U.S. Department of Transportation’s (DOT) Bureau of Transportation Statistics (BTS) tracks the on-time performance of domestic flights operated by large air carriers. Summary information on the number of on-time, delayed, canceled and diverted flights appears in DOT’s monthly Air Travel Consumer Report, published about 30 days after the month’s end, as well as in summary tables posted on this website. BTS began collecting details on the causes of flight delays in June 2003. Summary statistics and raw data are made available to the public at the time the Air Travel Consumer Report is released. Access it here

Worldwide flight data

Open flights: As of January 2017, the OpenFlights Airports Database contains over 10,000 airports, train stations and ferry terminals spanning the globe

Download: airports.dat (Airports only, high quality)

Download: airports-extended.dat (Airports, train stations and ferry terminals, including user contributions)

Bureau of Transportation:

Flightera.net seems to have a lot of good data for free. It has in-depth data on flights and doesn’t seem limited by date. I can’t speak on the validity of the data though.

flightradar24.com has lots of data, also historically, they might be willing to help you get it in a nice format.

 

2019 Crime statistics in the USA

Dataset with arrest in US by race and separate states. Download Excel here

Researchers from IBM, MIT and Harvard Announced The Release Of DARPA “Common Sense AI” Dataset Along With Two Machine Learning Models At ICML 2021

Building machines that can make decisions based on common sense is no easy feat. A machine must be able to do more than merely find patterns in data; it also needs a way of interpreting the intentions and beliefs behind people’s choices.

At the 2021 International Conference on Machine Learning (ICML), Researchers from IBM, MIT, and Harvard University have come together to release a DARPA “Common Sense AI” dataset for benchmarking AI intuition. They are also releasing two machine learning models that represent different approaches to the problem that relies on testing techniques psychologists use to study infants’ behavior to accelerate the development of AI exhibiting common sense.

Source – Summary – Paper – IBM Blog

100 million protein structures Dataset by DeepMind

DeepMind creates ‘transformative’ map of human proteins drawn by AI. By the end of the year, DeepMind hopes to release predictions for 100 million protein structures, a dataset that will be “transformative for our understanding of how life works,

Here’s a good article about this topic

Google Dataset Search

Google Dataset Search

Malware traffic dataset

Comprises 1914081 records created from all malware traffic analysis .net PCAP files, from 2013 to 2021. The logs are generated using Suricata and Zeek.

Originator: ali_alwashali

Fastest routes on land (and sometimes, boat) between all 990 pairs of European capitals

Las rutas más rápidas en tierra (y, a veces, en barco) entre los 990 pares de capitales europeas

Les itinéraires les plus rapides sur terre (et parfois en bateau) entre les 990 paires de capitales européennes

Source: Reddit

From the author: I started with data on roads from naturalearth.com, which also includes some ferry lines. I then calculated the fastest routes (assuming a speed of 90 km/h on roads, and 35 km/h on boat) between each pair of 45 European capitals. The animation visualizes these routes, with brighter lines for roads that are more frequently “traveled”.

In reality these are of course not the most traveled roads, since people don’t go from all capitals to all other capitals in equal measure. But I thought it would be fun to visualize all the possible connections.

The model is also very simple, and does not take into account varying speed limits, road conditions, congestion, border checks and so on. It is just for fun!

In order to keep the file size manageable, the animation only shows every tenth frame.

Is Russia, Turkey or country X really part of Europe? That of course depends on the definition, but it was more fun to include them than to exclude them! The Vatican is however not included since it would just be the same as the Rome routes. And, unfortunately, Nicosia on Cyprus is not included to due an error on my behalf. It should be!

Link to final still image in high resolution on my twitter

 

Pokemon Dataset

  1. Dataset of all 825 Pokemon (this includes Alolan Forms). It would be preferable if there are at least 100 images of each individual Pokemon.

pokedex: This is a Python library slash pile of data containing a whole lot of data scraped from Pokémon games. It’s the primary guts of veekun.

pokeapi.co/about

2) This dataset comprises of more than 800 pokemons belonging up to 8 generations.

Using this dataset have been fun for me. I used it to create a mosaic of pokemons taking image as reference. You can find it here and it’s free to use: Couple Mosaic (powered by Pokemons)

Here is the data type information in the file:

  • Name: Pokemon Name
  • Type: Type of Pokemon like Grass / Fire / Water etc..,.
  • HP: Hit Points
  • Attack: Attack Points
  • Defense: Defence Points
  • Sp. Atk: Special Attack Points
  • Sp. Def: Special Defence Points
  • Speed: Speed Points
  • Total: Total Points
  • url: Pokemon web-page
  • icon: Pokemon Image

Data File: Pokemon-Data.csv

30×30 m Worldwide High-Resolution Population and Demographics Data

ETL pipeline for Facebook’s research project to provide detailed large-scale demographics data. It’s broken down in roughly 30×30 m grid cells and provides info on groups by age and gender.

Population Density Overview

Data Source and API for access

Article about Dataset at Medium

Gridded global datasets for Gross Domestic Product and Human Development Index over 1990–2015

Rasterized GDP dataset – basically a heat map of global economic activity.

Gap-filled multiannual datasets in gridded form for Gross Domestic Product (GDP) and Human Development Index (HDI)

Data source here:

Decrease in worldwide infant mortality from 1950 to 2020

Post image

Data Sources: United Nations, CIA World Factbook, IndexMundi.

Data Collectors

Data Unblockers

Countries of the world sorted by those that have warmed the most in the last 10 years, showing temperatures from 1890 to 2020

r/dataisbeautiful - Countries of the world sorted by those that have warmed the most in the last 10 years, showing temperatures from 1890 to 2020 [OC]

Data source: Gistemp temperature data

The GISS Surface Temperature Analysis ver. 4 (GISTEMP v4) is an estimate of global surface temperature change. Graphs and tables are updated around the middle of every month using current data files from NOAA GHCN v4 (meteorological stations) and ERSST v5 (ocean areas), combined as described in our publications Hansen et al. (2010) and Lenssen et al. (2019).

Climate change concern vs personal spend to reduce climate change

r/dataisbeautiful - [OC] Climate change concern vs personal spend to reduce climate change

Data Source: Competitive Enterprise Institute (PDF)

Less than 20 firms produce over a third of all carbon emissions

The Illusion of Choice in Consumer Brands

The Illusion of Choice in Consumer Brands

Buying a chocolate bar? There are seemingly hundreds to choose from, but its just the illusion of choice. They pretty much all come from Mars, Nestlé, or Mondelēz (which owns Cadbury).

Source: Visual Capitalist

Yearly Software Sales on PlayStation Consoles since 1994

r/dataisbeautiful - [OC] Yearly Software Sales on PlayStation Consoles since 1994

Some context for these numbers :

  • PS4 holds the record for being the console to have sold the most games in video game history (> 1.622B units)
    • Previous record holder was PS2 at 1.537B games sold
  • PS4 holds the record for having sold the most games in a single year (> 300M units in FY20)
  • FY20 marks the biggest yearly software sales in PlayStation ecosystem with more than 338M units
  • Since PS5 release, Sony starts combining PS4/PS5 software sales
  • In FY12, Sony combined PS2/PS3 and PSP/VITA software sales
  • Sony stopped disclosing software sales in FY13/14

Yearly Hardware Sales of PlayStation Consoles since 1994

r/dataisbeautiful - [OC] Yearly Hardware Sales of PlayStation Consoles since 1994

Sony combined PS2/PS3 hardware sales in FY12 and combined PSP/VITA sales in FY12/13/14

Cybertruck vs F150 Lightning pre-orders, by time since debut

r/dataisbeautiful - [OC] Cybertruck vs F150 Lightning pre-orders, by time since debut

Source: Ford exec tweeting about preorder numbers this week

Top 100 Most Populous City Proper in the world

r/dataisbeautiful - (Fixed once again) Top 100 Most Populous City Proper in the world. [OC]

The City with 32 million is Chongqing, Shan is Shanghai, Beijin is Beijing, and Guangzho is Guangzhou

Tax data for different countries

Dataset is here

What do Europeans feel most attached to – their region, their country, or Europe?

r/dataisbeautiful - [OC] What do Europeans feel most attached to - their region, their country, or Europe?

Data source: Builds on data from the 2021 European Quality of Government Index. You can read more about the survey and download the data here

Cost of 1gb mobile data in every country

r/dataisbeautiful - Cost of 1gb mobile data in every country

r/dataisbeautiful - Cost of 1gb mobile data in every country

Dataset: Visual Capitalist

Frequency of all digrams in 18 languages, diacritics included 

r/dataisbeautiful - Langues germaniques

Dataset (according to author): Dictionaries are scattered on the internet and had to be borrowed from several sources: the Scrabble3d project, and Linux spellcheck dictionaries. The data can be found in the folder “Avec_diacritiques”.

Criteria for choosing a dictionary:
– No proper nouns
– “Official” source if available
– Inclusion of inflected forms
– Among two lists, the largest was fancied
– No or very rare abbreviations if possible- but hard to detect in unknown languages and across hundreds of thousands of words.

Mapped: The World’s Nuclear Reactor Landscape

r/dataisbeautiful - Mapped: The World’s Nuclear Reactor Landscape

Dataset: Visual Capitalist

Database of 999 chemicals based on liver-specific carcinogenicity

The author found this dataset in a more accessible format upon searching for the keyword “CDPB” (Carcinogenic Potency Database) in the National Library of Medicine Catalog. Check out this parent website for the data source and dataset description. The dataset referenced in OP’s post concerns liver specific carcinogens, which are marked by the “liv” keyword as described in the dataset description’s Tissue Codes section.

SMS Spam Collection Data Set

DownloadData FolderData Set Description

The SMS Spam Collection is a public set of SMS labeled messages that have been collected for mobile phone spam research

Open Datasets for Autonomous Driving

A2D2 DatasetApolloScape Dataset Argoverse Dataset Berkeley DeepDrive Dataset

CityScapes DatasetComma2k19 DatasetGoogle-Landmarks Dataset

KITTI Vision Benchmark SuiteLeddarTech PixSet Dataset Level 5 Open DatanuScenes Dataset

Oxford Radar RobotCar DatasetPandaSet Udacity Self Driving Car Dataset Waymo Open Dataset

Open Dataset people are looking for [Help if you can]

  1. Looking for Dataset on the outcomes of abstinence-only sex education.
  2. Funny Datasets for School Data Science Project [1, 2, 3, 4, 5]
  3. Need a dataset for English practicing chatbot. [1, 2 ]
  4. Creating a dataset for plant disease recognition [1, 2 ]
  5. Central Bank Speeches Dataset (Text data from 1997 to 2020 from 118 institutions) [1, 2]
  6. Cat Meow Classification dataset [1, 2]
  7. Looking for Raw Data: Camping / Outdoors Travel in United States trends, etc [1, 2 ]
  8. Looking for Data set of horse race results / lottery results any results related to gambling [1, 2, 3]
  9. Looking for Football (Soccer) Penalties Dataset [1, 2]
  10. Looking for public datasets on baseball [1, 2, 3]
  11. Looking for Datasets on edge computing for AI bandwidth usage, latency, memory, CPU/GPU resource usage? [1 ,2 ]
  12. Data set of people who died by suicide [1, 2 ]
  13. Supreme Court dataset with opinion text? [1, 2, 3, 4, https://storage.googleapis.com/scotus-db/scotus-db.db5]
  14. Dataset of employee attrition or turnover rate? [1, 2]
  15. Is there a Dataset for homophobic tweets? [1 ,2, 3, 4, ]
  16. Looking for a Machine condition Monitoring Dataset [1,2]
  17. Where to find data for credit risk analysis? [1, 2]
  18. Datasets on homicides anywhere in the world [1, 2]
  19. Looking for a dataset containing coronavirus self-test (if this is a thing globally) pictures for ML use
  20. Is there any transportation dataset with daily frequency? [1, 2]
  21. A Dataset of film Locations [1, 2 ]
  22. Looking for a classification dataset [1, 2, 3, 4, 5]
  23. Where can I search for macroeconomics data? [1, 2, 3, 4, 5, 6, 7]
  24. Looking for Beam alignment 5G vehicular networks dataset
  25. Looking for tidy dataset for multivariate analysis (PCA, FA, canonical correlations, clustering)
  26. Indian all types of Fuel location datasets [1, 2,]
  27. Spotify Playlists Dataset [1, 2]
  28. World News Headline Dataset. With Sentiment Scores. Free download in JSON format. Updated often. [1, 2]
  29. Are there any free open source recipe datasets for commercial use [1, 2, 3, 4, 5]
  30. Curated social network datasets with summary statistics and background info
  31. Looking for textile crop disease datasets such as jute, flax, hemp
  32. Shopify App Store and Chrome Webstore Datasets
  33. Looking for dataset for university chatbot
  34. Collecting real life (dirty/ugly) datasets for data analysis
  35. In Need of Food Additive/Ingredient Definition Database
  36. Recent smart phone sensor Dataset – Android
  37. Cracked Mobile Screen Image Dataset for Detection
  38. Looking for Chiller fault data in a chiller plant
  39. Looking for dataset that contains the genetic sequences of native plasmids?
  40. Looking for a dataset containing fetus size measurements at various gestational ages.
  41. Looking for datasets about mental health since 2021
  42. Do you know where to find a dataset with Graphical User Interfaces defects of web applications? [1, 2, 3 ]
  43. Looking for most popular accounts on social medias like Twitter, Tik Tok, instagram, [1, 2, 3]
  44. GPS dataset of grocery stores
  45. What is the easiest way to bulk download all of the data from this epidemiology website? (~20,000 files)
  46. Looking for Dataset on Percentage of death by US state and Canadian province grouped by cause of death?
  47. Looking for Social engineering attack dataset in social media
  48. Steam Store Games (Clean dataset) by Nik Davis
  49. Dataset that lists all US major hospitals by county
  50. Another Data that list all US major hospitals by county
  51. Looking for open source data relating privacy behavior or related marketing sets about the trustworthiness of responders?
  52. Looking for a dataset that tracks median household income by country and year
  53. Dataset on the number of specific surgical procedures performed in the US (yearly)
  54. Looking for a dataset from reddit or twitter on top posts or tweets related to crypto currency
  55. Looking for Image and flora Dataset of All Known Plants, Trees and Shrubs
  56. US total fertility rates data one the state level
  57. Dataset of Net Worth of *World* Politicians
  58. Looking for water wells and borehole datasets
  59. Looking for Crop growth conditions dataset
  60. Dataset for translate machine JA-EG
  61. Looking for Electronic Health Record (EHR) record prices
  62. Looking for tax data for different countries
  63. Musicians Birthday Datasets and Associated groups
  64. Searching for dataset related to car dealerships [1]
  65. Looking for Credit Score Approval dataset
  66. Cyberbullying Dataset by demographics
  67. Datasets on financial trends for minors
  68. Data where I can find out about reading habits? [1, 2]
  69. Data sets for global technology adoption rates
  70. Looking for any and all cat / feline cancer datasets, for both detection and treatment
  71. ITSM dictionary/taxonomy datasets for topic modeling purposes
  72. Multistage Reliability Dataset
  73. Looking for dataset of ingredients for food[1]
  74. Looking for datasets with responses to psychological questionnaires[1,2,3]
  75. Data source for OEM automotive parts
  76. Looking for dataset about gene regulation
  77. Customer Segmentation Datasets (For LTV Models)
  78. Automobile dataset, years of ownership and repairs
  79. Historic Housing Prices Dataset for Individual Houses
  80. Looking for the data for all the tokens on the Uniswap graph
  81. Job applications emails datasets, either rejection, applications or interviews
  82. E-learning datasets for impact on e learning on school/university students
  83. Food delivery dataset (Uber Eats, Just Eat, …)
  84. Data Sets for NFL Quarterbacks since 1995
  85. Medicare Beneficiary Population Data
  86. Covid 19 infected Cancer Patients datasets
  87. Looking for  EV charging behavior dataset
  88. State park budget or expansionary spending dataset
  89.  Autonomous car driving deaths dataset
  90. FMCG Spending habits over the pandemic
  91. Looking for a Question Type Classification dataset
  92. 20 years of Manufacturer/Retail price of Men’s footwear
  93. Dataset of Global Technology Adoption Rates
  94. Looking For Real Meeting Transcripts Dataset
  95. Dataset For A Large Archive Of Lyrics  [1,2,3]
  96. Audio dataset with swearing words
  97. A global, georeferenced event dataset on electoral violence with lethal outcomes from 1989 to 2017. [1,]
  98. Looking for Jaundice Dataset for ML model
  99. Looking for social engineering attack detection dataset?
  100. Wound image datasets to train ML model [1]
  101. Seeking for resume and job post dataset
  102. Labelled dataset (sets of images or videos) of human emotions [1,2]
  103. Dataset of specialized phone call transcripts
  104. Looking for Emergency Response Plan Dataset for family Homes, condo buildings and Companies
  105. Looking for Birthday wishes datasets
  106. Desperately in need of national data for real estate [1,2,]
  107. NFL playoffs games stadium attendance dataset
  108. Datasets with original publication dates of novels [1,2]
  109. Annotated Documents with Images Data Dump
  110. Looking for  dataset for “Face Presentation Attack Detection”
  111. Electric vehicle range & performance dataset [1, 2]
  112. Dataset or API with valid postal codes for US, Mexico, and Canada with country, state/province, and city/town [1, 2, 3, 4, 5, 6]
  113. Looking for Data sources regarding Online courses dropout rate, preferably by countries [1,2 ]
  114. Are there dataset for language learning [1, 2]
  115. Corporate Real Estate Data [1,2, 3]
  116. Looking for simple clinical trials datasets [1, 2]
  117. CO2 Emissions By Aircraft (or Aircraft Type) – Climate Analysis Dataset [1,2, 3, 4]
  118. Player Session/playtime dataset from games [1,2]
  119. Data sets that support Data Science (Technology, AI etc) being beneficial to sustainability [1,2]
  120. Datasets of a grocery store [1,2]
  121. Looking for mri breast cancer annotation datasets [1,2]
  122. Looking for free exportable data sets of companies by industry [1,2]
  123. Datasets on Coffee Production/Consumption [1,2]
  124. Video gaming industry datasets – release year, genre, games, titles, global data  [1,2]
  125. Looking for mobile speaker recognition dataset [1,2]
  126. Public DMV vehicle registration data [1,2]
  127. Looking for historical news articles based on industry sector [1,2]
  128. Looking for Historical state wide Divorce dataset [1,2]
  129. Public Big Datasets – with In-Database Analytics [1,2]
  130. Dataset for detecting Apple products (object detection) [1,2]
  131. Help needed to get the American Hospital Association (AHA) datasets (AHA Annual Survey, AHA Financial Database, and AHA IT Survey datasets)  [1, 2]
  132. Looking for help Getting College Football Betting Data [1,2]
  133. 2012-2020 US presidential election results by state/city dataset [1,2, 3]
  134. Looking for datasets of models and images captured using iphone’s LIDAR? [1,2]
  135. Finding Datasets to Age Texts (Newspapers, Books, Anything works) [1, 2, 3]
  136. Looking for cost of living index of some type for US [1,2]
  137. Looking for dataset that recorded historical NFT prices and their price increases, as well as timestamps. [1,2]
  138. Looking for datasets on park boundaries across the country [1, 2, 3]
  139. Looking for medical multimodal datasets [1, 2, 3]
  140. Looking for Scraped Parler Data [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
  141. Looking for Silicon Wafer Demand dataset [1, 2]
  142. Looking for a dataset with the values [Gender – Weight – Height – Health] [1, 2]
  143. Exam questions (mcqs and short answer) datasets? [1, 2]
  144. Canada Botanical Plants API/Database [1, 2, 3]
  145. Looking for a geospatial dataset of birds Migration path [1, 2, 3]
  146. WhatsApp messages dataset/archives [1, 2]
  147.  Dataset of GOOD probiotic microorganisms in the HUMAN gut [1, 2]
  148. Twitter competition to reduce bias in its image cropping [1,2]
  149. Dataset: US overseas military deployments, 1950–2020 [1,2]
  150. Dataset on human clicking on desktop [1,2]
  151. Covid-19 Cough Audio Classification Dataset [1, 2]
  152. 12,000+ known superconductors database [1, 2, 3]
  153. Looking for good dataset related to cyber security for prediction [1, 2]
  154. Where can I find face datasets to classify whether it is a real person or a picture of that person. For authentication purposes? [1,2]
  155. DataSet of Tokyo 2020 (2021) Olympics ( details about the Athletes, the countries they representing, details about events, coaches, genders participating in each event, etc.) [1, 2]
  156. What is your workflow for budget compute on datasets larger than 100GB? [1, 2, 3]
  157. Looking for a dataset that contains information about cryptocurrencies. [1, 2

  158. Looking for a depression dataset [1,2, 3]

  159. Looking for chocolate consumer demographic data [1,2, 3]
  160. Looking for thorough dataset of housing price/tax history [1, 2, 3]
  161. Wallstreetbets data scraping from 01/01/2020 to 01/06/2021 [1, 2]
  162. Retinal Disease Classification Dataset [1, 2]
  163. 400,000 years of CO2 and global temperature data [1, 2, 3]
  164. Looking for datasets on neurodegenerative diseases [1, 2, 3]
  165. Dataset for Job Interviews (either Phone, Online, or Physical) [1,2 ,3]
  166. Firm Cyber Breach Dataset with Firm Identifiers [1, 2, 3]
  167. Wondering how Stock market and Crypto website get the Data from [1, 2, 3, 4, 5]
  168. Looking for a dataset with US tourist injuries, attacks, and/or fatalities when traveling abroad [1, 2, 3]
  169. Looking for Wildfires Database for all countries by year and month? The quantity of wildfires happening, the acreage, things like that, etc.. [1, 2, 3, ]
  170. Looking for a pill vs fake pill image dataset [1, 2, 3, 4, 5, 6, 7]

Cars for sale in Germany from 2011 to 2021

Dataset obtained scraping AutoScout. In the file, you will find features describing 46405 vehicles: mileage, make, model, fuel, gear, offer type, price, horse power, registration year.

Dataset scraped from AutoScout24 with information about new and used cars.

Percentage of female students in higher education by subject area

r/dataisbeautiful - [OC] Percentage of female students in higher education by subject area

The data was obtained from the UK government website here , so unfortunately there are some things I’m unaware of regarding data and methodology.

All the passes: A visualization of ~1 million passes from 890 matches played in major football/soccer leagues/cups

  •  Champion League 1999
  • FA Women’s Super League 2018
  • FIFA World Cup 2018, La Liga 2004 – 2020
  • NWSL 2018
  • Premier League 2003 – 2004
  • Women’s World Cup 2019

Data Source: StatsBomb

Global “Urbanity” Dataset (using population mosaics, nighttime lights, & road networks

In this project, the authors  have designed a spatial model which is able to classify urbanity levels globally and with high granularity. As the target geographic support for our model we selected the quadkey grid in level 15, which has cells of approximately 1x1km at the equator.

Dataset:  Here 

Percentage of students with disabilities in higher education by subject area

r/dataisbeautiful - [OC] Percentage of students with disabilities in higher education by subject area

The author obtained the data from the UK Government website, so unfortunately don’t know the methodology or how they collected the data etc.

The comparison to the general public is  a great idea – according to the Government site, 6% of children, 16% of working-age adults and 45% of Pension-age adults are disabled.

Dataset: here

Arrests for Hate Crimes in NYC by Category, 2017-2020

r/dataisbeautiful - [OC] Arrests for Hate Crimes in NYC by Category, 2017-2020

The Most Successful U.S. Sports Franchises

r/dataisbeautiful - [OC] The Most Successful U.S. Sports Franchises

Data source: sports-reference.com/

Adult cognitive skills (PIAAC literacy and numeracy) by Percentile and by country

According to the author  , this animation depicts adult cognitive skills, as measured by the PIAAC study by OECD. Here, the numeracy and literacy skills have been combined into one. Each frame of the animation shows the xth percentile skill level of each individual country. Thus, you can see which countries have the highest and lowest scores among their bottom performers, median performers, and top performers. So for example, you can see that when the bottom 1st percentile of each country is ranked, Japan is at the top, Russia is second, etc. Looking at the 50th percentile (median) of each country, Japan is top, then Finland, etc.

Programme for the International Assessment of Adult Competencies (PIAAC) is a study by OECD to measure measured literacy, numeracy, and “problem-solving in technology-rich environments” skills for people ages 16 and up. For those of you who are familiar with the school-age children PISA study, this is essentially an adult version of it.

Dataset: PIAAC 

G7 Corporate Tax rate 1980 – 2020

r/dataisbeautiful - G7 Corporate Tax rate 1980 - 2020 [OC]

Dataset: Tax Foundation

 Euro 2020 (played in 2021) Group Stage Predictions Based of a Bayesian Linear Item Response Model

r/dataisbeautiful - [OC] Euro 2020 (played in 2021) Group Stage Predictions Based of a Bayesian Linear Item Response Model

Data Source: UEFA qualifying match data

The model was built in Stan and was inspired by Andrew Gelman’s World Cup model shown here. These plots show posterior probabilities that the team on the y axis will score more goals than the team on the x axis. There is some redundancy of information here (because if I know P(England beats Scotland) then I know P(Scotland beats England) )

Data

Source: Italian National Institute of Statistics (Istituto Nazionale di Statistica)

The 15 most shared musicians on Reddit

r/dataisbeautiful - [OC] The 15 most shared musicians on Reddit

Data source: The authors made a dataset of YouTube and Spotify shares on Reddit. More info available here

Annual Stream for the top artist on Spotify (billions)

Post image

Music Streaming market share 2021-2022

r/dataisbeautiful - [OC] Spotify and Apple Music together account for over 50% of the music streaming market (by subscribers)

Spam vs. Legitimate Email, Average Global Emails per Day

r/dataisbeautiful - Spam vs. Legitimate Email, Average Global Emails per Day [OC]

Data Source: Here. The author  computed the average per day over the June 3 – June 9, 2021 period.

spam vs legitimate email 2021

Falling Fertility, 1800–2016

Data source: Here (go to the “Babies per woman,” “Income,” and “Population” links on that page).

Europe Covid-19 waves

r/dataisbeautiful - Europe Covid-19 waves [OC]

Data Source: Here

Who is going to win EURO 2020? Predicted probabilities pooled together from 18 sources

r/dataisbeautiful - Who is going to win EURO 2020? Predicted probabilities pooled together from 18 sources [OC]

Data source: Here

Population Density of Canada 2020

r/dataisbeautiful - [OC] Population Density of Canada 2020

DataSet:  Gathered from worldpop.org/project

The greater the length of each spike correlates to greater population density.

The portion of a country’s population that is fully vaccinated for COVID (as of June 2021) scales with GDP per capita.

r/dataisbeautiful - [OC] The portion of a country's population that is fully vaccinated for COVID (as of June 2021) scales with GDP per capita.

Dataset of Chemical reaction equations

1-  chemequations.com/en/

2- Kaggle chemistry section

3- Reaction datasets 

4- Chemistry datasets

5- BiomedCentral 

Maths datasets

1111 2222 3333 Equation Learning 

Datasets for Stata Structural Equation Modeling

Mathematics Dataset

SQL Queries Dataset 

SEDE (Stack Exchange Data Explorer) is a dataset comprised of 12,023 complex and diverse SQL queries and their natural language titles and descriptions, written by real users of the Stack Exchange Data Explorer out of a natural interaction. These pairs contain a variety of real-world challenges which were rarely reflected so far in any other semantic parsing dataset. Access it here

Countries of the world, ranked by population, with the 100 largest cities in the world marked

According to the author:

Each map size is proportional to population, so China takes up about 18-19% of the map space.

Countries with very far-flung territories, such as France (or the USA) will have their maps shrunk to fit all territories. So it is the size of the map rectangle that is proportional to population, not the colored area. Made in R, using data from naturalearthdata.com. Maps drawn with the tmap package, and placed in the image with the gridExtra package. Map colors from the wesanderson package.

Data source: The Economist

What businesses in different countries search for when they look for a marketing agency – “creative” or “SEO”?

r/dataisbeautiful - What businesses in different countries search for when they look for a marketing agency - "creative" or "SEO"? [OC]

Data source: Google Trends

More maps, charts and written analysis on this topic here

Is the economic gap between new and old EU countries closing?

Post image

Data source:  Eurostat

Interactive version so you can click on those circles here

Reddit r/wallstreetbets posts and comments in real-time

  • Posts

  • Comments

  • Beneath adds some useful features for shared data, like the ability to run SQL queries, sync changes in real-time, a Python integration, and monitoring. The monitoring is really useful as it lets you check out the write activity of the scraper (no surprise, WSB is most active when markets are open
  • The scraper (which uses Async PRAW) is open source here

Global NO2 pollution data visualization June 2021

Data Source: SILAM

Shopify App Store Report: 2021

Data source: Marketplace Apps

The Chrome Webstore Report: 2021

Data source: Marketplace Apps

Percentage of Adults with HIV/AIDS in Africa

r/dataisbeautiful - [OC] Percentage of Adults with HIV/AIDS in Africa

Dataset:  All the countries through the UN AIDS organization 

Recorded CDC deaths (2014 – June 16, 2021) from Symptoms, signs and abnormal clinical and laboratory findings, not elsewhere classified (R00-R99)

r/dataisbeautiful - [OC] Recorded CDC deaths (2014 - June 16, 2021) from Symptoms, signs and abnormal clinical and laboratory findings, not elsewhere classified (R00-R99)

Data Source: combined CDC weekly death counts 2014 – 2019 and CDC weekly death counts 2020-2021

What are the long term gains on cryptocurrencies?

r/dataisbeautiful - What are the long term gains on cryptocurrencies? [OC]

Data Sources: investing.com and coingecko.com

The chart shows the average daily gain in $ if $100 were invested at a date on x-axis. Total gain was divided by the number of days between the day of investing and June 13, 2021. Gains were calculated on average 30-day prices.

Time range: from March 28, 2013, till June 13, 2021

Life Expectancy and Death Probability by Age and Gender

r/dataisbeautiful - [OC] Life Expectancy and Death Probability by Age and Gender

Data source: Here

Daily Coronavirus cases in Canada vs % of Population Vaccinated

r/dataisbeautiful - Daily Coronavirus cases in Canada vs % of Population Vaccinated [OC]

Data Source: Cases Vaccines

Google Playstore Apps with 2.3million app data on Kaggle

Google Playstore dataset is now available with double the data (2.3 Million) android application data and a new attribute stating the scraped date time in Kaggle.

Dataset: Get it here or here

African languages dataset

We have 3000 tribes or more in Africa and in that 3000 we have sub tribes.

1 Introduction to African Languages (Harvard)

2- Languages of the world at Ethnologue

3- Britannica: Nilo-Saharan Laguages

4- Britannica: Khoisan Languages

Daily Temperature of Major Cities Dataset

Daily average temperature values recorded in major cities of the world.

 The dataset is available as separate txt files for each city here. The data is available for research and non-commercial purposes only

 Do stricter gun laws reduce firearms homicides?

r/dataisbeautiful - [OC] Do stricter gun laws reduce firearms homicides?

Data Sources: Guns to CarryEFSGVCDC

According to the author: Looking at non-suicide firearms deaths by state (2019), and then grouping by the Guns to Carry rating (1-5 stars), it seems that stricter gun laws are correlated with fewer firearms homicides. Guns to Carry rates states based on “Gun friendliness” with 1 star being least friendly (California, for example), and 5 stars being most friendly (Wyoming, for example). The ratings aren’t perfect but they include considerations like: Permit required, Registration, Open carry, and Background checks to come up with a rating.

The numbers at the bottom are the average non-suicide deaths calculated within the rating group. Each bar shows the number for the individual state.

Interesting that DC is through the roof despite having strict laws. On the flip side, Maine is very friendly towards gun owners and has a very low homicide rate, despite having the highest ratio of suicides to homicides.

Obviously, lots of things to consider and this is merely a correlation at a basic level. This is a topic that interested me so I figured I’d share my findings. Not attempting to make a policy statement or anything.

In 1996 the Australia Government implemented stricter gun control and restrictions. The numbers don’t lie and proves it worked.

r/dataisbeautiful - When Kids that lived with shooting drills in school are old enough to vote, we will see a big changes.

Every mass shooting in the US visualised from 2014-2022

Every mass shooting in the US visualised from 2014-2022
Every mass shooting in the US visualised from 2014-2022

Relative frequency of words in economics textbooks vs their frequency in mainstream English (the Google Books corpus)

r/dataisbeautiful - [OC] Relative frequency of words in economics textbooks vs their frequency in mainstream English (the Google Books corpus)

Author:

Data Source: Data for word frequency in the Google corpus is from the 2019 Ngram dataset. For details about how to work with this data, see Working With Google Ngrams: A Data-Wrangling Tale.

Data for word frequency in econ textbooks was compiled by myself by scraping words from 43 undergraduate economics textbooks. For details see Deconstructing Econospeak.

Hours per day spent on mobile devices by US adults

r/dataisbeautiful - [OC] Hours per day spent on mobile devices by US adults

Author: nava_7777

Data Source: from eMarketer, as quoted byJon Erlichman

Purpose according to the author: raw textual numbers (like in the original tweet) are hard to compare, particularly the acceleration or deceleration of a trend. Did for myself, but maybe is useful to somebody.

Environmental Impact of Coffee Brewing Methods

r/dataisbeautiful - [OC] Environmental Impact of Coffee Brewing Methods

Author: Coffee_Medley

Data Source: 1 2 3

More according to the author:

  • Measurements and calculations of NG and Electricity used to heat four cups of distilled water by Coffee Medley (6/14/2021)

  • Average coffee bag and pod weight by Coffee Medley (6/14/2021)

Murders in major U.S. Cities: 2019 vs. 2020

r/dataisbeautiful - [OC] Murders in major U.S. Cities: 2019 vs. 2020

Author: datacanbeuseful

Data source: NPR

New Harvard Data (Accidentally) Reveal How Lockdowns Crushed the Working Class While Leaving Elites Unscathed

Data source: Harvard

Support for same-sex marriage by religious group

r/dataisbeautiful - Support for same-sex marriage by religious group [OC]

Data source: PEW

More: Summary of religiously (un)affiliated people’s views on homosexuality, broken down into 18 countries

Daily chance of dying for Americans

r/dataisbeautiful - Daily chance of dying for Americans [OC]

Author: NortherSugarLoaf

Data source: SSA Actuarial Data,

Processing: Yearly probability of death is converted to the daily probability and expressed in micromorts. Plotted versus age in years.

Micromort:

According to the author,

A few things to notice: It’s dangerous to be a newborn. The same mortality rates are reached again only in the fifties. However, mortality drops after birth very quickly and the safest age is about ten years old. After experiencing mortality jump in puberty – especially high for boys, mortality increases mostly exponentially with age. Every thirty years of life increase chances of dying about ten times. At 80, chance of dying in a year is about 5.8% for males and 4.3% for females. This mortality difference holds for all ages. The largest disparity is at about twenty three years old when males die at a rate about 2.7 times higher than females.

This data is from before COVID.

Here is the same graph but in linear Y axis scale

Here is the male to female mortality ratio

Mapping Global Carbon Emission Intensity (Dec 2020)

r/dataisbeautiful - [OC] Mapping Global Carbon Emission Intensity (Dec 2020)

Data Source: Copernicus Atmosphere Monitoring Service (CAMS)

Religions with the most Adherents from 1945 – 2010

Data source: Zeev Maoz and Errol A. Henderson. 2013. “The World Religion Dataset, 1945-2010: Logic, Estimates, and Trends.” International Interactions, 39: 265-291.

IPO Returns 2000-2020

IPO Returns 2000-2020

IPO Returns 2000-2020

IPO Returns 2000-2020

 

Data from: iposcoop.com
From the author u/nobjos: The full article on the above analysis can be found here
I have sub r/market_sentiment where I do a comprehensive deep-dive on one investment strategy/topic every week! Some of the author popular articles are
a. Performance of Jim Cramer’s stock picks
b. Performance of buy and sell recommendations made by financial analysts in the last decade
c. Benchmarking performance of Motely fool against SP500
Funko IPO is considered to have the worst first-day return for an IPO in the last two decades.
Out of the top 10 list, only 3 Investment banks had below-average returns.
On average, IPOs did make money for the investor. But the amount is significantly different if you got allocated the IPO at offer price vs you get the IPO at market price.
Baidu.com made a whopping 354% on its listing day. Another interesting observation is 6 out of 10 companies in the list were listed in 200 (just before the dot com crash)

 

Largest publicly-traded airline

Largest publicly-traded airlines

r/dataisbeautiful - [OC] Largest publicly-traded airlines

Total number of streams per artist vs. number of Top 200 hits (Spotify Top 200 since 2017)

r/dataisbeautiful - [OC] Total number of streams per artist vs. number of Top 200 hits (Spotify Top 200 since 2017)

Author: blairfix

Data is from the Spotify Top 200 and covers the period from Jan. 1, 2017 to Jun. 9, 2021. You can download my dataset here.

For every artist that appears in the Top 200, I add up their total streams (for all songs) and the total number of songs in the dataset.

For a commentary on the data, see The Half Life of a Spotify Hit.

Number of Miss Americas by U.S. State

r/dataisbeautiful - [OC] Number of Miss Americas by U.S. State

Data Source: Wikipedia

The World’s Nuclear Warheads

r/dataisbeautiful - [OC] The World's Nuclear Warheads

Author: academiadvice

Data Source: Federation of American Scientists – status-world-nuclear-forces/

Tools: Excel, Datawrapper, coolors.co/

Check out the FAS site for notes and caveats about their estimates. Governments don’t just print this stuff on their websites. These are evidence-based estimates of tightly-guarded national secrets.

Of particular note – Here’s what the FAS says about North Korea: “After six nuclear tests, including two of 10-20 kilotons and one of more than 150 kilotons, we estimate that North Korea might have produced sufficient fissile material for roughly 40-50 warheads. The number of assembled warheads is unknown, but lower. While we estimate North Korea might have a small number of assembled warheads for medium-range missiles, we have not yet seen evidence that it has developed a functioning warhead that can be delivered at ICBM range.”

The population of Las Vegas over time

r/dataisbeautiful - [OC] The population of Las Vegas over time

Data Source: Wikipedia

 The Alpha to Omega of Wikipedia

r/dataisbeautiful - [OC] The Alpha to Omega of Wikipedia

Author: feldesque

Data Source: The wikipediatrend package in R

Code published here

Glacial Inter-glacial cycles over the past 450000 years

Source:  geology.utah.gov/

Global temperature change from 1850-2020

r/dataisbeautiful - Global temperature change from 1850-2020

Worth noting these are largely driven by changes in the amount of solar radiation reaching us due to variations in earth’s orbit

Top Companies Contributing to Open Source – 2011/2021

Source and links

The author used several sources for this video and article. The first, for the video, is GitHub Archive & CodersRank. For the analysis of the OSCI index data, the author used opensourceindex.io

Crime Rates in the US: 1960-2021

r/dataisbeautiful - [OC] Crime Rates in the US: 1960-2021

Data source: Here

Here

2021 is straight projections, must be taken with a grain of salt. However, the assumption of continuous rise of murder rate is not a bad one based on recent news reports, such as: here

In a property crime, a victim’s property is stolen or destroyed, without the use or threat of force against the victim. Property crimes include burglary and theft as well as vandalism and arson.

 

A network visualization of privacy research (83k nodes, 462k edges)

r/dataisbeautiful - [OC] A network visualisation of privacy research (83k nodes, 462k edges)

Author: FvDijk

This image was generated for my research mapping the privacy research field. The visual is a combination of network visualisation and manual adding of the labels.

The data was gathered from Scopus, a high-quality academic publication database, and the visualisation was created with Gephi. The initial dataset held ~120k publications and over 3 million references, from which we selected only the papers and references in the field.

The labels were assigned by manually identifying clusters and two independent raters assigning names from a random sample of publications, with a 94% match between raters.

The scripts used are available on Github:

The full paper can be found on the author’s website:

 

GDP (at purchasing power parity) per capita in international dollars

r/dataisbeautiful - [OC] GDP (at purchasing power parity) per capita in international dollars

Author:  Simaniac

Data source: IMF

Phone Call Anxiety dataset for Millennials and Gen Z

r/dataisbeautiful - Phone Call Anxiety is a real thing for Millennials and Gen Z [OC]

Author: /u/CynicalScyntist

This is a randomized experiment the author  conducted with 450 people on Amazon MTurk. Each person was randomly assigned to one of three writing activities in which they either (a) described their phone, (b) described what they’d do if they received a call from someone they know, or (c) describe what they’d do if they received a call from an unknown number. Pictures of an iPhone with a corresponding call screen were displayed above the text box (blank, “Incoming Call,” or “Unknown”). Participants then rated their anxiety on a 1-4 scale.

Dataset: Here

Source Article

Hate Crime Statistics in New York State 2019-2021

Hate Crime Statistics NYC 2019-2021

Continue reading “What are some good datasets for Data Science and Machine Learning?”

Programming Languages used for Autopilot in Self Driving Cars like Tesla, Audi, BMW, Mercedes Benz, Volvo, Infiniti

Self Driving Cars Programming language - Misra C

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What are Programming Languages used for Autopilot in Self Driving Cars like Tesla, Audi, BMW, Mercedes Benz, Volvo, Infiniti?

Most self-driving cars on the market today use C programming language for their vehicle software. This is because C is a very robust and stable language that can be trusted for mission-critical applications. In addition, C is relatively easy to learn and has a wide range of features that make it well suited for automotive applications. However, there are some drawbacks to using C for self-driving cars. First, it is not a very concise language, so the code can be quite long and difficult to read. Second, C does not have built-in support for object-oriented programming, which is becoming increasingly important in the world of autonomous vehicles. As a result, many carmakers are starting to explore other languages for their autopilot systems, such as Java and Python.

Below are Top Cars with AutoPilot features for 2022:


  • Tesla (Model 3, Y, S & X)
  • GM – (Cadillac CT6, Cadillac Escalade, Chevy Bolt, Hummer EV)
  • Audi (A6, A8)
  • BMW (X5, 3 Series )
  • Ford / Lincoln (Mustang Mach-E, Ford F-150)
  • Kia / Hyundai (Telluride, Palisade, Sonata)
  • Mercedes Benz (E-Class, S-Class)
  • Volvo (XC90, XC60, XC40)
  • Nissan (Rogue, Leaf, etc.)
  • Infiniti (QX50)

Whilst it’s technically correct that Tesla most likely uses the C programming language for their vehicle software, it’s worth clarifying that the actual language would be MISRA C which has several constraints on the language to provide better control over its features .

Low-level communication requires using C. Especially for embedded systems, sensors and IoT software.

To develop software for supporting devices in the system C++ is the best option.

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However, Python is the language to enter the game when it comes to using AI.

WHY DOES ELON HATE LIDAR?

There are a few reasons for that:

  1. Lidar uses light to measure distances. But we know you can measure distances using a “stereo pair” of regular cameras with (by 2020 standards) very simple software processing.
  2. Lidar requires mechanical scanning of the scene – implying moving parts that will make it less reliable.
  3. Lidar sensors are quite costly compared to cameras. A digital camera costs less than $1 in quantity. Lidar units are in the hundred to several hundred dollar range.
  4. Radar and ultrasound both do a lot of what Lidar does – they are cheaper, and because they’re operating outside of the spectrum of visible light, they can see things that cameras and Lidar can’t – so they add more value than Lidar.
  5. Lidar does have a few odd “artifacts” – some objects don’t reflect light very well – very shiny objects reflect it only in a narrow direction that doesn’t return the light to the Lidar sensor. Processing to eliminate these artifacts is comparable in complexity to the stereo-camera solution.
  6. Lidar can’t REPLACE cameras – so you still need them for image recognition. For example, you can’t read the wording on a road sign using Lidar.

Activities – MISRA C


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  • Waymo (previously Google) are using much more clever sophistication – and having a wider variety of sensors helps them. But with only a small number of actual cars collecting driving data – training an AI is tough. They’ve only driven about 20 million miles with their test cars.
  • Tesla are using brute force AI. They’ve invested in a massively powerful AI computer in each car (two of them, actually) – and a billion dollar data center for processing AI learning. With a million cars collecting data for them, they can collect a BILLION miles of training data every month.

With the Tesla approach, less is more.

With the Waymo approach, sophistication is king – and the more data you can get from your sensors, the less processing you have to do.

Reference

Programming languages are used for Autopilot in Self Driving Cars. These cars have software that uses the C programming language. The MISRA C standard is important for the quality of this software. There are some core features of Autopilot, such as adaptive cruise control, lane centering, and autonomous parking. Some cars also have other advanced features that add to the convenience of the driver. Drivers can get these features by either buying a car with them included or by installing aftermarket Autopilot systems. Programming languages are also used for other purposes in these cars. For example, some companies use different languages to develop their infotainment systems or autonomous driving systems. Additionally, some companies have open-source projects for their vehicle software where they allow anyone to contribute code. Programming languages are thus an integral part of self-driving cars.

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To conclude:

Programming languages are used to give instructions to a computer. High-level programming languages are easier for humans to read and write than low-level languages, which are closer to machine code. Programming languages can be compiled or interpreted. A Compiled language is converted into machine code that the computer can understand before the program is run. An interpreted language is read by a software program called an interpreter, which then converts it into machine code that the computer can understand. Some programming languages are more suited to certain tasks than others. For example, FORTRAN is often used for scientific or engineering applications because its syntax is designed to produce code that is easy to read and understand. Finding the right programming language can be a challenging task for any programmer. When it comes to writing software for self-driving cars, there are a few important factors to consider. First, the language must be able to handle the large amounts of data that self-driving cars generate. Second, it must be able to handle the real-time processing requirements of autonomous vehicles. And third, it must be able to meet the safety requirements of the automotive industry.

 

Related:

I doubt it could operate well in the complete absence of light, but that situation can not arise. And it works extremely well in at least one very difficult seeing situation. Let me relate my experience.

On our recent road trip from San Diego to Clinton, Iowa, it was near dark as we reached the city limits of Clinton. Just as we did it started to rain heavily. A few seconds later the sky opened up and the heavy rain became what we call in Iowa, a Gully Washer. I was using Navigate on Autopilot, driving on the main road which led to the side street where our hotel destination was situated. I could see through the windshield by watching a 2 inch wide strip of cleared glass created as the windshield wiper passed back and forth. Other cars kept going and as I couldn’t see the road, I followed the car ahead of me. (Autopilot made that much easier than trying to stop as it even kept within the lane pretty much.) I could not see but I guess the cameras on the bumper below the headlights could see well enough. When the navigator told me to turn left in 200 feet, I couldn’t do that because I couldn’t see at all out the side window or the corner of the windshield. That is, nothing but flowing water, so I continued, to which Autopilot directed me to make a U-turn. On returning to the intersection of my turn, I caught a glimpse of a street sign and so, moving very slowly, turned around the sign. Water was flowing at least 6″ deep across the intersection but after a 50 or 100 feet, the crown of the road emerged and I realized that the rain was letting up.

We made it to the hotel parking lot which was full, shoes soaked getting into the door, and after checking in, waited the storm out which didn’t take long.

The point of this whole story is that the Tesla Autopilot will never have the opportunity to operate in the dark. The headlights provide enough light for the autopilot which can (in this case) see much better than a human driver. And if the battery is down to where the lights go out, I doubt the car will drive very far anyway.

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Autopilot-like functions are becoming more and more mainstream as technology improves. By late 2022, most car manufacturers will be offering some sort of more advanced self-driving capabilities.

What’s Important to Know When Evaluating

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When evaluating autopilot-like self driving systems, the main thing to look out for is Adaptive Cruise Control (ACC) and whether it handles starting and stopping at all speeds and on what kinds of roads. Then learn how well the vehicle can identify roads and stay in the center of the lane, called Lane Centering. Most manufacturers tout “Lane Keeping Assist” (LKA) as a way to help automate steering, but that’s different from Lane Centering and often a far cry from something like Tesla’s Autopilot system or Cadillac’s Super Cruise that are able to stay steadily centered in the lanes while driving.

If you’re not sure, check out videos on YouTube – enthusiasts and professionals often test out the systems to provide their opinions and real-world examples.

Also, ask the dealer how the system can be updated since technology and software changes so quickly. In Tesla’s case, the Autopilot system is continually updated over-the-air with software updates. Most other auto manufacturers require the updates to occur at the dealer during regular service updates.

Google’s Carbon Copy: Is Google’s Carbon Programming language the Right Successor to C++?

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What is the Top Job in AI? Data Scientist or Machine Learning Engineer?

The world of artificial intelligence (AI) is growing rapidly, and with it the demand for skilled data scientists and machine learning engineers. So which job is more in-demand? Which career path offers the biggest salary potential? And what skills do you need to pursue either one of these top jobs in AI? Here’s a closer look at what you need to know about becoming a data scientist or machine learning engineer.

What is the Top Job in AI? Data Scientist or Machine Learning Engineer?

Let’s start by defining what a Data Scientist and a Machine Learning Engineer do.

According to Wikipedia, A data scientist is someone who creates programming code and combines it with statistical knowledge to create insights from data.

Machine learning (ML) is a field of inquiry devoted to understanding and building methods that ‘learn’, that is, methods that leverage data to improve performance on some set of tasks.

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Do you want to work in AI? If so, which is the top job: data scientist or machine learning engineer? Many people might say that data scientist is the top job because they are responsible for analyzing data and extracting insights. However, machine learning engineers are responsible for designing and implementing algorithms that allow machines to learn from data, so they may be more responsible for the final outcome of an AI project. Which is the top job in AI? That depends on your priorities.

For me, The top job in all of AI is the machine learning engineer and NOT the data scientist.

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2023 AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams

It takes very unique skills and interests to be a Data Scientist which not everybody has. Obviously you need to enjoy Math and Statistics, because these are the foundations of any good data analysis. You need to have those technical skills, but also excellent social skills because as a Data Scientist you will have to communicate your results to stakeholders.

As a Data Scientist, you will often find yourself doing research and investigating why X happened, or how to achieve Y. That is why you should be a person that prefers to do investigative work over implementing a solution to certain problems.


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Data Science can be boring

The fun part of Data Science (for me) is building Machine Learning models to predict something. Those algorithms are extremely fascinating and take a very different approach to solving problems than traditional programming.

But building those models is only 10% of the work a Data Scientist is doing. The main part is wrangling and normalizing the data that has to be fed into those models. Wrangling, normalizing, transforming and aggregating data means that it is likely that you write a lot of SQL queries or something similar and execute query after query. Since most of the time the amount of data is pretty big, the queries will take a long time to run.

Many young Data Scientists cannot wait to get into their first job creating super efficient Machine Learning Models, maybe even doing Deep Learning. But then realizing that the work a Data Scientist is doing can vary a lot. Some Data Scientist may actually just do Deep Learning and heavy research, but many many others will just do SQL, Excel and very basic statistical models like linear regression. Most Data Scientists do not build their own Machine Learning Models from Scratch, but rather use some pre-built models like scikit-learn.

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Even though the pay is often good, the entry barriers are enormous, and the job market currently is oversaturated because a lot of people want to get into Data Science.

If you see yourself enjoying investigating causes/making predictions over implementing solutions, and you have or are looking to have a higher level education — then go for it. Data Science is definitely not for everyone, but might just be the right thing for you.

Data Science, as often known and mentioned, is a broader term for multiple processes and Machine Learning is one of the major parts of it. Machine Learning demands strong programming skills and understanding of algorithms, whereas, Data Science on the other hand requires strong analytical, statistical skills, combined by domain science and decision making.

The major difference between Data Science and Machine Learning lies in the set of tasks performed as a part of each process. Data Science contains a long list of tasks and tasks like predictions from the past data is a subset of this list of tasks and machine learning on the other hand absolutely deals with predictions only. One way to see the difference is that the end output of the Machine Learning algorithm is by and for a computer, whereas the output from a Data Science stack is meant to be understood by humans. Keeping in mind the differences between the underlying methodologies in the two fields of study, let’s try to understand the difference between the roles and responsibilities of someone who is designated as a Data Scientist vs a Machine Learning Engineer.

What is the Top Job in AI? Data Scientist or Machine Learning Engineer?
What is the Top Job in AI? Data Scientist or Machine Learning Engineer?

A Data Scientist cleans data, does data mining, feature engineering, and the like, building models. Their models may or may not use ML and when it does use ML it is generic ML from a library like XGBoost. Data Scientists do not specialize in advanced ML.

A Machine Learning Engineer takes a model then chooses a more advanced ML for the job. They often end up creating their own ML, their own deep neural networks typically, to get more accuracy out of the model created by the data scientist. When the model is ready for prime time the MLEng will deploy the model into the cloud and work with often the frontend web dev team (or whoever) to help them interface with the model.

One of the benefits of an MLE is they are never on call. (ymmv if you’re at a shitty company, or a small company that is under resourced.) Instead DevOps, MLOps, or in rare situations Data Engineers / Infrastructure Software Engineers, will be on call and responsible for the server that is hosting the model. If something goes down, or something is on fire, they’re on call to fix it. If the model the MLE deployed is broken and causing errors, instead of contacting the MLE in the middle of the night they’ll just roll back to an older version.

DevOps monitors servers for issues and takes care of server issues.

 

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Summary:

There’s a lot of buzz around AI and machine learning right now, and for good reason – the potential applications are endless. But with all the uncertainty around what these technologies will eventually look like, it can be tough to decide which career path to pursue in this field.

There are many differences between a Data Scientist and Machine Learning Engineer, but the main ones are:

  • Data scientists define the metrics. MLEs try to move them.
  • Data scientists understand the problem. MLEs find the solution.

Sources: Quora Reddit analyticxlabs

What are some good datasets for Data Science and Machine Learning?

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Summary of Machine Learning and AI Capabilities

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Learning: Supervised, Unsupervised, Reinforcement Learning

What is Machine Learning?

Machine learning is the study of computer algorithms that improve automatically through experience. It is seen as a subset of artificial intelligence. Machine Learning explores the study and construction of algorithms that can learn from and make predictions on data. You select a model to train and then manually perform feature extraction. Used to devise complex models and algorithms that lend themselves to a prediction which in commercial use is known as predictive analytics.

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Below are the most common Machine Learning use cases and capabilities:

Summary of ML/AI Capabilities

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What is Supervised Learning? 

Supervised learning is the machine learning task of inferring a function from labeled training data. The training data consist of a set of training examples.

Algorithms: Support Vector Machines, Regression, Naive Bayes, Decision Trees, K-nearest Neighbor Algorithm and Neural Networks

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Example: If you built a fruit classifier, the labels will be “this is an orange, this is an apple and this is a banana”, based on showing the classifier examples of apples, oranges and bananas.

What is Unsupervised learning?

Unsupervised learning is a type of machine learning algorithm used to draw inferences from datasets consisting of input data without labelled responses.

Algorithms: Clustering, Anomaly Detection, Neural Networks and Latent Variable Models

Example: In the same example, a fruit clustering will categorize as “fruits with soft skin and lots of dimples”, “fruits with shiny hard skin” and “elongated yellow fruits”.

Explain the difference between supervised and unsupervised machine learning?

In supervised machine learning algorithms, we have to provide labeled data, for example, prediction of stock market prices, whereas in unsupervised we need not have labeled data, for example, classification of emails into spam and non-spam.

What is deep learning, and how does it contrast with other machine learning algorithms?

Deep learning is a subset of machine learning that is concerned with neural networks: how to use backpropagation and certain principles from neuroscience to more accurately model large sets of unlabelled or semi-structured data. In that sense, deep learning represents an unsupervised learning algorithm that learns representations of data through the use of neural nets.

What is Problem Formulation in Machine Learning?

The problem formulation phase of the ML Pipeline is critical, and it’s where everything begins. Typically, this phase is kicked off with a question of some kind. Examples of these kinds of questions include: Could cars really drive themselves?  What additional product should we offer someone as they checkout? How much storage will clients need from a data center at a given time? 

The problem formulation phase starts by seeing a problem and thinking “what question, if I could answer it, would provide the most value to my business?” If I knew the next product a customer was going to buy, is that most valuable? If I knew what was going to be popular over the holidays, is that most valuable? If I better understood who my customers are, is that most valuable?

However, some problems are not so obvious. When sales drop, new competitors emerge, or there’s a big change to a company/team/org, it can be easy to say, “I see the problem!” But sometimes the problem isn’t so clear. Consider self-driving cars. How many people think to themselves, “driving cars is a huge problem”? Probably not many. In fact, there isn’t a problem in the traditional sense of the word but there is an opportunity. Creating self-driving cars is a huge opportunity. That doesn’t mean there isn’t a problem or challenge connected to that opportunity. How do you design a self-driving system? What data would you look at to inform the decisions you make? Will people purchase self-driving cars?

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Part of the problem formulation phase includes seeing where there are opportunities to use machine learning.  

To formulate a problem in ML, consider the following questions:

  1. Is machine learning appropriate for this problem, and why or why not?
  2. What is the ML problem if there is one, and what would a success metric look like?
  3. What kind of ML problem is this?
  4. Is the data appropriate?

Machine Learning Problem Formulation Examples:

1)  Amazon recently began advertising to its customers when they visit the company website. The Director in charge of the initiative wants the advertisements to be as tailored to the customer as possible. You will have access to all the data from the retail webpage, as well as all the customer data.

  • ML is appropriate because of the scale, variety and speed required. There are potentially thousands of ads and millions of customers that need to be served customized ads immediately as they arrive to the site.
  • The problem is ads that are not useful to customers are a wasted opportunity and a nuisance to customers, yet not serving ads at all is a wasted opportunity. So how does Amazon serve the most relevant advertisements to its retail customers?
    1. Success would be the purchase of a product that was advertised.
  • This is a supervised learning problem because we have a labeled data point, our success metric, which is the purchase of a product.
  • This data is appropriate because it is both the retail webpage data as well as the customer data.

What are the different Algorithm techniques in Machine Learning?

The different types of techniques in Machine Learning are
● Supervised Learning
● Unsupervised Learning
● Semi-supervised Learning
● Reinforcement Learning
● Transduction
● Learning to Learn

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Different Types of Machine Learning with examples

 

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What’s the difference between a generative and discriminative model?

A generative model will learn categories of data while a discriminative model will simply learn the distinction between different categories of data. Discriminative models will generally outperform generative models on classification tasks.

What Are the Applications of Supervised Machine Learning in Modern Businesses?

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Applications of supervised machine learning include:
Email Spam Detection
Here we train the model using historical data that consists of emails categorized as spam or not spam. This labeled information is fed as input to the model.
Healthcare Diagnosis
By providing images regarding a disease, a model can be trained to detect if a person is suffering from the disease or not.
Sentiment Analysis
This refers to the process of using algorithms to mine documents and determine whether they’re positive, neutral, or negative in sentiment.
Fraud Detection
Training the model to identify suspicious patterns, we can detect instances of possible fraud.

What Is Semi-supervised Machine Learning?

Supervised learning uses data that is completely labeled, whereas unsupervised learning uses no training data.
In the case of semi-supervised learning, the training data contains a small amount of labeled data and a large amount of unlabeled data.

What Are Unsupervised Machine Learning Techniques?

There are two techniques used in unsupervised learning: clustering and association.

Clustering
● Clustering problems involve data to be divided into subsets. These subsets, also called clusters, contain data that are similar to each other. Different clusters reveal different details about the objects, unlike classification or regression.

Association
● In an association problem, we identify patterns of associations between different variables or items.
● For example, an eCommerce website can suggest other items for you to buy, based on the prior purchases that you have made, spending habits, items in your wish list, other customers’ purchase habits, and so on.

What evaluation approaches would you work to gauge the effectiveness of a machine learning model?

You would first split the dataset into training and test sets, or perhaps use cross-validation techniques to further segment the dataset into composite sets of training and test sets within the data. You should then implement a choice selection of performance metrics: here is a fairly comprehensive list. You could use measures such as the F1 score, the accuracy, and the confusion matrix. What’s important here is to demonstrate that you understand the nuances of how a model is measured and how to choose the right performance measures for the right situations.

What Are the Three Stages of Building a Model in Machine Learning?

The three stages of building a machine learning model are:
● Model Building Choose a suitable algorithm for the model and train it according to the requirement
● Model Testing Check the accuracy of the model through the test data
● Applying the Mode Make the required changes after testing and use the final model for real-time projects. Here, it’s important to remember that once in a while, the model needs to be checked to make sure it’s working correctly. It should be modified to make sure that it is up-to-date.

A data scientist wants to visualize the correlation between features in their dataset. What tool(s) can they use to visualize this in a correlation matrix? 

Answer: Matplotlib, Seaborn

You are preprocessing a dataset that includes categorical features. You want to determine which categories of particular features are most common in your dataset. Which basic descriptive statistic could you use?
Answer: Mode

What are some examples of categorical features?

In machine learning and data science, categorical features are variables that can take on one of a limited number of values. For example, a categorical feature might represent the color of a car as Red, Yellow, or Blue. In general, categorical features are used to represent discrete characteristics (such as gender, race, or profession) that can be sorted into categories. When working with categorical features, it is often necessary to convert them into numerical form so that they can be used by machine learning algorithms. This process is known as encoding, and there are several different ways to encode categorical features. One common approach is to use a technique called one-hot encoding, which creates a new column for each possible category. For example, if there are three colors (Red, Yellow, and Blue), then each color would be represented by a separate column where all the values are either 0 or 1 (1 indicates that the row belongs to that category). Machine learning algorithms can then treat each column as a separate feature when training the model. Other approaches to encoding categorical data include label encoding and target encoding. These methods are often used in conjunction with one-hot encoding to improve the accuracy of machine learning models.

How many variables are enough for multiple regressions?

Which of the following is most suitable for supervised learning?

 
Answer: Identifying birds in an image
 
 
 

You’ve plotted the correlation matrix of your dataset’s features and realized that two of the features present a high negative correlation (-0.95). What should you do?

Answer: Remove one of the features

You are in charge of preprocessing the data your publishing company wants to use for a new ML model they’re building, which aims to predict the influence an academic journal will have in its field. The preprocessing step is necessary to prepare the data for model training. What type of issue with the data might you encounter during this preprocessing phase? 

Answer: Outliers, Missing values

A Machine Learning Engineer is creating and preparing data for a linear regression model. However, while preparing the data, the Engineer notices that about 20% of the numerical data contains missing values in the same two columns. The shape of the data is 500 rows by 4 columns, including the target column.
How can the Engineer handle the missing values in the data?

(Select TWO.)
 
 
 

Answer: Fill he missing values with mean of the column, Impute the missing values using regression

A Data Scientist created a correlation matrix between nine variables and the target variable. The correlation coefficient between two of the numerical variables, variable 1 and variable 5, is -0.95. How should the Data Scientist interpret the correlation coefficient?

Answer: As variable 1 increases, variable 5 decreases

An advertising and analytics company uses machine learning to predict user response to online advertisements using a custom XGBoost model. The company wants to improve its ML pipeline by porting its training and inference code, written in R, to Amazon SageMaker, and do so with minimal changes to the existing code.

Answer: Use the Build Your Own Container (BYOC) Amazon Sagemaker option.
Create a new docker container with the existing code. Register the container in Amazon Elastic Container registry. with the existing code. Register the container in Amazon Elastic Container Registry. Finally run the training and inference jobs using this container.

An ML engineer at a text analytics startup wants to develop a text classification model. The engineer collected large amounts of data to develop a supervised text classification model. The engineer is getting 99% accuracy on the dataset but when the model is deployed to production, it performs significantly worse. What is the most likely cause of this?

Answer: The engineer did not split the data to validate the model on unseen data.

For a classification problem, what does the loss function measure?
Answer: A loss function measures how accurate your prediction is with respect to the true values.

Gradient Descent is an important optimization method. What are 3 TRUE statements about the gradient descent method?

(Select THREE)
 
 

Answer: It tries to find the minimum of a loss function. It can involve multiple iterations
It uses learning rate to multiply the effect of gradients

What is Deep Learning?

Deep Learning is nothing but a paradigm of machine learning which has shown incredible promise in recent years. This is because of the fact that Deep Learning shows a great analogy with the functioning of the neurons in the human brain.

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What is the difference between machine learning and deep learning?

Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed. Machine learning can be categorized in the following four categories.
1. Supervised machine learning,
2. Semi-supervised machine learning,
3. Unsupervised machine learning,
4. Reinforcement learning.

Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks.

• The main difference between deep learning and machine learning is due to the way data is
presented in the system. Machine learning algorithms almost always require structured data, while deep learning networks rely on layers of ANN (artificial neural networks).

• Machine learning algorithms are designed to “learn” to act by understanding labeled data and then use it to produce new results with more datasets. However, when the result is incorrect, there is a need to “teach them”. Because machine learning algorithms require bulleted data, they are not suitable for solving complex queries that involve a huge amount of data.

• Deep learning networks do not require human intervention, as multilevel layers in neural
networks place data in a hierarchy of different concepts, which ultimately learn from their own mistakes. However, even they can be wrong if the data quality is not good enough.

• Data decides everything. It is the quality of the data that ultimately determines the quality of the result.

• Both of these subsets of AI are somehow connected to data, which makes it possible to represent a certain form of “intelligence.” However, you should be aware that deep learning requires much more data than a traditional machine learning algorithm. The reason for this is that deep learning networks can identify different elements in neural network layers only when more than a million data points interact. Machine learning algorithms, on the other hand, are capable of learning by pre-programmed criteria.

Can you explain the differences between supervised, unsupervised, and reinforcement learning?

In supervised learning, we train a model to learn the relationship between input data and output
data. We need to have labeled data to be able to do supervised learning.
With unsupervised learning, we only have unlabeled data. The model learns a representation of the data. Unsupervised learning is frequently used to initialize the parameters of the model when we have a lot of unlabeled data and a small fraction of labeled data. We first train an unsupervised model and, after that, we use the weights of the model to train a supervised model. In reinforcement learning, the model has some input data and a reward depending on the output of the model. The model learns a policy that maximizes the reward. Reinforcement learning has been applied successfully to strategic games such as Go and even classic Atari video games.

What is the reason for the popularity of Deep Learning in recent times? 

Now although Deep Learning has been around for many years, the major breakthroughs from these techniques came just in recent years. This is because of two main reasons:
• The increase in the amount of data generated through various sources
• The growth in hardware resources required to run these models
GPUs are multiple times faster and they help us build bigger and deeper deep learning models in comparatively less time than we required previously

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What is reinforcement learning?

 

Reinforcement Learning allows to take actions to max cumulative reward. It learns by trial and error through reward/penalty system. Environment rewards agent so by time agent makes better decisions.
Ex: robot=agent, maze=environment. Used for complex tasks (self-driving cars, game AI).

 

RL is a series of time steps in a Markov Decision Process:

 

1. Environment: space in which RL operates
2. State: data related to past action RL took
3. Action: action taken
4. Reward: number taken by agent after last action
5. Observation: data related to environment: can be visible or partially shadowed

 

Explain Ensemble learning.

In ensemble learning, many base models like classifiers and regressors are generated and combined together so that they give better results. It is used when we build component classifiers that are accurate and independent. There are sequential as well as parallel ensemble methods.

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What are the parametric models? Give an example.

Parametric models are those with a finite number of parameters. To predict new data, you only need to know the parameters of the model. Examples include linear regression, logistic regression, and linear SVMs.
Non-parametric models are those with an unbounded number of parameters, allowing for more flexibility. To predict new data, you need to know the parameters of the model and the state of the data that has been observed. Examples include decision trees, k-nearest neighbors, and topic models using latent Dirichlet analysis.

What are support vector machines?

 

Support vector machines are supervised learning algorithms used for classification and regression analysis.

What is batch statistical learning?

Statistical learning techniques allow learning a function or predictor from a set of observed data that can make predictions about unseen or future data. These techniques provide guarantees on the performance of the learned predictor on the future unseen data based on a statistical assumption on the data generating process.

 

What Will Happen If the Learning Rate is Set inaccurately (Too Low or Too High)? 

 

When your learning rate is too low, training of the model will progress very slowly as we are making minimal updates to the weights. It will take many updates before reaching the minimum point.
If the learning rate is set too high, this causes undesirable divergent behavior to the loss function due to drastic updates in weights. It may fail to converge (model can give a good output) or even diverge (data is too chaotic for the network to train).

 

What Is The Difference Between Epoch, Batch, and Iteration in Deep Learning? 

 

Epoch – Represents one iteration over the entire dataset (everything put into the training model).
Batch – Refers to when we cannot pass the entire dataset into the neural network at once, so we divide the dataset into several batches.
Iteration – if we have 10,000 images as data and a batch size of 200. then an epoch should run 50 iterations (10,000 divided by 50).

 

Why Is Tensorflow the Most Preferred Library in Deep Learning?

Tensorflow provides both C++ and Python APIs, making it easier to work on and has a faster compilation time compared to other Deep Learning libraries like Keras and Torch. Tensorflow supports both CPU and GPU computing devices.

What Do You Mean by Tensor in Tensorflow?

A tensor is a mathematical object represented as arrays of higher dimensions. These arrays of data with different dimensions and ranks fed as input to the neural network are called “Tensors.”

Explain a Computational Graph.

Everything in TensorFlow is based on creating a computational graph. It has a network of nodes where each node operates, Nodes represent mathematical operations, and edges represent tensors. Since data flows in the form of a graph, it is also called a “DataFlow Graph.”

Cognition: Reasoning on top of data (Regression, Classification, Pattern Recognition)

What is the difference between classification and regression?

Classification is used to produce discrete results, classification is used to classify data into some specific categories. For example, classifying emails into spam and non-spam categories.
Whereas, We use regression analysis when we are dealing with continuous data, for example predicting stock prices at a certain point in time.

 

Explain the Bias-Variance Tradeoff.

Predictive models have a tradeoff between bias (how well the model fits the data) and variance (how much the model changes based on changes in the inputs).
Simpler models are stable (low variance) but they don’t get close to the truth (high bias).
More complex models are more prone to overfitting (high variance) but they are expressive enough to get close to the truth (low bias). The best model for a given problem usually lies somewhere in the middle.

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What is the difference between stochastic gradient descent (SGD) and gradient descent (GD)?

Both algorithms are methods for finding a set of parameters that minimize a loss function by evaluating parameters against data and then making adjustments.
In standard gradient descent, you’ll evaluate all training samples for each set of parameters.
This is akin to taking big, slow steps toward the solution.
In stochastic gradient descent, you’ll evaluate only 1 training sample for the set of parameters before updating them. This is akin to taking small, quick steps toward the solution.

How Can You Choose a Classifier Based on a Training Set Data Size?

When the training set is small, a model that has a right bias and low variance seems to work better because they are less likely to overfit. For example, Naive Bayes works best when the training set is large. Models with low bias and high variance tend to perform better as they work fine with complex relationships. 

 

Explain Latent Dirichlet Allocation (LDA)

Latent Dirichlet Allocation (LDA) is a common method of topic modeling, or classifying documents by subject matter.
LDA is a generative model that represents documents as a mixture of topics that each have their own probability distribution of possible words.
The “Dirichlet” distribution is simply a distribution of distributions. In LDA, documents are distributions of topics that are distributions of words.

Explain Principle Component Analysis (PCA)

PCA is a method for transforming features in a dataset by combining them into uncorrelated linear combinations.
These new features, or principal components, sequentially maximize the variance represented (i.e. the first principal component has the most variance, the second principal component has the second most, and so on).
As a result, PCA is useful for dimensionality reduction because you can set an arbitrary variance cutoff.

PCA is a dimensionality reduction technique that enables you to identify the correlations and patterns in the dataset so that it can be transformed into a dataset of significantly lower dimensions without any loss of important information.

• It is an unsupervised statistical technique used to examine the interrelations among a set of variables. It is also known as a general factor analysis where regression determines a line of best fit.

• It works on a condition that while the data in a higher-dimensional space is mapped to data in a lower dimension space, the variance or spread of the data in the lower dimensional space should be maximum.

PCA is carried out in the following steps

1. Standardization of Data
2. Computing the covariance matrix
3. Calculation of the eigenvectors and eigenvalues
4. Computing the Principal components
5. Reducing the dimensions of the Data.

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What’s the F1 score? How would you use it?

The F1 score is a measure of a model’s performance. It is a weighted average of the precision and recall of a model, with results tending to 1 being the best, and those tending to 0 being the worst. You would use it in classification tests where true negatives don’t matter much.

When should you use classification over regression?

Classification produces discrete values and dataset to strict categories, while regression gives you continuous results that allow you to better distinguish differences between individual points.
You would use classification over regression if you wanted your results to reflect the belongingness of data points in your dataset to certain explicit categories (ex: If you wanted to know whether a name was male or female rather than just how correlated they were with male and female names.)

How do you ensure you’re not overfitting with a model?

This is a simple restatement of a fundamental problem in machine learning: the possibility of overfitting training data and carrying the noise of that data through to the test set, thereby providing inaccurate generalizations.
There are three main methods to avoid overfitting:
1- Keep the model simpler: reduce variance by taking into account fewer variables and parameters, thereby removing some of the noise in the training data.
2- Use cross-validation techniques such as k-folds cross-validation.
3- Use regularization techniques such as LASSO that penalize certain model parameters if they’re likely to cause overfitting.

How Will You Know Which Machine Learning Algorithm to Choose for Your Classification Problem?

While there is no fixed rule to choose an algorithm for a classification problem, you can follow these guidelines:
● If accuracy is a concern, test different algorithms and cross-validate them
● If the training dataset is small, use models that have low variance and high bias
● If the training dataset is large, use models that have high variance and little bias

Why is Area Under ROC Curve (AUROC) better than raw accuracy as an out-of-sample evaluation metric?

AUROC is robust to class imbalance, unlike raw accuracy.
For example, if you want to detect a type of cancer that’s prevalent in only 1% of the population, you can build a model that achieves 99% accuracy by simply classifying everyone has cancer-free.

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What are the advantages and disadvantages of neural networks?

Advantages: Neural networks (specifically deep NNs) have led to performance breakthroughs for unstructured datasets such as images, audio, and video. Their incredible flexibility allows them to learn patterns that no other ML algorithm can learn.
Disadvantages: However, they require a large amount of training data to converge. It’s also difficult to pick the right architecture, and the internal “hidden” layers are incomprehensible.

Define Precision and Recall.

Precision
● Precision is the ratio of several events you can correctly recall to the total number of events you recall (mix of correct and wrong recalls).
● Precision = (True Positive) / (True Positive + False Positive)
Recall
● A recall is the ratio of a number of events you can recall the number of total events.
● Recall = (True Positive) / (True Positive + False Negative)

Model Evaluation with test data set

image

What Is Decision Tree Classification?

A decision tree builds classification (or regression) models as a tree structure, with datasets broken up into ever-smaller subsets while developing the decision tree, literally in a tree-like way with branches and nodes. Decision trees can handle both categorical and numerical data.

What Is Pruning in Decision Trees, and How Is It Done?

Pruning is a technique in machine learning that reduces the size of decision trees. It reduces the complexity of the final classifier, and hence improves predictive accuracy by the reduction of overfitting.
Pruning can occur in:
● Top-down fashion. It will traverse nodes and trim subtrees starting at the root
● Bottom-up fashion. It will begin at the leaf nodes
There is a popular pruning algorithm called reduced error pruning, in which:
● Starting at the leaves, each node is replaced with its most popular class
● If the prediction accuracy is not affected, the change is kept
● There is an advantage of simplicity and speed

What Is a Recommendation System?

Anyone who has used Spotify or shopped at Amazon will recognize a recommendation system:
It’s an information filtering system that predicts what a user might want to hear or see based on choice patterns provided by the user.

What Is Kernel SVM?

Kernel SVM is the abbreviated version of the kernel support vector machine. Kernel methods are a class of algorithms for pattern analysis, and the most common one is the kernel SVM.

What Are Some Methods of Reducing Dimensionality?

You can reduce dimensionality by combining features with feature engineering, removing collinear features, or using algorithmic dimensionality reduction.
Now that you have gone through these machine learning interview questions, you must have got an idea of your strengths and weaknesses in this domain.

 

How is KNN different from k-means clustering?

K-Nearest Neighbors is a supervised classification algorithm, while k-means clustering is an unsupervised clustering algorithm. While the mechanisms may seem similar at first, what this really means is that in order for K-Nearest Neighbors to work, you need labeled data you want to classify an unlabeled point into (thus the nearest neighbor part). K-means clustering requires only a set of unlabeled points and a threshold: the algorithm will take unlabeled points and gradually learn how to cluster them into groups by computing the mean of the distance between different points.

What are difference between Data Mining and Machine learning?

Machine learning relates to the study, design, and development of the algorithms that give computers the capability to learn without being explicitly programmed. While data mining can be defined as the process in which the unstructured data tries to extract knowledge or unknown interesting patterns. During this processing machine, learning algorithms are used.

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What is “Naive” in a Naive Bayes?

Reference: Naive Bayes Classifier on Wikipedia

Naive Bayes methods are a set of supervised learning algorithms based on applying Bayes’ theorem with the “naive” assumption of conditional independence between every pair of features given the value of the class variable. Bayes’ theorem states the following relationship, given class variable y and dependent feature vector X1through Xn:

Machine Learning Algorithms Naive Bayes

What is PCA (Principal Component Analysis)? When do you use it?

Reference: PCA on wikipedia

Principal component analysis (PCA) is a statistical method used in Machine Learning. It consists in projecting data in a higher dimensional space into a lower dimensional space by maximizing the variance of each dimension.

The process works as following. We define a matrix A with > rows (the single observations of a dataset – in a tabular format, each single row) and @ columns, our features. For this matrix we construct a variable space with as many dimensions as there are features. Each feature represents one coordinate axis. For each feature, the length has been standardized according to a scaling criterion, normally by scaling to unit variance. It is determinant to scale the features to a common scale, otherwise the features with a greater magnitude will weigh more in determining the principal components. Once plotted all the observations and computed the mean of each variable, that mean will be represented by a point in the center of our plot (the center of gravity). Then, we subtract each observation with the mean, shifting the coordinate system with the center in the origin. The best fitting line resulting is the line that best accounts for the shape of the point swarm. It represents the maximum variance direction in the data. Each observation may be projected onto this line in order to get a coordinate value along the PC-line. This value is known as a score. The next best-fitting line can be similarly chosen from directions perpendicular to the first.
Repeating this process yields an orthogonal basis in which different individual dimensions of the data are uncorrelated. These basis vectors are called principal components.

PCA is mostly used as a tool in exploratory data analysis and for making predictive models. It is often used to visualize genetic distance and relatedness between populations.

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PCA is a technique that is used for reducing the dimensionality of a dataset while still preserving as much of the variance as possible. It is commonly used in machine learning and data science, as it can help to improve the performance of models by making the data easier to work with. In order to perform PCA on a dataset, there are a few pre-processing steps that need to be undertaken.

  • First, any features that are strongly correlated with each other should be removed, as PCA will not be effective in reducing the dimensionality of the data if there are strong correlations present.
  • Next, any features that contain missing values should be imputed, as PCA cannot be performed on data that contains missing values.
  • Finally, the data should be scaled so that all features are on the same scale; this is necessary because PCA is based on the variance of the data, and if the scales of the features are different then PCA will not be able to accurately identify which features are most important in terms of variance.
  • Once these pre-processing steps have been completed, PCA can be performed on the dataset.

Principal component analysis (PCA) is a statistical technique that is used to reduce the dimensionality of a dataset. PCA is often used as a pre-processing step in machine learning and data science, as it can help to improve the performance of models. In order to perform PCA on a dataset, the data must first be scaled and centered. Scaling ensures that all of the features are on the same scale, which is important for PCA. Centering means that the mean of each feature is zero. This is also important for PCA, as PCA is sensitive to changes in the mean of the data. Once the data has been scaled and centered, PCA can be performed by computing the eigenvectors and eigenvalues of the covariance matrix. These eigenvectors and eigenvalues can then be used to transform the data into a lower-dimensional space.

SVM (Support Vector Machine)  algorithm

Reference: SVM on wikipedia

Classifying data is a common task in machine learning. Suppose some given data points each belong to one of two classes, and the goal is to decide which class a new data point will be in. In the case of supportvector machines, a data point is viewed as a p-dimensional vector (a list of p numbers), and we want to know whether we can separate such points with a (p − 1)-dimensional hyperplane. This is called a linear classifier. There are many hyperplanes that might classify the data. One reasonable choice as the best hyperplane is the one that represents the largest separation, or margin, between the two classes. So, we
choose the hyperplane so that the distance from it to the nearest data point on each side is maximized. If such a hyperplane exists, it is known as the maximum-margin hyperplane and the linear classifier it defines is known as a maximum-margin classifier; or equivalently, the perceptron of optimal stability. The best hyper plane that divides the data is H3.

  • SVMs are helpful in text and hypertext categorization, as their application can significantly reduce the need for labeled training instances in both the standard inductive and transductive settings.
  • Some methods for shallow semantic parsing are based on support vector machines.
  • Classification of images can also be performed using SVMs. Experimental results show that SVMs achieve significantly higher search accuracy than traditional query refinement schemes after just three to four rounds of relevance feedback.
  • Classification of satellite data like SAR data using supervised SVM.
  • Hand-written characters can be recognized using SVM.

What are the support vectors in SVM? 

In the diagram, we see that the sketched lines mark the distance from the classifier (the hyper plane) to the closest data points called the support vectors (darkened data points). The distance between the two thin lines is called the margin.

To extend SVM to cases in which the data are not linearly separable, we introduce the hinge loss function, max (0, 1 – yi(w∙ xi − b)). This function is zero if x lies on the correct side of the margin. For data on the wrong side of the margin, the function’s value is proportional to the distance from the margin. 

What are the different kernels in SVM?

There are four types of kernels in SVM.
1. LinearKernel
2. Polynomial kernel
3. Radial basis kernel
4. Sigmoid kernel

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What are the most known ensemble algorithms? 

Reference: Ensemble Algorithms

The most popular trees are: AdaBoost, Random Forest, and  eXtreme Gradient Boosting (XGBoost).

AdaBoost is best used in a dataset with low noise, when computational complexity or timeliness of results is not a main concern and when there are not enough resources for broader hyperparameter tuning due to lack of time and knowledge of the user.

Random forests should not be used when dealing with time series data or any other data where look-ahead bias should be avoided, and the order and continuity of the samples need to be ensured. This algorithm can handle noise relatively well, but more knowledge from the user is required to adequately tune the algorithm compared to AdaBoost.

The main advantages of XGBoost is its lightning speed compared to other algorithms, such as AdaBoost, and its regularization parameter that successfully reduces variance. But even aside from the regularization parameter, this algorithm leverages a learning rate (shrinkage) and subsamples from the features like random forests, which increases its ability to generalize even further. However, XGBoost is more difficult to understand, visualize and to tune compared to AdaBoost and random forests. There is a multitude of hyperparameters that can be tuned to increase performance.

What are Artificial Neural Networks?

Artificial Neural networks are a specific set of algorithms that have revolutionized machine learning. They are inspired by biological neural networks. Neural Networks can adapt to changing the input, so the network generates the best possible result without needing to redesign the output criteria.

Artificial Neural Networks works on the same principle as a biological Neural Network. It consists of inputs which get processed with weighted sums and Bias, with the help of Activation Functions.

How Are Weights Initialized in a Network?

There are two methods here: we can either initialize the weights to zero or assign them randomly.

Initializing all weights to 0: This makes your model similar to a linear model. All the neurons and every layer perform the same operation, giving the same output and making the deep net useless.

Initializing all weights randomly: Here, the weights are assigned randomly by initializing them very close to 0. It gives better accuracy to the model since every neuron performs different computations. This is the most commonly used method.

What Is the Cost Function? 

Also referred to as “loss” or “error,” cost function is a measure to evaluate how good your model’s performance is. It’s used to compute the error of the output layer during backpropagation. We push that error backwards through the neural network and use that during the different training functions.
The most known one is the mean sum of squared errors.

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What Are Hyperparameters?

With neural networks, you’re usually working with hyperparameters once the data is formatted correctly.
A hyperparameter is a parameter whose value is set before the learning process begins. It determines how a network is trained and the structure of the network (such as the number of hidden units, the learning rate, epochs, batches, etc.).

What Are the Different Layers on CNN?

Reference: Layers of CNN 

The Convolutional neural networks are regularized versions of multilayer perceptron (MLP). They were developed based on the working of the neurons of the animal visual cortex.

The objective of using the CNN:

The idea is that you give the computer this array of numbers and it will output numbers that describe the probability of the image being a certain class (.80 for a cat, .15 for a dog, .05 for a bird, etc.). It works similar to how our brain works. When we look at a picture of a dog, we can classify it as such if the picture has identifiable features such as paws or 4 legs. In a similar way, the computer is able to perform image classification by looking for low-level features such as edges and curves and then building up to more abstract concepts through a series of convolutional layers. The computer uses low-level features obtained at the initial levels to generate high-level features such as paws or eyes to identify the object.

There are four layers in CNN:
1. Convolutional Layer – the layer that performs a convolutional operation, creating several smaller picture windows to go over the data.
2. Activation Layer (ReLU Layer) – it brings non-linearity to the network and converts all the negative pixels to zero. The output is a rectified feature map. It follows each convolutional layer.
3. Pooling Layer – pooling is a down-sampling operation that reduces the dimensionality of the feature map. Stride = how much you slide, and you get the max of the n x n matrix
4. Fully Connected Layer – this layer recognizes and classifies the objects in the image.

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What Is Pooling on CNN, and How Does It Work?

Pooling is used to reduce the spatial dimensions of a CNN. It performs down-sampling operations to reduce the dimensionality and creates a pooled feature map by sliding a filter matrix over the input matrix.

What are Recurrent Neural Networks (RNNs)? 

Reference: RNNs

RNNs are a type of artificial neural networks designed to recognize the pattern from the sequence of data such as Time series, stock market and government agencies etc.

Recurrent Neural Networks (RNNs) add an interesting twist to basic neural networks. A vanilla neural network takes in a fixed size vector as input which limits its usage in situations that involve a ‘series’ type input with no predetermined size.

RNNs are designed to take a series of input with no predetermined limit on size. One could ask what’s\ the big deal, I can call a regular NN repeatedly too?

Sure can, but the ‘series’ part of the input means something. A single input item from the series is related to others and likely has an influence on its neighbors. Otherwise it’s just “many” inputs, not a “series” input (duh!).
Recurrent Neural Network remembers the past and its decisions are influenced by what it has learnt from the past. Note: Basic feed forward networks “remember” things too, but they remember things they learnt during training. For example, an image classifier learns what a “1” looks like during training and then uses that knowledge to classify things in production.
While RNNs learn similarly while training, in addition, they remember things learnt from prior input(s) while generating output(s). RNNs can take one or more input vectors and produce one or more output vectors and the output(s) are influenced not just by weights applied on inputs like a regular NN, but also by a “hidden” state vector representing the context based on prior input(s)/output(s). So, the same input could produce a different output depending on previous inputs in the series.

In summary, in a vanilla neural network, a fixed size input vector is transformed into a fixed size output vector. Such a network becomes “recurrent” when you repeatedly apply the transformations to a series of given input and produce a series of output vectors. There is no pre-set limitation to the size of the vector. And, in addition to generating the output which is a function of the input and hidden state, we update the hidden state itself based on the input and use it in processing the next input.

What is the role of the Activation Function?

The Activation function is used to introduce non-linearity into the neural network helping it to learn more complex function. Without which the neural network would be only able to learn linear function which is a linear combination of its input data. An activation function is a function in an artificial neuron that delivers an output based on inputs.

Machine Learning libraries for various purposes

What is an Auto-Encoder?

Reference: Auto-Encoder

Auto-encoders are simple learning networks that aim to transform inputs into outputs with the minimum possible error. This means that we want the output to be as close to input as possible. We add a couple of layers between the input and the output, and the sizes of these layers are smaller than the input layer. The auto-encoder receives unlabeled input which is then encoded to reconstruct the input. 

An autoencoder is a type of artificial neural network used to learn efficient data coding in an unsupervised manner. The aim of an autoencoder is to learn a representation (encoding) for a set of data, typically for dimensionality reduction, by training the network to ignore signal “noise”. Along with the reduction side, a reconstructing side is learnt, where the autoencoder tries to generate from the reduced encoding a representation as close as possible to its original input, hence its name. Several variants exist to the basic model, with the aim of forcing the learned representations of the input to assume useful properties.
Autoencoders are effectively used for solving many applied problems, from face recognition to acquiring the semantic meaning of words.

What is a Boltzmann Machine?

Boltzmann machines have a simple learning algorithm that allows them to discover interesting features that represent complex regularities in the training data. The Boltzmann machine is basically used to optimize the weights and the quantity for the given problem. The learning algorithm is very slow in networks with many layers of feature detectors. “Restricted Boltzmann Machines” algorithm has a single layer of feature detectors which makes it faster than the rest.

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What Is Dropout and Batch Normalization?

Dropout is a technique of dropping out hidden and visible nodes of a network randomly to prevent overfitting of data (typically dropping 20 per cent of the nodes). It doubles the number of iterations needed to converge the network. It used to avoid overfitting, as it increases the capacity of generalization.

Batch normalization is the technique to improve the performance and stability of neural networks by normalizing the inputs in every layer so that they have mean output activation of zero and standard deviation of one

Why Is TensorFlow the Most Preferred Library in Deep Learning?

TensorFlow provides both C++ and Python APIs, making it easier to work on and has a faster compilation time compared to other Deep Learning libraries like Keras and PyTorch. TensorFlow supports both CPU and GPU computing devices.

What is Tensor in TensorFlow?

A tensor is a mathematical object represented as arrays of higher dimensions. Think of a n-D matrix. These arrays of data with different dimensions and ranks fed as input to the neural network are called “Tensors.”

What is the Computational Graph?

Everything in a TensorFlow is based on creating a computational graph. It has a network of nodes where each node operates. Nodes represent mathematical operations, and edges represent tensors. Since data flows in the form of a graph, it is also called a “DataFlow Graph.”

How is logistic regression done? 

Logistic regression measures the relationship between the dependent variable (our label of what we want to predict) and one or more independent variables (our features) by estimating probability using its underlying logistic function (sigmoid).

Explain the steps in making a decision tree. 

1. Take the entire data set as input
2. Calculate entropy of the target variable, as well as the predictor attributes
3. Calculate your information gain of all attributes (we gain information on sorting different objects from each other)
4. Choose the attribute with the highest information gain as the root node
5. Repeat the same procedure on every branch until the decision node of each branch is finalized
For example, let’s say you want to build a decision tree to decide whether you should accept or decline a job offer. The decision tree for this case is as shown:

It is clear from the decision tree that an offer is accepted if:
• Salary is greater than $50,000
• The commute is less than an hour
• Coffee is offered

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How do you build a random forest model?

A random forest is built up of a number of decision trees. If you split the data into different packages and make a decision tree in each of the different groups of data, the random forest brings all those trees together.

Steps to build a random forest model:

1. Randomly select ; features from a total of = features where  k<< m
2. Among the ; features, calculate the node D using the best split point
3. Split the node into daughter nodes using the best split
4. Repeat steps two and three until leaf nodes are finalized
5. Build forest by repeating steps one to four for > times to create > number of trees

Differentiate between univariate, bivariate, and multivariate analysis. 

Univariate data contains only one variable. The purpose of the univariate analysis is to describe the data and find patterns that exist within it.

The patterns can be studied by drawing conclusions using mean, median, mode, dispersion or range, minimum, maximum, etc.

Bivariate data involves two different variables. The analysis of this type of data deals with causes and relationships and the analysis is done to determine the relationship between the two variables.

Here, the relationship is visible from the table that temperature and sales are directly proportional to each other. The hotter the temperature, the better the sales.

Multivariate data involves three or more variables, it is categorized under multivariate. It is similar to a bivariate but contains more than one dependent variable.

Example: data for house price prediction
The patterns can be studied by drawing conclusions using mean, median, and mode, dispersion or range, minimum, maximum, etc. You can start describing the data and using it to guess what the price of the house will be.

What are the feature selection methods used to select the right variables?

There are two main methods for feature selection.
Filter Methods
This involves:
• Linear discrimination analysis
• ANOVA
• Chi-Square
The best analogy for selecting features is “bad data in, bad answer out.” When we’re limiting or selecting the features, it’s all about cleaning up the data coming in.

Wrapper Methods
This involves:
• Forward Selection: We test one feature at a time and keep adding them until we get a good fit
• Backward Selection: We test all the features and start removing them to see what works
better
• Recursive Feature Elimination: Recursively looks through all the different features and how they pair together

Wrapper methods are very labor-intensive, and high-end computers are needed if a lot of data analysis is performed with the wrapper method.

You are given a data set consisting of variables with more than 30 percent missing values. How will you deal with them? 

If the data set is large, we can just simply remove the rows with missing data values. It is the quickest way; we use the rest of the data to predict the values.

For smaller data sets, we can impute missing values with the mean, median, or average of the rest of the data using pandas data frame in python. There are different ways to do so, such as: df.mean(), df.fillna(mean)

Other option of imputation is using KNN for numeric or classification values (as KNN just uses k closest values to impute the missing value).

Q76: How will you calculate the Euclidean distance in Python?

plot1 = [1,3]

plot2 = [2,5]

The Euclidean distance can be calculated as follows:

euclidean_distance = sqrt((plot1[0]-plot2[0])**2 + (plot1[1]- plot2[1])**2)

What are dimensionality reduction and its benefits? 

Dimensionality reduction refers to the process of converting a data set with vast dimensions into data with fewer dimensions (fields) to convey similar information concisely.

This reduction helps in compressing data and reducing storage space. It also reduces computation time as fewer dimensions lead to less computing. It removes redundant features; for example, there’s no point in storing a value in two different units (meters and inches).

How should you maintain a deployed model?

The steps to maintain a deployed model are (CREM):

1. Monitor: constant monitoring of all models is needed to determine their performance accuracy.
When you change something, you want to figure out how your changes are going to affect things.
This needs to be monitored to ensure it’s doing what it’s supposed to do.
2. Evaluate: evaluation metrics of the current model are calculated to determine if a new algorithm is needed.
3. Compare: the new models are compared to each other to determine which model performs the best.
4. Rebuild: the best performing model is re-built on the current state of data.

How can a time-series data be declared as stationery?

  1. The mean of the series should not be a function of time.

  1. The variance of the series should not be a function of time. This property is known as homoscedasticity.

  1. The covariance of the i th term and the (i+m) th term should not be a function of time.

‘People who bought this also bought…’ recommendations seen on Amazon are a result of which algorithm?

The recommendation engine is accomplished with collaborative filtering. Collaborative filtering explains the behavior of other users and their purchase history in terms of ratings, selection, etc.
The engine makes predictions on what might interest a person based on the preferences of other users. In this algorithm, item features are unknown.
For example, a sales page shows that a certain number of people buy a new phone and also buy tempered glass at the same time. Next time, when a person buys a phone, he or she may see a recommendation to buy tempered glass as well.

What is a Generative Adversarial Network?

Suppose there is a wine shop purchasing wine from dealers, which they resell later. But some dealers sell fake wine. In this case, the shop owner should be able to distinguish between fake and authentic wine. The forger will try different techniques to sell fake wine and make sure specific techniques go past the shop owner’s check. The shop owner would probably get some feedback from wine experts that some of the wine is not original. The owner would have to improve how he determines whether a wine is fake or authentic.
The forger’s goal is to create wines that are indistinguishable from the authentic ones while the shop owner intends to tell if the wine is real or not accurately.

• There is a noise vector coming into the forger who is generating fake wine.
• Here the forger acts as a Generator.
• The shop owner acts as a Discriminator.
• The Discriminator gets two inputs; one is the fake wine, while the other is the real authentic wine.
The shop owner has to figure out whether it is real or fake.

So, there are two primary components of Generative Adversarial Network (GAN) named:
1. Generator
2. Discriminator

The generator is a CNN that keeps keys producing images and is closer in appearance to the real images while the discriminator tries to determine the difference between real and fake images. The ultimate aim is to make the discriminator learn to identify real and fake images.

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You are given a dataset on cancer detection. You have built a classification model and achieved an accuracy of 96 percent. Why shouldn’t you be happy with your model performance? What can you do about it?

Cancer detection results in imbalanced data. In an imbalanced dataset, accuracy should not be based as a measure of performance. It is important to focus on the remaining four percent, which represents the patients who were wrongly diagnosed. Early diagnosis is crucial when it comes to cancer detection and can greatly improve a patient’s prognosis.

Hence, to evaluate model performance, we should use Sensitivity (True Positive Rate), Specificity (True Negative Rate), F measure to determine the class wise performance of the classifier.

We want to predict the probability of death from heart disease based on three risk factors: age, gender, and blood cholesterol level. What is the most appropriate algorithm for this case?

The most appropriate algorithm for this case is logistic regression.

After studying the behavior of a population, you have identified four specific individual types that are valuable to your study. You would like to find all users who are most similar to each individual type. Which algorithm is most appropriate for this study? 

As we are looking for grouping people together specifically by four different similarities, it indicates the value of k. Therefore, K-means clustering is the most appropriate algorithm for this study.

You have run the association rules algorithm on your dataset, and the two rules {banana, apple} => {grape} and {apple, orange} => {grape} have been found to be relevant. What else must be true? 

{grape, apple} must be a frequent itemset.

Your organization has a website where visitors randomly receive one of two coupons. It is also possible that visitors to the website will not receive a coupon. You have been asked to determine if offering a coupon to website visitors has any impact on their purchase decisions. Which analysis method should you use?

One-way ANOVA: in statistics, one-way analysis of variance is a technique that can be used to compare means of two or more samples. This technique can be used only for numerical response data, the “Y”, usually one variable, and numerical or categorical input data, the “X”, always one variable, hence “oneway”.
The ANOVA tests the null hypothesis, which states that samples in all groups are drawn from populations with the same mean values. To do this, two estimates are made of the population variance. The ANOVA produces an F-statistic, the ratio of the variance calculated among the means to the variance within the samples. If the group means are drawn from populations with the same mean values, the variance between the group means should be lower than the variance of the samples, following the central limit
theorem. A higher ratio therefore implies that the samples were drawn from populations with different mean values.

What are the feature vectors?

A feature vector is an n-dimensional vector of numerical features that represent an object. In machine learning, feature vectors are used to represent numeric or symbolic characteristics (called features) of an object in a mathematical way that’s easy to analyze.

What is root cause analysis?

Root cause analysis was initially developed to analyze industrial accidents but is now widely used in other areas. It is a problem-solving technique used for isolating the root causes of faults or problems. A factor is called a root cause if its deduction from the problem-fault-sequence averts the final undesirable event from recurring.

Do gradient descent methods always converge to similar points?

They do not, because in some cases, they reach a local minimum or a local optimum point. You would not reach the global optimum point. This is governed by the data and the starting conditions.

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What are the different Deep Learning Frameworks?

PyTorch: PyTorch is an open source machine learning library based on the Torch library, used for applications such as computer vision and natural language processing, primarily developed by Facebook’s AI Research lab. It is free and open-source software released under the Modified BSD license.
TensorFlow: TensorFlow is a free and open-source software library for dataflow and differentiable programming across a range of tasks. It is a symbolic math library and is also used for machine learning applications such as neural networks. Licensed by Apache License 2.0. Developed by Google Brain Team.
Microsoft Cognitive Toolkit: Microsoft Cognitive Toolkit describes neural networks as a series of computational steps via a directed graph.
Keras: Keras is an open-source neural-network library written in Python. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, R, Theano, or PlaidML. Designed to enable fast experimentation with deep neural networks, it focuses on being user-friendly, modular, and extensible. Licensed by MIT.

What are the different Deep Learning Frameworks?

PyTorch: PyTorch is an open source machine learning library based on the Torch library, used for applications such as computer vision and natural language processing, primarily developed by Facebook’s AI Research lab. It is free and open-source software released under the Modified BSD license.
TensorFlow: TensorFlow is a free and open-source software library for dataflow and differentiable programming across a range of tasks. It is a symbolic math library and is also used for machine learning applications such as neural networks. Licensed by Apache License 2.0. Developed by Google Brain Team.
Microsoft Cognitive Toolkit: Microsoft Cognitive Toolkit describes neural networks as a series of computational steps via a directed graph.
Keras: Keras is an open-source neural-network library written in Python. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, R, Theano, or PlaidML. Designed to enable fast experimentation with deep neural networks, it focuses on being user-friendly, modular, and extensible. Licensed by MIT.

How Does an LSTM Network Work?

Reference: LTSM

Long-Short-Term Memory (LSTM) is a special kind of recurrent neural network capable of learning long-term dependencies, remembering information for long periods as its default behavior. There are three steps in an LSTM network:
• Step 1: The network decides what to forget and what to remember.
• Step 2: It selectively updates cell state values.
• Step 3: The network decides what part of the current state makes it to the output.

What Is a Multi-layer Perceptron (MLP)?

Reference: MLP

As in Neural Networks, MLPs have an input layer, a hidden layer, and an output layer. It has the same structure as a single layer perceptron with one or more hidden layers.

Perceptron is a single layer neural network and a multi-layer perceptron is called Neural Networks.
A (single layer) perceptron is a single layer neural network that works as a linear binary classifier. Being a single layer neural network, it can be trained without the use of more advanced algorithms like back propagation and instead can be trained by “stepping towards” your error in steps specified by a learning rate. When someone says perceptron, I usually think of the single layer version.

 

Machine Learning Multi-Layer Perceptron

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What is exploding gradients? 

https://machinelearningmastery.com/exploding-gradients-in-neural-networks/

While training an RNN, if you see exponentially growing (very large) error gradients which accumulate and result in very large updates to neural network model weights during training, they’re known as exploding gradients. At an extreme, the values of weights can become so large as to overflow and result in NaN values. The explosion occurs through exponential growth by repeatedly multiplying gradients through the network layers that have values larger than 1.0.
This has the effect of your model is unstable and unable to learn from your training data.
There are some subtle signs that you may be suffering from exploding gradients during the training of your network, such as:
• The model is unable to get traction on your training data (e.g. poor loss).
• The model is unstable, resulting in large changes in loss from update to update.
• The model loss goes to NaN during training.
• The model weights quickly become very large during training.
• The error gradient values are consistently above 1.0 for each node and layer during training.

Solutions
1. Re-Design the Network Model:
a. In deep neural networks, exploding gradients may be addressed by redesigning the
network to have fewer layers. There may also be some benefit in using a smaller batch
size while training the network.
b. In RNNs, updating across fewer prior time steps during training, called truncated
Backpropagation through time, may reduce the exploding gradient problem.

2. Use Long Short-Term Memory Networks: In RNNs, exploding gradients can be reduced by using the Long Short-Term Memory (LSTM) memory units and perhaps related gated-type neuron structures. Adopting LSTM memory units is a new best practice for recurrent neural networks for sequence prediction.

3. Use Gradient Clipping: Exploding gradients can still occur in very deep Multilayer Perceptron networks with a large batch size and LSTMs with very long input sequence lengths. If exploding gradients are still occurring, you can check for and limit the size of gradients during the training of your network. This is called gradient clipping. Specifically, the values of the error gradient are checked against a threshold value and clipped or set to that threshold value if the error gradient exceeds the threshold.

4. Use Weight Regularization: another approach, if exploding gradients are still occurring, is to check the size of network weights and apply a penalty to the networks loss function for large weight values. This is called weight regularization and often an L1 (absolute weights) or an L2 (squared weights) penalty can be used.

Machine Learning For Dummies

What is vanishing gradients? 

While training an RNN, your slope can become either too small; this makes the training difficult. When the slope is too small, the problem is known as a Vanishing Gradient. It leads to long training times, poor performance, and low accuracy.
• Hyperbolic tangent and Sigmoid/Soft-max suffer vanishing gradient.
• RNNs suffer vanishing gradient, LSTM no (so it is perfect to predict stock prices). In fact, the propagation of error through previous layers makes the gradient get smaller so the weights are not updated.

Solutions
1. Choose RELU
2. Use LSTM (for RNNs)
3. Use ResNet (Residual Network) → after some layers, add x again: F(x) → ⋯ → F(x) + x
4. Multi-level hierarchy: pre-train one layer at the time through unsupervised learning, then fine-tune via backpropagation
5. Gradient checking: debugging strategy used to numerically track and assess gradients during training.

What is Gradient Descent?

Let’s first explain what a gradient is. A gradient is a mathematical function. When calculated on a point of a function, it gives the hyperplane (or slope) of the directions in which the function increases more. The gradient vector can be interpreted as the “direction and rate of fastest increase”. If the gradient of a function is non-zero at a point p, the direction of the gradient is the direction in which the function increases most quickly from p, and the magnitude of the gradient is the rate of increase in that direction.
Further, the gradient is the zero vector at a point if and only if it is a stationary point (where the derivative vanishes).
In Data Science, it simply measures the change in all weights with regard to the change in error, as we are partially derivating by w the loss function.

Gradient descent is a first-order iterative optimization algorithm for finding the minimum of a function.

 

Machine Learning Gradient Descent

The goal of the gradient descent is to minimize a given function which, in our case, is the loss function of the neural network. To achieve this goal, it performs two steps iteratively.
1. Compute the slope (gradient) that is the first-order derivative of the function at the current point
2. Move-in the opposite direction of the slope increase from the current point by the computed amount
So, the idea is to pass the training set through the hidden layers of the neural network and then update the parameters of the layers by computing the gradients using the training samples from the training dataset.
Think of it like this. Suppose a man is at top of the valley and he wants to get to the bottom of the valley.
So, he goes down the slope. He decides his next position based on his current position and stops when he gets to the bottom of the valley which was his goal.

• Gradient descent is an iterative optimization algorithm that is popular and it is a base for many other optimization techniques, which tries to obtain minimal loss in a model by tuning the weights/parameters in the objective function.

• Types of Gradient Descent:

  1. Batch Gradient Descent
  2. Stochastic Gradient Descent
  3. Mini Batch Gradient Descent

• Steps to achieve minimal loss:

  1. The first stage in gradient descent is to pick a starting value (a starting point) for w1, which is set to 0 by many algorithms.
  2. The gradient descent algorithm then calculates the gradient of the loss curve at the starting point.
  3. The gradient always points in the direction of steepest increase in the loss function. The gradient descent algorithm takes a step in the direction of the negative gradient in order to reduce loss as quickly as possible.
  4. To determine the next point along the loss function curve, the gradient descent algorithm adds some fraction of the gradient’s magnitude to the starting point and moves forward.
  5. The gradient descent then repeats this process, edging ever closer to the minimum.

What is vanishing gradients? 

While training an RNN, your slope can become either too small; this makes the training difficult. When the slope is too small, the problem is known as a Vanishing Gradient. It leads to long training times, poor performance, and low accuracy.
• Hyperbolic tangent and Sigmoid/Soft-max suffer vanishing gradient.
• RNNs suffer vanishing gradient, LSTM no (so it is perfect to predict stock prices). In fact, the propagation of error through previous layers makes the gradient get smaller so the weights are not updated.

Solutions
1. Choose RELU
2. Use LSTM (for RNNs)
3. Use ResNet (Residual Network) → after some layers, add x again: F(x) → ⋯ → F(x) + x
4. Multi-level hierarchy: pre-train one layer at the time through unsupervised learning, then fine-tune via backpropagation
5. Gradient checking: debugging strategy used to numerically track and assess gradients during training.

What is Back Propagation and Explain it Works. 

Back propagation is a training algorithm used for neural network. In this method, we update the weights of each layer from the last layer recursively, with the formula:

Machine Learning Back Propagation Formula

It has the following steps:
• Forward Propagation of Training Data (initializing weights with random or pre-assigned values)
• Gradients are computed using output weights and target
• Back Propagate for computing gradients of error from output activation
• Update the Weights

What are the variants of Back Propagation? 

Reference: Variants of back propagation

  • Stochastic Gradient Descent: In Batch Gradient Descent we were considering all the examples for every step of Gradient Descent. But what if our dataset is very huge. Deep learning models crave for data. The more the data the more chances of a model to be good. Suppose our dataset has 5 million examples, then just to take one step the model will have to calculate the gradients of all the 5 million examples. This does not seem an efficient way. To tackle this problem, we have Stochastic Gradient Descent. In Stochastic Gradient Descent (SGD), we consider just one example at a time to take a single step. We do the following steps in one epoch for SGD:
    1. Take an example
    2. Feed it to Neural Network
    3. Calculate its gradient
    4. Use the gradient we calculated in step 3 to update the weights
    5. Repeat steps 1–4 for all the examples in training dataset
    Since we are considering just one example at a time the cost will fluctuate over the training examples and it will not necessarily decrease. But in the long run, you will see the cost decreasing with fluctuations. Also, because the cost is so fluctuating, it will never reach the minimum, but it will keep dancing around it. SGD can be used for larger datasets. It converges faster when the dataset is large as it causes updates to the parameters more frequently.

 Stochastic Gradient Descent (SGD)

 

Stochastic Gradient Descent (SGD)

  • Batch Gradient Descent: all the training data is taken into consideration to take a single step. We take the average of the gradients of all the training examples and then use that mean gradient to update our parameters. So that’s just one step of gradient descent in one epoch. Batch Gradient Descent is great for convex or relatively smooth error manifolds. In this case, we move somewhat directly towards an optimum solution. The graph of cost vs epochs is also quite smooth because we are averaging over all the gradients of training data for a single step. The cost keeps on decreasing over the epochs.

 

Batch Gradient Descent

  • Mini-batch Gradient Descent: It’s one of the most popular optimization algorithms. It’s a variant of Stochastic Gradient Descent and here instead of single training example, mini batch of samples is used. Batch Gradient Descent can be used for smoother curves. SGD can be used when the dataset is large. Batch Gradient Descent converges directly to minima. SGD converges faster for larger datasets.
    But, since in SGD we use only one example at a time, we cannot implement the vectorized implementation on it. This can slow down the computations. To tackle this problem, a mixture of Batch Gradient Descent and SGD is used. Neither we use all the dataset all at once nor we use the single example at a time. We use a batch of a fixed number of training examples which is less than the actual dataset and call it a mini-batch. Doing this helps us achieve the advantages of both the former variants we saw. So, after creating the mini-batches of fixed size, we do the following steps in one epoch:
    1. Pick a mini-batch
    2. Feed it to Neural Network
    3. Calculate the mean gradient of the mini-batch
    4. Use the mean gradient we calculated in step 3 to update the weights
    5. Repeat steps 1–4 for the mini-batches we created
    Just like SGD, the average cost over the epochs in mini-batch gradient descent fluctuates because we are averaging a small number of examples at a time. So, when we are using the mini-batch gradient descent we are updating our parameters frequently as well as we can use vectorized implementation for faster computations.

While we continue to integrate ML systems in high-stakes environments such as medical settings, roads, command control centers, we need to ensure they do not cause the loss of life. How can you handle this?

By focusing on the following, which includes everything outside of just developing SOTA models, as well inclusion of key stakeholders.

🔹Robustness: Create models that are resilient to adversaries, unusual situations, and Black Swan events

🔹Monitoring: Detect malicious use, monitor predictions, and discover unexpected model functionality

🔹Alignment: Build models that represent and safely optimize hard-to-specify human values

🔹External Safety: Use ML to address risks to how ML systems are handled, such as cyber attacks

Machine Learning Unsolved Problems_ n_Safety

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You are given a data set. The data set has missing values that spread along 1 standard deviation from the median. What percentage of data would remain unaffected? Why?

Since the data is spread across the median, let’s assume it’s a normal distribution. We know, in a normal distribution, ~68% of the data lies in 1 standard deviation from mean (or mode, median), which leaves ~32% of the data unaffected. Therefore, ~32% of the data would remain unaffected by missing values.

What are PCA, KPCA, and ICA used for?

PCA (Principal Components Analysis), KPCA ( Kernel-based Principal Component Analysis) and ICA ( Independent Component Analysis) are important feature extraction techniques used for dimensionality reduction.

What is the bias-variance decomposition of classification error in the ensemble method?

The expected error of a learning algorithm can be decomposed into bias and variance. A bias term measures how closely the average classifier produced by the learning algorithm matches the target function. The variance term measures how much the learning algorithm’s prediction fluctuates for different training sets.

When is Ridge regression favorable over Lasso regression?

You can quote ISLR’s authors Hastie, Tibshirani who asserted that, in the presence of few variables with medium / large sized effect, use lasso regression. In presence of many variables with small/medium-sized effects, use ridge regression.
Conceptually, we can say, lasso regression (L1) does both variable selection and parameter shrinkage, whereas Ridge regression only does parameter shrinkage and end up including all the coefficients in the model. In the presence of correlated variables, ridge regression might be the preferred choice. Also, ridge regression works best in situations where the least square estimates have higher variance. Therefore, it depends on our model objective.

You’ve built a random forest model with 10000 trees. You got delighted after getting training error as 0.00. But, the validation error is 34.23. What is going on? Haven’t you trained your model perfectly?

The model has overfitted. Training error 0.00 means the classifier has mimicked the training data patterns to an extent, that they are not available in the unseen data. Hence, when this classifier was run on an unseen sample, it couldn’t find those patterns and returned predictions with higher error. In a random forest, it happens when we use a larger number of trees than necessary. Hence, to avoid this situation, we should tune the number of trees using cross-validation.

What is a convex hull?

In the case of linearly separable data, the convex hull represents the outer boundaries of the two groups of data points. Once the convex hull is created, we get maximum margin hyperplane (MMH) as a perpendicular bisector between two convex hulls. MMH is the line which attempts to create the greatest separation between two groups.

What do you understand by Type I vs Type II error?

Type I error is committed when the null hypothesis is true and we reject it, also known as a ‘False Positive’. Type II error is committed when the null hypothesis is false and we accept it, also known as ‘False Negative’.
In the context of the confusion matrix, we can say Type I error occurs when we classify a value as positive (1) when it is actually negative (0). Type II error occurs when we classify a value as negative (0) when it is actually positive(1).

In k-means or kNN, we use euclidean distance to calculate the distance between nearest neighbors. Why not manhattan distance?

We don’t use manhattan distance because it calculates distance horizontally or vertically only. It has dimension restrictions. On the other hand, the euclidean metric can be used in any space to calculate distance. Since the data points can be present in any dimension, euclidean distance is a more viable option.

Example: Think of a chessboard, the movement made by a bishop or a rook is calculated by manhattan distance because of their respective vertical & horizontal movements.

Do you suggest that treating a categorical variable as a continuous variable would result in a better predictive model?

For better predictions, the categorical variable can be considered as a continuous variable only when the variable is ordinal in nature.

OLS is to linear regression what the maximum likelihood is logistic regression. Explain the statement.

OLS and Maximum likelihood are the methods used by the respective regression methods to approximate the unknown parameter (coefficient) value. In simple words, Ordinary least square(OLS) is a method used in linear regression which approximates the parameters resulting in minimum distance between actual and predicted values. Maximum
Likelihood helps in choosing the values of parameters which maximizes the likelihood that the parameters are most likely to produce observed data.

When does regularization becomes necessary in Machine Learning?

Regularization becomes necessary when the model begins to overfit/underfit. This technique introduces a cost term for bringing in more features with the objective function. Hence, it tries to push the coefficients for many variables to zero and hence reduce the cost term. This helps to reduce model complexity so that the model can become better at predicting (generalizing).

What is Linear Regression?

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Linear Regression is a supervised Machine Learning algorithm. It is used to find the linear relationship between the dependent and the independent variables for predictive analysis.

• Linear regression assumes that the relationship between the features and the target vector is approximately linear. That is, the effect of the features on the target vector is constant.

• In linear regression, the target variable y is assumed to follow a linear function of one or more predictor variables plus some random error. The machine learning task is to estimate the parameters of this equation which can be achieved in two ways:

• The first approach is through the lens of minimizing loss. A common practice in machine learning is to choose a loss function that defines how well a model with a given set of parameters estimates the observed data. The most common loss function for linear regression is squared error loss.

• The second approach is through the lens of maximizing the likelihood. Another common practice in machine learning is to model the target as a random variable whose distribution depends on one or more parameters, and then find the parameters that maximize its likelihood.

No alternative text description for this image

Credit: Vikram K.

image

What is the Variance Inflation Factor?

Variance Inflation Factor (VIF) is the estimate of the volume of multicollinearity in a collection of many regression variables.
VIF = Variance of the model / Variance of the model with a single independent variable
We have to calculate this ratio for every independent variable. If VIF is high, then it shows the high collinearity of the independent variables.

We know that one hot encoding increases the dimensionality of a dataset, but label encoding doesn’t. How?

When we use one-hot encoding, there is an increase in the dimensionality of a dataset. The reason for the increase in dimensionality is that, for every class in the categorical variables, it forms a different variable.

What is a Decision Tree?

A decision tree is used to explain the sequence of actions that must be performed to get the desired output. It is a hierarchical diagram that shows the actions.

What is the Binarizing of data? How to Binarize?

In most of the Machine Learning Interviews, apart from theoretical questions, interviewers focus on the implementation part. So, this ML Interview Questions focused on the implementation of the theoretical concepts.
Converting data into binary values on the basis of threshold values is known as the binarizing of data. The values that are less than the threshold are set to 0 and the values that are greater than the threshold are set to 1.
This process is useful when we have to perform feature engineering, and we can also use it for adding unique features.

Machine Learning For Dummies
Machine Learning For Dummies

What is cross-validation?

Cross-validation is essentially a technique used to assess how well a model performs on a new independent dataset. The simplest example of cross-validation is when you split your data into two groups: training data and testing data, where you use the training data to build the model and the testing data to test the model.

• Cross-validation is a resampling procedure used to evaluate machine learning models on a limited data sample. The procedure has a single parameter called k that refers to the number of groups that a given data sample is to be split into. As such, the procedure is often called k-fold cross-validation.

• Cross-validation is primarily used in applied machine learning to estimate the skill of a machine learning model on unseen data. That is, to use a limited sample in order to estimate how the model is expected to perform in general when used to make predictions on data not used during the training of the model.

• It is a popular method because it is simple to understand and because it generally results in a less biased or less optimistic estimate of the model skill than other methods, such as a simple train/test split.

• Procedure for K-Fold Cross Validation:
1. Shuffle the dataset randomly.
2. Split the dataset into k groups

3. For each unique group:
a. Take the group as a holdout or test data set
b. Take the remaining groups as a training data set
c. Fit a model on the training set and evaluate it on the test set
d. Retain the evaluation score and discard the model

4. Summarize the skill of the model using the sample of model evaluation scores

No alternative text description for this image

Credit: Vikram K.

When would you use random forests Vs SVM and why?

There are a couple of reasons why a random forest is a better choice of the model than a support vector machine:
● Random forests allow you to determine the feature importance. SVM’s can’t do this.
● Random forests are much quicker and simpler to build than an SVM.
● For multi-class classification problems, SVMs require a one-vs-rest method, which is less scalable and more memory intensive.

What are the drawbacks of a linear model?

There are a couple of drawbacks of a linear model:
● A linear model holds some strong assumptions that may not be true in the application. It assumes a linear relationship, multivariate normality, no or little multicollinearity, no auto-correlation, and homoscedasticity
● A linear model can’t be used for discrete or binary outcomes.
● You can’t vary the model flexibility of a linear model.

 

While we continue to integrate ML systems in high-stakes environments such as medical settings, roads, command control centers, we need to ensure they do not cause the loss of life. How can you handle this?

By focusing on the following, which includes everything outside of just developing SOTA models, as well inclusion of key stakeholders.

🔹Robustness: Create models that are resilient to adversaries, unusual situations, and Black Swan events

🔹Monitoring: Detect malicious use, monitor predictions, and discover unexpected model functionality

🔹Alignment: Build models that represent and safely optimize hard-to-specify human values

🔹External Safety: Use ML to address risks to how ML systems are handled, such as cyber attacks

Machine Learning Unsolved Problems_ n_Safety

Download Unsolved Problems in ML Safety Here

You are given a data set. The data set has missing values that spread along 1 standard deviation from the median. What percentage of data would remain unaffected? Why?

Since the data is spread across the median, let’s assume it’s a normal distribution. We know, in a normal distribution, ~68% of the data lies in 1 standard deviation from mean (or mode, median), which leaves ~32% of the data unaffected. Therefore, ~32% of the data would remain unaffected by missing values.

Machine Learning For Dummies  on iOs

Machine Learning For Dummies on Windows

Machine Learning For Dummies Web/Android 

#MachineLearning #AI #ArtificialIntelligence #ML #MachineLearningForDummies #MLOPS #NLP #ComputerVision #AWSMachineLEarning #AzureAI #GCPML

Machine Learning For Dummies
Machine Learning For Dummies

What are PCA, KPCA, and ICA used for?

PCA (Principal Components Analysis), KPCA ( Kernel-based Principal Component Analysis) and ICA ( Independent Component Analysis) are important feature extraction techniques used for dimensionality reduction.

What are support vector machines?

 

Support vector machines are supervised learning algorithms used for classification and regression analysis.

What is batch statistical learning?

Statistical learning techniques allow learning a function or predictor from a set of observed data that can make predictions about unseen or future data. These techniques provide guarantees on the performance of the learned predictor on the future unseen data based on a statistical assumption on the data generating process.

What is the bias-variance decomposition of classification error in the ensemble method?

The expected error of a learning algorithm can be decomposed into bias and variance. A bias term measures how closely the average classifier produced by the learning algorithm matches the target function. The variance term measures how much the learning algorithm’s prediction fluctuates for different training sets.

When is Ridge regression favorable over Lasso regression?

You can quote ISLR’s authors Hastie, Tibshirani who asserted that, in the presence of few variables with medium / large sized effect, use lasso regression. In presence of many variables with small/medium-sized effects, use ridge regression.
Conceptually, we can say, lasso regression (L1) does both variable selection and parameter shrinkage, whereas Ridge regression only does parameter shrinkage and end up including all the coefficients in the model. In the presence of correlated variables, ridge regression might be the preferred choice. Also, ridge regression works best in situations where the least square estimates have higher variance. Therefore, it depends on our model objective.

You’ve built a random forest model with 10000 trees. You got delighted after getting training error as 0.00. But, the validation error is 34.23. What is going on? Haven’t you trained your model perfectly?

The model has overfitted. Training error 0.00 means the classifier has mimicked the training data patterns to an extent, that they are not available in the unseen data. Hence, when this classifier was run on an unseen sample, it couldn’t find those patterns and returned predictions with higher error. In a random forest, it happens when we use a larger number of trees than necessary. Hence, to avoid this situation, we should tune the number of trees using cross-validation.

What is a convex hull?

In the case of linearly separable data, the convex hull represents the outer boundaries of the two groups of data points. Once the convex hull is created, we get maximum margin hyperplane (MMH) as a perpendicular bisector between two convex hulls. MMH is the line which attempts to create the greatest separation between two groups.

What do you understand by Type I vs Type II error?

Type I error is committed when the null hypothesis is true and we reject it, also known as a ‘False Positive’. Type II error is committed when the null hypothesis is false and we accept it, also known as ‘False Negative’.
In the context of the confusion matrix, we can say Type I error occurs when we classify a value as positive (1) when it is actually negative (0). Type II error occurs when we classify a value as negative (0) when it is actually positive(1).

In k-means or kNN, we use euclidean distance to calculate the distance between nearest neighbors. Why not manhattan distance?

We don’t use manhattan distance because it calculates distance horizontally or vertically only. It has dimension restrictions. On the other hand, the euclidean metric can be used in any space to calculate distance. Since the data points can be present in any dimension, euclidean distance is a more viable option.

Example: Think of a chessboard, the movement made by a bishop or a rook is calculated by manhattan distance because of their respective vertical & horizontal movements.

Do you suggest that treating a categorical variable as a continuous variable would result in a better predictive model?

For better predictions, the categorical variable can be considered as a continuous variable only when the variable is ordinal in nature.

OLS is to linear regression wha the maximum likelihood is logistic regression. Explain the statement.

OLS and Maximum likelihood are the methods used by the respective regression methods to approximate the unknown parameter (coefficient) value. In simple words, Ordinary least square(OLS) is a method used in linear regression which approximates the parameters resulting in minimum distance between actual and predicted values. Maximum
Likelihood helps in choosing the values of parameters which maximizes the likelihood that the parameters are most likely to produce observed data.

When does regularization becomes necessary in Machine Learning?

Regularization becomes necessary when the model begins to overfit/underfit. This technique introduces a cost term for bringing in more features with the objective function. Hence, it tries to push the coefficients for many variables to zero and hence reduce the cost term. This helps to reduce model complexity so that the model can become better at predicting (generalizing).

Machine Learning For Dummies  on iOs

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What is Linear Regression?

Linear Regression is a supervised Machine Learning algorithm. It is used to find the linear relationship between the dependent and the independent variables for predictive analysis.

What is the Variance Inflation Factor?

Variance Inflation Factor (VIF) is the estimate of the volume of multicollinearity in a collection of many regression variables.
VIF = Variance of the model / Variance of the model with a single independent variable
We have to calculate this ratio for every independent variable. If VIF is high, then it shows the high collinearity of the independent variables.

We know that one hot encoding increases the dimensionality of a dataset, but label encoding doesn’t. How?

When we use one-hot encoding, there is an increase in the dimensionality of a dataset. The reason for the increase in dimensionality is that, for every class in the categorical variables, it forms a different variable.

What is a Decision Tree?

A decision tree is used to explain the sequence of actions that must be performed to get the desired output. It is a hierarchical diagram that shows the actions.

What is the Binarizing of data? How to Binarize?

In most of the Machine Learning Interviews, apart from theoretical questions, interviewers focus on the implementation part. So, this ML Interview Questions focused on the implementation of the theoretical concepts.
Converting data into binary values on the basis of threshold values is known as the binarizing of data. The values that are less than the threshold are set to 0 and the values that are greater than the threshold are set to 1.
This process is useful when we have to perform feature engineering, and we can also use it for adding unique features.

What is cross-validation?

Cross-validation is essentially a technique used to assess how well a model performs on a new independent dataset. The simplest example of cross-validation is when you split your data into two groups: training data and testing data, where you use the training data to build the model and the testing data to test the model.

When would you use random forests Vs SVM and why?

There are a couple of reasons why a random forest is a better choice of the model than a support vector machine:
● Random forests allow you to determine the feature importance. SVM’s can’t do this.
● Random forests are much quicker and simpler to build than an SVM.
● For multi-class classification problems, SVMs require a one-vs-rest method, which is less scalable and more memory intensive.

What are the drawbacks of a linear model?

There are a couple of drawbacks of a linear model:
● A linear model holds some strong assumptions that may not be true in the application. It assumes a linear relationship, multivariate normality, no or little multicollinearity, no auto-correlation, and homoscedasticity
● A linear model can’t be used for discrete or binary outcomes.
● You can’t vary the model flexibility of a linear model.

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Do you think 50 small decision trees are better than a large one? Why?

Another way of asking this question is “Is a random forest a better model than a decision tree?”
And the answer is yes because a random forest is an ensemble method that takes many weak decision trees to make a strong learner. Random forests are more accurate, more robust, and less prone to overfitting. 

What is a kernel? Explain the kernel trick

A kernel is a way of computing the dot product of two vectors x and ᫣y in some (possibly very high dimensional) feature space, which is why kernel functions are sometimes called “generalized dot product”
The kernel trick is a method of using a linear classifier to solve a non-linear problem by transforming linearly inseparable data to linearly separable ones in a higher dimension.

State the differences between causality and correlation?

Causality applies to situations where one action, say X, causes an outcome, say Y, whereas Correlation is just relating one action (X) to another action(Y) but X does not necessarily cause Y.

What is the exploding gradient problem while using the backpropagation technique?

When large error gradients accumulate and result in large changes in the neural network weights during training, it is called the exploding gradient problem. The values of weights can become so large as to overflow and result in NaN values. This makes the model unstable and the learning of the model to stall just like the vanishing gradient problem.

What do you mean by Associative Rule Mining (ARM)?

Associative Rule Mining is one of the techniques to discover patterns in data like features (dimensions) which occur together and features (dimensions) which are correlated.

What is Marginalization? Explain the process.

Marginalization is summing the probability of a random variable X given the joint probability distribution of X with other variables. It is an application of the law of total probability.

Why is the rotation of components so important in Principle Component Analysis(PCA)?

Rotation in PCA is very important as it maximizes the separation within the variance obtained by all the components because of which interpretation of components would become easier. If the components are not rotated, then we need extended components to describe the variance of the components.

What is the difference between regularization and normalization?

Normalization adjusts the data; regularization adjusts the prediction function. If your data is on very different scales (especially low to high), you would want to normalize the data. Alter each column to have compatible basic statistics. This can be helpful to make sure there is no loss of accuracy. One of the goals of model training is to identify the signal and ignore the noise if the model is given free rein to minimize error, there is a possibility of suffering from overfitting.
Regularization imposes some control on this by providing simpler fitting functions over complex ones.

How does the SVM algorithm deal with self-learning?

SVM has a learning rate and expansion rate which takes care of this. The learning rate compensates or penalizes the hyperplanes for making all the wrong moves and expansion rate deals with finding the maximum separation area between classes.

How do you handle outliers in the data?

Outlier is an observation in the data set that is far away from other observations in the data set.
We can discover outliers using tools and functions like box plot, scatter plot, Z-Score, IQR score etc. and then handle them based on the visualization we have got. To handle outliers, we can cap at some threshold, use transformations to reduce skewness of the data and remove outliers if they are anomalies or errors.

What are some techniques used to find similarities in the recommendation system?

 

Pearson correlation and Cosine correlation are techniques used to find similarities in recommendation systems.

Why would you Prune your tree?

In the context of data science or AIML, pruning refers to the process of reducing redundant branches of a decision tree. Decision Trees are prone to overfitting, pruning the tree helps to reduce the size and minimizes the chances of overfitting. Pruning involves turning branches of a decision tree into leaf nodes and removing the leaf nodes from the original branch. It serves as a tool to perform the tradeoff.

What are some of the EDA Techniques?

Exploratory Data Analysis (EDA) helps analysts to understand the data better and forms the foundation of better models.
Visualization
● Univariate visualization
● Bivariate visualization
● Multivariate visualization
Missing Value Treatment – Replace missing values with Either Mean/Median Outlier Detection – Use Boxplot to identify the distribution of Outliers, then Apply IQR to set the boundary for IQR

What is data augmentation?

 

Data augmentation is a technique for synthesizing new data by modifying existing data in such a way that the target is not changed, or it is changed in a known way.
CV is one of the fields where data augmentation is very useful. There are many modifications that we can do to images:
● Resize
● Horizontal or vertical flip
● Rotate
● Add noise
● Deform
● Modify colors
Each problem needs a customized data augmentation pipeline. For example, on OCR, doing flips will change the text and won’t be beneficial; however, resizes and small rotations may help.

What is Inductive Logic Programming in Machine Learning (ILP)?

Inductive Logic Programming (ILP) is a subfield of machine learning which uses logic programming representing background knowledge and examples.

What is the difference between inductive machine learning and deductive machine learning?

The difference between inductive machine learning and deductive machine learning are as follows: machine-learning where the model learns by examples from a set of observed instances to draw a generalized conclusion whereas in deductive learning the model first draws the conclusion and then the conclusion is drawn.

What is the Difference between machine learning and deep learning?

 

Machine learning is a branch of computer science and a method to implement artificial intelligence. This technique provides the ability to automatically learn and improve from experiences without being explicitly programmed.
Deep learning can be said as a subset of machine learning. It is mainly based on the artificial neural network where data is taken as an input and the technique makes intuitive decisions using the artificial neural network.

What Are The Steps Involved In Machine Learning Project?

As you plan for doing a machine learning project. There are several important steps you must follow to achieve a good working model and they are data collection, data preparation, choosing a machine learning model, training the model, model evaluation, parameter tuning and lastly prediction.

What are Differences between Artificial Intelligence and Machine Learning?

Artificial intelligence is a broader prospect than machine learning. Artificial intelligence mimics the cognitive functions of the human brain. The purpose of AI is to carry out a task in an intelligent manner based on algorithms. On the other hand, machine learning is a subclass of artificial intelligence. To develop an autonomous machine in such a way so that it can learn without being explicitly programmed is the goal of machine learning.

What are the steps Needed to choose the Appropriate Machine Learning Algorithm for your Classification problem?

Firstly, you need to have a clear picture of your data, your constraints, and your problems before heading towards different machine learning algorithms. Secondly, you have to understand which type and kind of data you have because it plays a primary role in deciding which algorithm you have to use.

Following this step is the data categorization step, which is a two-step process – categorization by input and categorization by output. The next step is to understand your constraints; that is, what is your data storage capacity? How fast the prediction has to be? etc.

Finally, find the available machine learning algorithms and implement them wisely. Along with that, also try to optimize the hyperparameters which can be done in three ways – grid search, random search, and Bayesian optimization.

What is the Convex Function?

A convex function is a continuous function, and the value of the midpoint at every interval in its given domain is less than the numerical mean of the values at the two ends of the interval.

What’s the Relationship between True Positive Rate and Recall?

The True positive rate in machine learning is the percentage of the positives that have been properly acknowledged, and recall is just the count of the results that have been correctly identified and are relevant. Therefore, they are the same things, just having different names. It is also known as sensitivity.

What are some tools for parallelizing Machine Learning Algorithms?

Almost all machine learning algorithms are easy to serialize. Some of the basic tools for parallelizing are Matlab, Weka, R, Octave, or the Python-based sci-kit learn.

What is meant by Genetic Programming?

Genetic Programming (GP) is almost similar to an Evolutionary Algorithm, a subset of machine learning. Genetic programming software systems implement an algorithm that uses random mutation, a fitness function, crossover, and multiple generations of evolution to resolve a user-defined task. The genetic programming model is based on testing and choosing the best option among a set of results.

What is meant by Bayesian Networks?

Bayesian Networks also referred to as ‘belief networks’ or ‘casual networks’, are used to represent the graphical model for probability relationship among a set of variables.
For example, a Bayesian network can be used to represent the probabilistic relationships between diseases and symptoms. As per the symptoms, the network can also compute the probabilities of the presence of various diseases.
Efficient algorithms can perform inference or learning in Bayesian networks. Bayesian networks which relate the variables (e.g., speech signals or protein sequences) are called dynamic Bayesian networks.

 

Which are the two components of the Bayesian logic program?

A Bayesian logic program consists of two components:
● Logical It contains a set of Bayesian Clauses, which capture the qualitative structure of the domain.
● Quantitative It is used to encode quantitative information about the domain.

How is machine learning used in day-to-day life?

Most of the people are already using machine learning in their everyday life. Assume that you are engaging with the internet, you are actually expressing your preferences, likes, dislikes through your searches. All these things are picked up by cookies coming on your computer, from this, the behavior of a user is evaluated. It helps to increase the progress of a user through the internet and provide similar suggestions.
The navigation system can also be considered as one of the examples where we are using machine learning to calculate a distance between two places using optimization techniques.

What is Sampling. Why do we need it?

Sampling is a process of choosing a subset from a target population that would serve as its representative. We use the data from the sample to understand the pattern in the community as a whole. Sampling is necessary because often, we can not gather or process the complete data within a reasonable time.

What does the term decision boundary mean?

A decision boundary or a decision surface is a hypersurface which divides the underlying feature space into two subspaces, one for each class. If the decision boundary is a hyperplane, then the classes are linearly separable.

Define entropy?

Entropy is the measure of uncertainty associated with random variable Y. It is the expected number of bits required to communicate the value of the variable.

Indicate the top intents of machine learning?

The top intents of machine learning are stated below,
● The system gets information from the already established computations to give well-founded decisions and outputs.
● It locates certain patterns in the data and then makes certain predictions on it to provide answers on matters.

Highlight the differences between the Generative model and the Discriminative model?

The aim of the Generative model is to generate new samples from the same distribution and new data instances, Whereas, the Discriminative model highlights the differences between different kinds of data instances. It tries to learn directly from the data and then classifies the data.

Identify the most important aptitudes of a machine learning engineer?

Machine learning allows the computer to learn itself without being decidedly programmed. It helps the system to learn from experience and then improve from its mistakes. The intelligence system, which is based on machine learning, can learn from recorded data and past incidents.
In-depth knowledge of statistics, probability, data modelling, programming language, as well as CS, Application of ML Libraries and algorithms, and software design is required to become a successful machine learning engineer.

What is feature engineering? How do you apply it in the process of modelling?

Feature engineering is the process of transforming raw data into features that better represent the underlying problem to the predictive models, resulting in improved model accuracy on unseen data.

How can learning curves help create a better model?

Learning curves give the indication of the presence of overfitting or underfitting. In a learning curve, the training error and cross-validating error are plotted against the number of training data points.

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Perception: Vision, Audio, Speech, Natural Language

NLP: TF-IDF helps you to establish what?

TFIDF helps to establish how important a particular word is in the context of the document corpus. TF-IDF takes into account the number of times the word appears in the document and offset by the number of documents that appear in the corpus.
– TF is the frequency of term divided by a total number of terms in the document.
– IDF is obtained by dividing the total number of documents by the number of documents containing the term and then taking the logarithm of that quotient.
– Tf.idf is then the multiplication of two values TF and IDF
 

List 10 use cases to be solved using NLP techniques?

● Sentiment Analysis
● Language Translation (English to German, Chinese to English, etc..)
● Document Summarization
● Question Answering
● Sentence Completion
● Attribute extraction (Key information extraction from the documents)
● Chatbot interactions
● Topic classification
● Intent extraction
● Grammar or Sentence correction
● Image captioning
● Document Ranking
● Natural Language inference

Which NLP model gives the best accuracy amongst the following: BERT, XLNET, GPT-2, ELMo

XLNET has given best accuracy amongst all the models. It has outperformed BERT on 20 tasks and achieves state of art results on 18 tasks including sentiment analysis, question answering, natural language inference, etc.

What is Naive Bayes algorithm, When we can use this algorithm in NLP?

Naive Bayes algorithm is a collection of classifiers which works on the principles of the Bayes’theorem. This series of NLP model forms a family of algorithms that can be used for a wide range of classification tasks including sentiment prediction, filtering of spam, classifying documents and more.
Naive Bayes algorithm converges faster and requires less training data. Compared to other discriminative models like logistic regression, Naive Bayes model  takes lesser time to train. This algorithm is perfect for use while working with multiple classes and text classification where the data is dynamic and changes frequently.

Explain Dependency Parsing in NLP?

Dependency Parsing, also known as Syntactic parsing in NLP is a process of assigning syntactic structure to a sentence and identifying its dependency parses. This process is crucial to understand the correlations between the “head” words in the syntactic structure.
The process of dependency parsing can be a little complex considering how any sentence can have more than one dependency parses. Multiple parse trees are known as ambiguities.
Dependency parsing needs to resolve these ambiguities in order to effectively assign a syntactic structure to a sentence.
Dependency parsing can be used in the semantic analysis of a sentence apart from the syntactic structuring.

What is text Summarization?

Text summarization is the process of shortening a long piece of text with its meaning and effect intact. Text summarization intends to create a summary of any given piece of text and outlines the main points of the document. This technique has improved in recent times and is capable of summarizing volumes of text successfully.
Text summarization has proved to a blessing since machines can summarize large volumes of text in no time which would otherwise be really time-consuming. There are two types of text summarization:
● Extraction-based summarization
● Abstraction-based summarization

What is NLTK? How is it different from Spacy?

NLTK or Natural Language Toolkit is a series of libraries and programs that are used for symbolic and statistical natural language processing. This toolkit contains some of the most powerful libraries that can work on different ML techniques to break down and understand human language. NLTK is used for Lemmatization, Punctuation, Character count, Tokenization, and Stemming.
The difference between NLTK and Spacey are as follows:
● While NLTK has a collection of programs to choose from, Spacey contains only the best suited algorithm for a problem in its toolkit
● NLTK supports a wider range of languages compared to Spacey (Spacey supports only 7 languages)
● While Spacey has an object-oriented library, NLTK has a string processing library
● Spacey can support word vectors while NLTK cannot

What is information extraction?

Information extraction in the context of Natural Language Processing refers to the technique of extracting structured information automatically from unstructured sources to ascribe meaning to it. This can include extracting information regarding attributes of entities, relationship between different entities and more. The various models of information extraction includes:
● Tagger Module
● Relation Extraction Module
● Fact Extraction Module
● Entity Extraction Module
● Sentiment Analysis Module
● Network Graph Module
● Document Classification & Language Modeling Module

What is Bag of Words?

Bag of Words is a commonly used model that depends on word frequencies or occurrences to train a classifier. This model creates an occurrence matrix for documents or sentences irrespective of its grammatical structure or word order.

What is Pragmatic Ambiguity in NLP?

Pragmatic ambiguity refers to those words which have more than one meaning and their use in any sentence can depend entirely on the context. Pragmatic ambiguity can result in multiple interpretations of the same sentence. More often than not, we come across sentences which have words with multiple meanings, making the sentence open to interpretation. This multiple interpretation causes ambiguity and is known as Pragmatic ambiguity in NLP.

What is a Masked Language Model?

Masked language models help learners to understand deep representations in downstream tasks by taking an output from the corrupt input. This model is often used to predict the words to be used in a sentence.

What are the best NLP Tools?

Some of the best NLP tools from open sources are:
● SpaCy
● TextBlob
● Textacy
● Natural language Toolkit
● Retext
● NLP.js
● Stanford NLP
● CogcompNLP

What is POS tagging?

Parts of speech tagging better known as POS tagging refers to the process of identifying specific words in a document and group them as part of speech, based on its context. POS tagging is also known as grammatical tagging since it involves understanding grammatical structures and identifying the respective component.
POS tagging is a complicated process since the same word can be different parts of speech depending on the context. The same generic process used for word mapping is quite ineffective for POS tagging because of the same reason.

What is NES?

Name entity recognition is more commonly known as NER is the process of identifying specific entities in a text document which are more informative and have a unique context. These often denote places, people, organizations, and more. Even though it seems like these entities are proper nouns, the NER process is far from identifying just the nouns. In fact, NER involves entity
chunking or extraction wherein entities are segmented to categorize them under different predefined classes. This step further helps in extracting information.

Explain the Masked Language Model?

Masked language modelling is the process in which the output is taken from the corrupted input.
This model helps the learners to master the deep representations in downstream tasks. You can predict a word from the other words of the sentence using this model.

What is pragmatic analysis in NLP?

Pragmatic Analysis: It deals with outside word knowledge, which means knowledge that is external to the documents and/or queries. Pragmatics analysis that focuses on what was described is reinterpreted by what it actually meant, deriving the various aspects of language that require real-world knowledge.

What is perplexity in NLP?

The word “perplexed” means “puzzled” or “confused”, thus Perplexity in general means the inability to tackle something complicated and a problem that is not specified. Therefore, Perplexity in NLP is a way to determine the extent of uncertainty in predicting some text.
In NLP, perplexity is a way of evaluating language models. Perplexity can be high and low; Low perplexity is ethical because the inability to deal with any complicated problem is less while high perplexity is terrible because the failure to deal with a complicated is high.

What is ngram in NLP?

N-gram in NLP is simply a sequence of n words, and we also conclude the sentences which appeared more frequently, for example, let us consider the progression of these three words:
● New York (2 gram)
● The Golden Compass (3 gram)
● She was there in the hotel (4 gram)
Now from the above sequence, we can easily conclude that sentence (a) appeared more frequently than the other two sentences, and the last sentence(c) is not seen that often. Now if we assign probability in the occurrence of an n-gram, then it will be advantageous. It would help in making next-word predictions and in spelling error corrections.

Explain differences between AI, Machine Learning and NLP

Why self-attention is awesome?

“In terms of computational complexity, self-attention layers are faster than recurrent layers when the sequence length n is smaller than the representation dimensionality d, which is most often the case with sentence representations used by state-of-the-art models in machine translations, such as word-piece and byte-pair representations.” — from Attention is all you need.

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What are stop words?

 

Stop words are said to be useless data for a search engine. Words such as articles, prepositions, etc. are considered as stop words. There are stop words such as was, were, is, am, the, a, an, how, why, and many more. In Natural Language Processing, we eliminate the stop words to understand and analyze the meaning of a sentence. The removal of stop words is one of the most important tasks for search engines. Engineers design the algorithms of search engines in such a way that they ignore the use of stop words. This helps show the relevant search result for a query.

What is Latent Semantic Indexing (LSI)?

Latent semantic indexing is a mathematical technique used to improve the accuracy of the information retrieval process. The design of LSI algorithms allows machines to detect the hidden (latent) correlation between semantics (words). To enhance information understanding, machines generate various concepts that associate with the words of a sentence.
The technique used for information understanding is called singular value decomposition. It is generally used to handle static and unstructured data. The matrix obtained for singular value decomposition contains rows for words and columns for documents. This method best suits to identify components and group them according to their types.
The main principle behind LSI is that words carry a similar meaning when used in a similar context.
Computational LSI models are slow in comparison to other models. However, they are good at contextual awareness that helps improve the analysis and understanding of a text or a document.

What are Regular Expressions?

A regular expression is used to match and tag words. It consists of a series of characters for matching strings.
Suppose, if A and B are regular expressions, then the following are true for them:
● If {ɛ} is a regular language, then ɛ is a regular expression for it.
● If A and B are regular expressions, then A + B is also a regular expression within the language {A, B}.
● If A and B are regular expressions, then the concatenation of A and B (A.B) is a regular expression.
● If A is a regular expression, then A* (A occurring multiple times) is also a regular expression.

What are unigrams, bigrams, trigrams, and n-grams in NLP?

When we parse a sentence one word at a time, then it is called a unigram. The sentence parsed two words at a time is a bigram.
When the sentence is parsed three words at a time, then it is a trigram. Similarly, n-gram refers to the parsing of n words at a time.

What are the steps involved in solving an NLP problem?

Below are the steps involved in solving an NLP problem:

1. Gather the text from the available dataset or by web scraping
2. Apply stemming and lemmatization for text cleaning
3. Apply feature engineering techniques
4. Embed using word2vec
5. Train the built model using neural networks or other Machine Learning techniques
6. Evaluate the model’s performance
7. Make appropriate changes in the model
8. Deploy the model

There have some various common elements of natural language processing. Those elements are very important for understanding NLP properly, can you please explain the same in details with an example?

There have a lot of components normally using by natural language processing (NLP). Some of the major components are explained below:
● Extraction of Entity: It actually identifying and extracting some critical data from the available information which help to segmentation of provided sentence on identifying each entity. It can help in identifying one human that it’s fictional or real, same kind of reality identification for any organization, events or any geographic location etc.
● The analysis in a syntactic way: it mainly helps for maintaining ordering properly of the available words.

In the case of processing natural language, we normally mentioned one common terminology NLP and binding every language with the same terminology properly. Please explain in details about this NLP terminology with an example?

This is the basic NLP Interview Questions asked in an interview. There have some several factors available in case of explaining natural language processing. Some of the key factors are given below:

● Vectors and Weights: Google Word vectors, length of TF-IDF, varieties documents, word vectors, TF-IDF.
● Structure of Text: Named Entities, tagging of part of speech, identifying the head of the sentence.
● Analysis of sentiment: Know about the features of sentiment, entities available for the sentiment, sentiment common dictionary.
● Classification of Text: Learning supervising, set off a train, set of validation in Dev, Set of define test, a feature of the individual text, LDA.
● Reading of Machine Language: Extraction of the possible entity, linking with an individual entity, DBpedia, some libraries like Pikes or FRED.

Explain briefly about word2vec

Word2Vec embeds words in a lower-dimensional vector space using a shallow neural network.
The result is a set of word-vectors where vectors close together in vector space have similar meanings based on context, and word-vectors distant to each other have differing meanings. For example, apple and orange would be close together and apple and gravity would be relatively far.
There are two versions of this model based on skip-grams (SG) and continuous-bag-of-words (CBOW).

What are the metrics used to test an NLP model?

Accuracy, Precision, Recall and F1. Accuracy is the usual ratio of the prediction to the desired output. But going just be accuracy is naive considering the complexities involved.

What are some ways we can preprocess text input?

Here are several preprocessing steps that are commonly used for NLP tasks:
● case normalization: we can convert all input to the same case (lowercase or uppercase) as a way of reducing our text to a more canonical form
● punctuation/stop word/white space/special characters removal: if we don’t think these words or characters are relevant, we can remove them to reduce the feature space
● lemmatizing/stemming: we can also reduce words to their inflectional forms (i.e. walks → walk) to further trim our vocabulary
● generalizing irrelevant information: we can replace all numbers with a <NUMBER> token or all names with a <NAME> token.

How does the encoder-decoder structure work for language modelling?

The encoder-decoder structure is a deep learning model architecture responsible for several state of the art solutions, including Machine Translation.
The input sequence is passed to the encoder where it is transformed to a fixed-dimensional vector representation using a neural network. The transformed input is then decoded using another neural network. Then, these outputs undergo another transformation and a SoftMax layer. The final output is a vector of probabilities over the vocabularies. Meaningful information is extracted based on these probabilities.

How would you implement an NLP system as a service, and what are some pitfalls you might face in production?

This is less of a NLP question than a question for productionizing machine learning models. There are however certain intricacies to NLP models.

Without diving too much into the productionization aspect, an ideal Machine Learning service will have:
● endpoint(s) that other business systems can use to make inference
● a feedback mechanism for validating model predictions
● a database to store predictions and ground truths from the feedback
● a workflow orchestrator which will (upon some signal) re-train and load the new model for
serving based on the records from the database + any prior training data
● some form of model version control to facilitate rollbacks in case of bad deployments
● post-production accuracy and error monitoring

What are attention mechanisms and why do we use them?

This was a follow-up to the encoder-decoder question. Only the output from the last time step is passed to the decoder, resulting in a loss of information learned at previous time steps. This information loss is compounded for longer text sequences with more time steps.
Attention mechanisms are a function of the hidden weights at each time step. When we use attention in encoder-decoder networks, the fixed-dimensional vector passed to the decoder becomes a function of all vectors outputted in the intermediary steps.
Two commonly used attention mechanisms are additive attention and multiplicative attention. As the names suggest, additive attention is a weighted sum while multiplicative attention is a weighted multiplier of the hidden weights. During the training process, the model also learns weights for the attention mechanisms to recognize the relative importance of each time step.

How can we handle misspellings for text input?

By using word embeddings trained over a large corpus (for instance, an extensive web scrape of billions of words), the model vocabulary would include common misspellings by design. The model can then learn the relationship between misspelled and correctly spelled words to recognize their semantic similarity.
We can also preprocess the input to prevent misspellings. Terms not found in the model vocabulary can be mapped to the “closest” vocabulary term using:
● edit distance between strings
● phonetic distance between word pronunciations
● keyword distance to catch common typos

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What is the problem with ReLu?

● Exploding gradient(Solved by gradient clipping)
● Dying ReLu — No learning if the activation is 0 (Solved by parametric relu)
● Mean and variance of activations is not 0 and 1.(Partially solved by subtracting around 0.5 from activation. Better explained in fastai videos)

What is the difference between learning latent features using SVD and getting embedding vectors using deep network?

SVD uses linear combination of inputs while a neural network uses nonlinear combination.

What is the information in the hidden and cell state of LSTM?

Hidden stores all the information till that time step and cell state stores particular information that might be needed in the future time step.

When is self-attention not faster than recurrent layers?

When the sequence length is greater than the representation dimensions. This is rare.

What is the benefit of learning rate warm-up?

Learning rate warm-up is a learning rate schedule where you have low (or lower) learning rate at the beginning of training to avoid divergence due to unreliable gradients at the beginning. As the model becomes more stable, the learning rate would increase to speed up convergence.

What’s the difference between hard and soft parameter sharing in multi-task learning?

What’s the difference between BatchNorm and LayerNorm?

BatchNorm computes the mean and variance at each layer for every minibatch whereas LayerNorm computes the mean and variance for every sample for each layer independently.

Hard sharing is where we train for all the task at the same time and update our weights using all the losses whereas soft sharing is where we train for one task at a time.

Batch normalisation allows you to set higher learning rates, increasing speed of training as it reduces the unstability of initial starting weights.

Difference between BatchNorm and LayerNorm?

BatchNorm — Compute the mean and var at each layer for every minibatch
LayerNorm — Compute the mean and var for every single sample for each layer independently

Why does the transformer block have LayerNorm instead of BatchNorm?

Looking at the advantages of LayerNorm, it is robust to batch size and works better as it works at the sample level and not batch level.

What changes would you make to your deep learning code if you knew there are errors in your training data?

We can do label smoothening where the smoothening value is based on % error. If any particular class has known error, we can also use class weights to modify the loss.

What are the tricks used in ULMFiT? (Not a great questions but checks the awareness)
● LM tuning with task text
● Weight dropout
● Discriminative learning rates for layers
● Gradual unfreezing of layers
● Slanted triangular learning rate schedule
This can be followed up with a question on explaining how they help.

Tell me a language model which doesn’t use dropout

ALBERT v2 — This throws a light on the fact that a lot of assumptions we take for granted are not necessarily true. The regularization effect of parameter sharing in ALBERT is so strong that dropouts are not needed. (ALBERT v1 had dropouts.)

What are the differences between GPT and GPT-2?

● Layer normalization was moved to the input of each sub-block, similar to a residual unit of type “building block” (differently from the original type “bottleneck”, it has batch normalization applied before weight layers).
● An additional layer normalization was added after the final self-attention block.
● A modified initialization was constructed as a function of the model depth.
● The weights of residual layers were initially scaled by a factor of 1/√n where n is the number of residual layers.
● Use larger vocabulary size and context size.

What are the differences between GPT and BERT?

● GPT is not bidirectional and has no concept of masking
● BERT adds next sentence prediction task in training and so it also has a segment embedding

What are the differences between BERT and ALBERT v2?

● Embedding matrix factorisation(helps in reducing no. of parameters)
● No dropout
● Parameter sharing(helps in reducing no. of parameters and regularisation)

How does parameter sharing in ALBERT affect the training and inference time?

No effect. Parameter sharing just decreases the number of parameters.

How would you reduce the inference time of a trained NN model?

● Serve on GPU/TPU/FPGA
● 16 bit quantisation and served on GPU with fp16 support
● Pruning to reduce parameters
● Knowledge distillation (To a smaller transformer model or simple neural network)
● Hierarchical softmax/Adaptive softmax
● You can also cache results as explained here.

Would you use BPE with classical models?

Of course! BPE is a smart tokeniser and it can help us get a smaller vocabulary which can help us find a model with less parameters.

How would you make an arxiv papers search engine? 

How would you make a plagiarism detector?

Get top k results with TF-IDF similarity and then rank results with
● semantic encoding + cosine similarity
● a model trained for ranking

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How would you make a sentiment classifier?

This is a trick question. The interviewee can say all things such as using transfer learning and latest models but they need to talk about having a neutral class too otherwise you can have really good accuracy/f1 and still, the model will classify everything into positive or negative.
The truth is that a lot of news is neutral and so the training needs to have this class. The interviewee should also talk about how he will create a dataset and his training strategies like the selection of language model, language model fine-tuning and using various datasets for multitask learning.

What is the difference between regular expression and regular grammar?

A regular expression is the representation of natural language in the form of mathematical expressions containing a character sequence. On the other hand, regular grammar is the generator of natural language, defining a set of defined rules and syntax which the strings in the natural language must follow.

Why should we use Batch Normalization?

Once the interviewer has asked you about the fundamentals of deep learning architectures, they would move on to the key topic of improving your deep learning model’s performance.
Batch Normalization is one of the techniques used for reducing the training time of our deep learning algorithm. Just like normalizing our input helps improve our logistic regression model, we can normalize the activations of the hidden layers in our deep learning model as well:

How is backpropagation different in RNN compared to ANN?

In Recurrent Neural Networks, we have an additional loop at each node:
This loop essentially includes a time component into the network as well. This helps in capturing sequential information from the data, which could not be possible in a generic artificial neural network.
This is why the backpropagation in RNN is called Backpropagation through Time, as in backpropagation at each time step.

Which of the following is a challenge when dealing with computer vision problems?

Variations due to geometric changes (like pose, scale, etc), Variations due to photometric factors (like illumination, appearance, etc) and Image occlusion. All the above-mentioned options are challenges in computer vision.

Consider an image with width and height as 100×100. Each pixel in the image can have a color from Grayscale, i.e. values. How much space would this image require for storing?

The answer will be 8x100x100 because 8 bits will be required to represent a number from 0-256

Why do we use convolutions for images rather than just FC layers?

Firstly, convolutions preserve, encode, and actually use the spatial information from the image. If we used only FC layers we would have no relative spatial information. Secondly, Convolutional Neural Networks (CNNs) have a partially built-in translation in-variance, since each convolution kernel acts as it’s own filter/feature detector

What makes CNN’s translation-invariant?

As explained above, each convolution kernel acts as it’s own filter/feature detector. So let’s say you’re doing object detection, it doesn’t matter where in the image the object is since we’re going to apply the convolution in a sliding window fashion across the entire image anyways.

Why do we have max-pooling in classification CNNs?

Max-pooling in a CNN allows you to reduce computation since your feature maps are smaller after the pooling. You don’t lose too much semantic information since you’re taking the maximum activation. There’s also a theory that max-pooling contributes a bit to giving CNN’s more translation in-variance. Check out this great video from Andrew Ng on the benefits of max-pooling.

Why do segmentation CNN’s typically have an encoder-decoder style/structure?

The encoder CNN can basically be thought of as a feature extraction network, while the decoder uses that information to predict the image segments by “decoding” the features and upscaling to the original image size.

What is the significance of Residual Networks?

The main thing that residual connections did was allow for direct feature access from previous layers. This makes information propagation throughout the network much easier. One very interesting paper about this shows how using local skip connections gives the network a type of ensemble multi-path structure, giving features multiple paths to propagate throughout the network.

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What is batch normalization and why does it work?

Training Deep Neural Networks is complicated by the fact that the distribution of each layer’s inputs changes during training, as the parameters of the previous layers change. The idea is then to normalize the inputs of each layer in such a way that they have a mean output activation of zero and a standard deviation of one. This is done for each individual mini-batch at each layer i.e compute the mean and variance of that mini-batch alone, then normalize. This is analogous to how the inputs to networks are standardized. How does this help? We know that normalizing the inputs to a network helps it learn.
But a network is just a series of layers, where the output of one layer becomes the input to the next. That means we can think of any layer in a neural network as the first layer of a smaller subsequent network. Thought of as a series of neural networks feeding into each other, we normalize the output of one layer before applying the activation function and then feed it into the following layer (sub-network).

Why would you use many small convolutional kernels such as 3×3 rather than a few large ones?

This is very well explained in the VGGNet paper.

There are 2 reasons: First, you can use several smaller kernels rather than few large ones to get the same receptive field and capture more spatial context, but with the smaller kernels you are using less parameters and computations. Secondly, because with smaller kernels you will be using more filters, you’ll be able to use more activation functions and thus have a more discriminative mapping function being learned by your CNN.

What is Precision?

Precision (also called positive predictive value) is the fraction of relevant instances among the retrieved instances
Precision = true positive / (true positive + false positive)

What is Recall?

Recall (also known as sensitivity) is the fraction of relevant instances that have been retrieved over the total amount of relevant instances.
Recall = true positive / (true positive + false negative)

Define F1-score.

It is the weighted average of precision and recall. It considers both false positive and false negatives into account. It is used to measure the model’s performance.

What is cost function?

The cost function is a scalar function that Quantifies the error factor of the Neural Network. Lower the cost function better than the Neural network. Eg: MNIST Data set to classify the image, the input image is digit 2 and the Neural network wrongly predicts it to be 3.

List different activation neurons or functions

● Linear Neuron
● Binary Threshold Neuron
● Stochastic Binary Neuron
● Sigmoid Neuron
● Tanh function
● Rectified Linear Unit (ReLU)

Define Learning rate

The learning rate is a hyper-parameter that controls how much we are adjusting the weights of our network with respect to the loss gradient.

What is Momentum (w.r.t NN optimization)?

Momentum lets the optimization algorithm remembers its last step, and adds some proportion of it to the current step. This way, even if the algorithm is stuck in a flat region, or a small local minimum, it can get out and continue towards the true minimum.

What is the difference between Batch Gradient Descent and Stochastic Gradient Descent?

Batch gradient descent computes the gradient using the whole dataset. This is great for convex or relatively smooth error manifolds. In this case, we move somewhat directly towards an optimum solution, either local or global. Additionally, batch gradient descent, given an annealed learning rate, will eventually find the minimum located in its basin of attraction.
Stochastic gradient descent (SGD) computes the gradient using a single sample. SGD works well (Not well, I suppose, but better than batch gradient descent) for error manifolds that have lots of local maxima/minima. In this case, the somewhat noisier gradient calculated using the reduced number of samples tends to jerk the model out of local minima into a region that hopefully is more optimal.

Epoch vs Batch vs Iteration.

Epoch: one forward pass and one backward pass of all the training examples
Batch: examples processed together in one pass (forward and backward)
Iteration: number of training examples / Batch size

What is the vanishing gradient?

As we add more and more hidden layers, backpropagation becomes less and less useful in passing information to the lower layers. In effect, as information is passed back, the gradients begin to vanish and become small relative to the weights of the networks.

What are dropouts?

Dropout is a simple way to prevent a neural network from overfitting. It is the dropping out of some of the units in a neural network. It is similar to the natural reproduction process, where nature produces offsprings by combining distinct genes (dropping out others) rather than strengthening the co-adapting of them.

What is data augmentation? Can you give some examples?

Data augmentation is a technique for synthesizing new data by modifying existing data in such a way that the target is not changed, or it is changed in a known way. Computer vision is one of the fields where data augmentation is very useful. There are many modifications that we can do to images:
● Resize
● Horizontal or vertical flip
● Rotate, Add noise, Deform
● Modify colors Each problem needs a customized data augmentation pipeline. For example, on OCR, doing flips will change the text and won’t be beneficial; however, resizes and small rotations may help.

What are the components of GAN?

● Generator
● Discriminator

What’s the difference between a generative and discriminative model?

A generative model will learn categories of data while a discriminative model will simply learn the distinction between different categories of data. Discriminative models will generally outperform generative models on classification tasks.

What is Linear Filtering?

Linear filtering is a neighborhood operation, which means that the output of a pixel’s value is decided by the weighted sum of the values of the input pixels.

How can you achieve Blurring through Gaussian Filter?

This is the most common technique for blurring or smoothing an image. This filter improves the resulting pixel found at the center and slowly minimizes the effects as pixels move away from the center. This filter can also help in removing noise in an image.

How can you achieve Blurring through Gaussian Filter?

This is the most common technique for blurring or smoothing an image. This filter improves the resulting pixel found at the center and slowly minimizes the effects as pixels move away from the center. This filter can also help in removing noise in an image.

What is Non-Linear Filtering? How it is used?

Linear filtering is easy to use and implement. In some cases, this method is enough to get the necessary output. However, an increase in performance can be obtained through non-linear filtering. Through non-linear filtering, we can have more control and achieve better results when we encounter a more complex computer vision task.

Explain Median Filtering.

The median filter is an example of a non-linear filtering technique. This technique is commonly used for minimizing the noise in an image. It operates by inspecting the image pixel by pixel and taking the place of each pixel’s value with the value of the neighboring pixel median.
Some techniques in detecting and matching features are:
● Lucas-Kanade
● Harris
● Shi-Tomasi
● SUSAN (smallest uni value segment assimilating nucleus)
● MSER (maximally stable extremal regions)
● SIFT (scale-invariant feature transform)
● HOG (histogram of oriented gradients)
● FAST (features from accelerated segment test)
● SURF (speeded-up robust features)

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Describe the Scale Invariant Feature Transform (SIFT) algorithm

SIFT solves the problem of detecting the corners of an object even if it is scaled. Steps to implement this algorithm:
● Scale-space extrema detection – This step will identify the locations and scales that can still be recognized from different angles or views of the same object in an image.
● Keypoint localization – When possible key points are located, they would be refined to get accurate results. This would result in the elimination of points that are low in contrast or points that have edges that are deficiently localized.
● Orientation assignment – In this step, a consistent orientation is assigned to each key point to attain invariance when the image is being rotated.
● Keypoint matching – In this step, the key points between images are now linked to recognizing their nearest neighbors.

Why Speeded-Up Robust Features (SURF) came into existence?

SURF was introduced to as a speed-up version of SIFT. Though SIFT can detect and describe key points of an object in an image, still this algorithm is slow.

What is Oriented FAST and rotated BRIEF (ORB)?

This algorithm is a great possible substitute for SIFT and SURF, mainly because it performs better in computation and matching. It is a combination of fast key point detector and brief descriptor, which contains a lot of alterations to improve performance. It is also a great alternative in terms of cost because the SIFT and SURF algorithms are patented, which means that you need to buy them for their utilization.

What is image segmentation?

In computer vision, segmentation is the process of extracting pixels in an image that is related.
Segmentation algorithms usually take an image and produce a group of contours (the boundary of an object that has well-defined edges in an image) or a mask where a set of related pixels are assigned to a unique color value to identify it.
Popular image segmentation techniques:
● Active contours
● Level sets
● Graph-based merging
● Mean Shift
● Texture and intervening contour-based normalized cuts

What is the purpose of semantic segmentation?

The purpose of semantic segmentation is to categorize every pixel of an image to a certain class or label. In semantic segmentation, we can see what is the class of a pixel by simply looking directly at the color, but one downside of this is that we cannot identify if two colored masks belong to a certain object.

Explain instance segmentation.

In semantic segmentation, the only thing that matters to us is the class of each pixel. This would somehow lead to a problem that we cannot identify if that class belongs to the same object or not.
Semantic segmentation cannot identify if two objects in an image are separate entities. So to solve this problem, instance segmentation was created. This segmentation can identify two different objects of the same class. For example, if an image has two sheep in it, the sheep will be detected and masked with different colors to differentiate what instance of a class they belong to.

How is panoptic segmentation different from semantic/instance segmentation?

Panoptic segmentation is basically a union of semantic and instance segmentation. In panoptic segmentation, every pixel is classified by a certain class and those pixels that have several instances of a class are also determined. For example, if an image has two cars, these cars will be masked with different colors. These colors represent the same class — car — but point to different instances of a certain class.

Explain the problem of recognition in computer vision.

Recognition is one of the toughest challenges in the concepts in computer vision. Why is recognition hard? For the human eyes, recognizing an object’s features or attributes would be very easy. Humans can recognize multiple objects with very small effort. However, this does not apply to a machine. It would be very hard for a machine to recognize or detect an object because these objects vary. They vary in terms of viewpoints, sizes, or scales. Though these things are still challenges faced by most computer vision systems, they are still making advancements or approaches for solving these daunting tasks.

What is Object Recognition?

Object recognition is used for indicating an object in an image or video. This is a product of machine learning and deep learning algorithms. Object recognition tries to acquire this innate human ability, which is to understand certain features or visual detail of an image.

What is Object Detection and it’s real-life use cases?

Object detection in computer vision refers to the ability of machines to pinpoint the location of an object in an image or video. A lot of companies have been using object detection techniques in their system. They use it for face detection, web images, and security purposes.

Describe Optical Flow, its uses, and assumptions.

Optical flow is the pattern of apparent motion of image objects between two consecutive frames caused by the movement of object or camera. It is a 2D vector field where each vector is a displacement vector showing the movement of points from the first frame to the second
Optical flow has many applications in areas like :
● Structure from Motion
● Video Compression
● Video Stabilization
Optical flow works on several assumptions:
1. The pixel intensities of an object do not change between consecutive frames.
2. Neighboring pixels have similar motion.

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What is Histogram of Oriented Gradients (HOG)?

HOG stands for Histograms of Oriented Gradients. HOG is a type of “feature descriptor”. The intent of a feature descriptor is to generalize the object in such a way that the same object (in this case a person) produces as close as possible to the same feature descriptor when viewed under different conditions. This makes the classification task easier.

What’s the difference between valid and same padding in a CNN?

This question has more chances of being a follow-up question to the previous one. Or if you have explained how you used CNNs in a computer vision task, the interviewer might ask this question along with the details of the padding parameters.
● Valid Padding: When we do not use any padding. The resultant matrix after convolution will have dimensions (n – f + 1) X (n – f + 1)
● Same padding: Adding padded elements all around the edges such that the output matrix will have the same dimensions as that of the input matrix

What is BOV: Bag-of-visual-words (BOV)?

BOV also called the bag of key points, is based on vector quantization. Similar to HOG features, BOV features are histograms that count the number of occurrences of certain patterns within a patch of the image.

What is Poselets? Where are poselets used?

Poselets rely on manually added extra keypoints such as “right shoulder”, “left shoulder”, “right knee” and “left knee”. They were originally used for human pose estimation

Explain Textons in context of CNNs

A texton is the minimal building block of vision. The computer vision literature does not give a strict definition for textons, but edge detectors could be one example. One might argue that deep learning techniques with Convolution Neuronal Networks (CNNs) learn textons in the first filters.

What is Markov Random Fields (MRFs)?

MRFs are undirected probabilistic graphical models which are a wide-spread model in computer vision. The overall idea of MRFs is to assign a random variable for each feature and a random variable for each pixel.

Explain the concept of superpixel?

A superpixel is an image patch that is better aligned with intensity edges than a rectangular patch.
Superpixels can be extracted with any segmentation algorithm, however, most of them produce highly irregular superpixels, with widely varying sizes and shapes. A more regular space tessellation may be desired.

What is Non-maximum suppression(NMS) and where is it used?

NMS is often used along with edge detection algorithms. The image is scanned along the image gradient direction, and if pixels are not part of the local maxima they are set to zero. It is widely used in object detection algorithms.

Describe the use of Computer Vision in Healthcare.

Computer vision has also been an important part of advances in health-tech. Computer vision algorithms can help automate tasks such as detecting cancerous moles in skin images or finding symptoms in x-ray and MRI scans

Describe the use of Computer Vision in Augmented Reality & Mixed Reality

Computer vision also plays an important role in augmented and mixed reality, the technology that enables computing devices such as smartphones, tablets, and smart glasses to overlay and embed virtual objects on real-world imagery. Using computer vision, AR gear detects objects in the real world in order to determine the locations on a device’s display to place a virtual object.
For instance, computer vision algorithms can help AR applications detect planes such as tabletops, walls, and floors, a very important part of establishing depth and dimensions and placing virtual objects in the physical world.

Describe the use of Computer Vision in Facial Recognition

Computer vision also plays an important role in facial recognition applications, the technology that enables computers to match images of people’s faces to their identities. Computer vision algorithms detect facial features in images and compare them with databases of face profiles.
Consumer devices use facial recognition to authenticate the identities of their owners. Social media apps use facial recognition to detect and tag users. Law enforcement agencies also rely on facial recognition technology to identify criminals in video feeds.

Describe the use of Computer Vision in Self-Driving Cars

Computer vision enables self-driving cars to make sense of their surroundings. Cameras capture video from different angles around the car and feed it to computer vision software, which then processes the images in real-time to find the extremities of roads, read traffic signs, detect other cars, objects, and pedestrians. The self-driving car can then steer its way on streets and highways, avoid hitting obstacles, and (hopefully) safely drive its passengers to their destination.

Explain famous Computer Vision tasks using a single image example.

Many popular computer vision applications involve trying to recognize things in photographs; for example:
Object Classification: What broad category of object is in this photograph?
Object Identification: Which type of a given object is in this photograph?
Object Verification: Is the object in the photograph?
Object Detection: Where are the objects in the photograph?
Object Landmark Detection: What are the key points for the object in the photograph?
Object Segmentation: What pixels belong to the object in the image?
Object Recognition: What objects are in this photograph and where are they?

Explain the distinction between Computer Vision and Image Processing.

Computer vision is distinct from image processing.
Image processing is the process of creating a new image from an existing image, typically simplifying or enhancing the content in some way. It is a type of digital signal processing and is not concerned with understanding the content of an image.
A given computer vision system may require image processing to be applied to raw input, e.g. pre-processing images.
Examples of image processing include:
● Normalizing photometric properties of the image, such as brightness or color.
● Cropping the bounds of the image, such as centering an object in a photograph.
● Removing digital noise from an image, such as digital artifacts from low light levels

Explain business use cases in computer vision.

● Optical character recognition (OCR)
● Machine inspection
● Retail (e.g. automated checkouts)
● 3D model building (photogrammetry)
● Medical imaging
● Automotive safety
● Match move (e.g. merging CGI with live actors in movies)
● Motion capture (mocap)
● Surveillance
● Fingerprint recognition and biometrics

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What is the Boltzmann Machine?

One of the most basic Deep Learning models is a Boltzmann Machine, resembling a simplified version of the Multi-Layer Perceptron. This model features a visible input layer and a hidden layer — just a two-layer neural net that makes stochastic decisions as to whether a neuron should be on or off. Nodes are connected across layers, but no two nodes of the same layer are connected.

What Is the Role of Activation Functions in a Neural Network?

At the most basic level, an activation function decides whether a neuron should be fired or not. It accepts the weighted sum of the inputs and bias as input to any activation function. Step function,
Sigmoid, ReLU, Tanh, and Softmax are examples of activation functions.

What Is the Difference Between a Feedforward Neural Network and Recurrent Neural Network?

A Feedforward Neural Network signals travel in one direction from input to output. There are no feedback loops; the network considers only the current input. It cannot memorize previous inputs (e.g., CNN).

What Are the Applications of a Recurrent Neural Network (RNN)?

The RNN can be used for sentiment analysis, text mining, and image captioning. Recurrent Neural Networks can also address time series problems such as predicting the prices of stocks in a month or quarter.

What Are the Softmax and ReLU Functions?

Softmax is an activation function that generates the output between zero and one. It divides each output, such that the total sum of the outputs is equal to one. Softmax is often used for output layers.

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Machine Learning For Dummies
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Machine Learning Techniques

What Is Overfitting, and How Can You Avoid It?

Overfitting is a situation that occurs when a model learns the training set too well, taking up random fluctuations in the training data as concepts. These impact the model’s ability to generalize and don’t apply to new data.
When a model is given the training data, it shows 100 percent accuracy—technically a slight loss. But, when we use the test data, there may be an error and low efficiency. This condition is known as overfitting.
There are multiple ways of avoiding overfitting, such as:
● Regularization. It involves a cost term for the features involved with the objective function
● Making a simple model. With lesser variables and parameters, the variance can be reduced
● Cross-validation methods like k-folds can also be used
● If some model parameters are likely to cause overfitting, techniques for regularization like LASSO can be used that penalize these parameters

What is meant by ‘Training set’ and ‘Test Set’?

We split the given data set into two different sections namely, ‘Training set’ and ‘Test Set’.
‘Training set’ is the portion of the dataset used to train the model.
‘Testing set’ is the portion of the dataset used to test the trained model.

How Do You Handle Missing or Corrupted Data in a Dataset?

One of the easiest ways to handle missing or corrupted data is to drop those rows or columns or replace them entirely with some other value.
There are two useful methods in Pandas:
● IsNull() and dropna() will help to find the columns/rows with missing data and drop them
● Fillna() will replace the wrong values with a placeholder value

How Do You Design an Email Spam Filter?

Building a spam filter involves the following process:

● The email spam filter will be fed with thousands of emails
● Each of these emails already has a label: ‘spam’ or ‘not spam.’
● The supervised machine learning algorithm will then determine which type of emails are being marked as spam based on spam words like the lottery, free offer, no money, full refund, etc.
● The next time an email is about to hit your inbox, the spam filter will use statistical analysis and algorithms like Decision Trees and SVM to determine how likely the email is spam
● If the likelihood is high, it will label it as spam, and the email won’t hit your inbox
● Based on the accuracy of each model, we will use the algorithm with the highest accuracy after testing all the models

Explain bagging.

Bagging, or Bootstrap Aggregating, is an ensemble method in which the dataset is first divided into multiple subsets through resampling.
Then, each subset is used to train a model, and the final predictions are made through voting or averaging the component models.
Bagging is performed in parallel.

What is the ROC Curve and what is AUC (a.k.a. AUROC)?

The ROC (receiver operating characteristic) the performance plot for binary classifiers of True Positive Rate (y-axis) vs. False Positive Rate (xaxis).
AUC is the area under the ROC curve, and it’s a common performance metric for evaluating binary classification models.
It’s equivalent to the expected probability that a uniformly drawn random positive is ranked before a uniformly drawn random negative.

 

What are the various Machine Learning algorithms?

 

 

What is cross-validation?

 

Reference: k-fold cross validation 

 

Cross-validation is a resampling procedure used to evaluate machine learning models on a limited data sample. The procedure has a single parameter called k that refers to the number of groups that a given data sample is to be split into. As such, the procedure is often called k-fold cross-validation. When a specific value for k is chosen, it may be used in place of k in the reference to the model, such as k=10 becoming 10-fold cross-validation. Mainly used in backgrounds where the objective is forecast, and one wants to estimate how accurately a model will accomplish in practice.

 

Cross-validation is primarily used in applied machine learning to estimate the skill of a machine learning model on unseen data. That is, to use a limited sample in order to estimate how the model is expected to perform in general when used to make predictions on data not used during the training of the model.

 

It is a popular method because it is simple to understand and because it generally results in a less biased or less optimistic estimate of the model skill than other methods, such as a simple train/test split.

 

The general procedure is as follows:
1. Shuffle the dataset randomly.
2. Split the dataset into k groups
3. For each unique group:
a. Take the group as a hold out or test data set
b. Take the remaining groups as a training data set
c. Fit a model on the training set and evaluate it on the test set
d. Retain the evaluation score and discard the model
4. Summarize the skill of the model using the sample of model evaluation scores

What are 3 data preprocessing techniques to handle outliers?

1. Winsorize (cap at threshold).
2. Transform to reduce skew (using Box-Cox or similar).
3. Remove outliers if you’re certain they are anomalies or measurement errors.

How much data should you allocate for your training, validation, and test sets?

You have to find a balance, and there’s no right answer for every problem.
If your test set is too small, you’ll have an unreliable estimation of model performance (performance statistic will have high variance). If your training set is too small, your actual model parameters will have a high variance.
A good rule of thumb is to use an 80/20 train/test split. Then, your train set can be further split into train/validation or into partitions for cross-validation.

What Is a False Positive and False Negative and How Are They Significant?

False positives are those cases which wrongly get classified as True but are False.
False negatives are those cases which wrongly get classified as False but are True.
In the term ‘False Positive’, the word ‘Positive’ refers to the ‘Yes’ row of the predicted value in
the confusion matrix. The complete term indicates that the system has predicted it as a positive, but the actual value is negative.

What’s a Fourier transform?

A Fourier transform is a generic method to decompose generic functions into a superposition of symmetric functions. Or as this more intuitive tutorial puts it, given a smoothie, it’s how we find the recipe. The Fourier transform finds the set of cycle speeds, amplitudes, and phases to match any time signal. A Fourier transform converts a signal from time to frequency domain — it’s a very common way to extract features from audio signals or other time series such as sensor data.

 

Machine Learning Cheat Sheets, Tutorial, Practical examples, References, Datasets

Machine Learning For Dummies  on iOs

Machine Learning For Dummies on Windows

Machine Learning For Dummies Web/Android 

#MachineLearning #AI #ArtificialIntelligence #ML #MachineLearningForDummies #MLOPS #NLP #ComputerVision #AWSMachineLEarning #AzureAI #GCPML

Machine Learning Cheat Sheet

Machine Learning Cheat Sheets

Download it here

Credit: Remi Canard

TensorFlow Practical Examples and Tutorial

– Basic Models
Linear Regression
Logistic Regression
Word2Vec (Word Embedding)

– Neural Networks
Simple Neural Network
Convolutional Neural Network
Recurrent Neural Network (LSTM)
Bi-directional Recurrent Neural Network (LSTM)
Dynamic Recurrent Neural Network (LSTM)

-Unsupervised
Auto-Encoder
DCGAN (Deep Convolutional Generative Adversarial Networks)

-Utilities:
Save and Restore a model
Build Custom Layers & Modules

– Data Management
Load and Parse data
Build and Load TFRecords
Image Transformation (i.e. Image Augmentation)

TensorFlow Examples abd Tutorials

Download it here

Credit: Alex Wang

Cool MLOps repository of free talks, books, papers and more

Link to the repo:

Image preview

Machine Learning  Training Videos

 



References

1 https://springboard.com
2 https://simplilearn.com
3 https://geeksforgeeks.org
4 https://elitedatascience.com
5 https://analyticsvidhya.com
6 https://guru99.com
7 https://intellipaat.com
8 https://towardsdatascience.com
9 https://mygreatlearning.com
10 https://mindmajix.com
11 https://toptal.com
12 https://glassdoor.co.in
13 https://udacity.com
14 https://educba.com
15 https://analyticsindiamag.com
16 https://ubuntupit.com
17 https://javatpoint.com
18 https://quora.com
19 hackr.io
20 kaggle.com
21 https://www.linkedin.com/in/stevenouri/

 

Machine Learning For Dummies  on iOs

Machine Learning For Dummies on Windows

Machine Learning For Dummies Web/Android 

#MachineLearning #AI #ArtificialIntelligence #ML #MachineLearningForDummies #MLOPS #NLP #ComputerVision #AWSMachineLEarning #AzureAI #GCPML

Explain differences between AI, Machine Learning and NLP

 

 

 

 

 

 

 

 

 

 

 

 

 

Artificial IntelligenceMachine LearningNatural Language Processing
It is the technique t create smarter machinesMachine Learning is the term used for systems that learn from experienceThis is the set of system that has the ability to understand the language
AI includes human interventionMachine Learning purely involves the working of computers and no human interventionNLP links both computer and human languages
Artificial intelligence is a broader concept than Machine LearningML is a narrow concept and is a subset of AI 

Top Machine Learning Algorithms for Predictions:

Top ML algorithms for predictions

TensorFlow Interview Questions and Answers

Tensorflow Interview Questions and Answers

Direct link here

Machine Learning For Dummies  on iOs

Machine Learning For Dummies on Windows

Machine Learning For Dummies Web/Android 

#MachineLearning #AI #ArtificialIntelligence #ML #MachineLearningForDummies #MLOPS #NLP #ComputerVision #AWSMachineLEarning #AzureAI #GCPML

Machine Learning For Dummies
Machine Learning For Dummies

Machine learning is just one component of a larger field called artificial intelligence (AI). AI researchers have done an excellent job at describing the fundamental problems they must solve to achieve intelligent behavior; these problems fall into four general categories: representation, reasoning, learning, and search.

Basically, all of AI research can be classified under these headings; for example, language understanding is a special case of representation (natural language), planning is a special case of reasoning (analogical logical inferences), learning to play chess is a special case of learning (policy search in the game tree), and table lookup is a special case of search (symbol-table lookups). We will focus on two: representation and search.

What follows are our ten favorite problems/areas for the next decade or so. Each one has been researched quite heavily already, but we think that there are no silver bullets yet discovered nor are there any obvious candidates lurking in the wings waiting to take over. Each area has a different flavor to it; all have something to offer the machine learning community, and we believe that many will find fertile ground for their own investigations.

Machine learning methods are useful on large problems, which is becoming increasingly important as applications such as speech recognition are moving into real-world situations outside the lab (e.g., using voice commands while driving). Solution: This is a difficult one because there are many possible solutions to this problem; all will require advances in both theoretical and experimental techniques but we do not know what they are yet. A better understanding of why certain learning algorithms work well on some types of problems but not others may provide insights into how to scale them up. Some examples of the types of problems we would like to tackle include: (i) learning from large databases, (ii) learning in multiple domains, and (iii) learning task-specific knowledge.

Artificial intelligence methods have been used to solve combinatorial problems such as chess playing and problem-solving; these are problems that can be represented as a search tree using nodes representing possible moves for each player. These methods work well on small problems but often fail when applied to larger real-world problems because there are too many options in the search trees that must be explored. For example, consider a game where there are 100 moves per second for each player with 10^100 different games possible over a 40 year lifetime. Solving the AI problem amounts to finding a winning strategy. This is much different from the type of problems we are used to solving which normally fit in memory and where the number of potential options can be kept manageable. Solution: We need better methods than those currently available for searching through very large trees; these could involve ideas from machine learning, such as neural networks or evolutionary algorithms.

Searching for solutions to a problem among all possible alternatives is an important capability but one that has not been researched nearly enough due to its complexity. A brute-force search would seem to require enumerating all alternatives, which is impossible even on extremely simple problems, whereas other approaches seem so specialized that they have little value outside their specific domain (and sometimes not even there). In contrast, machine learning methods can be applied to virtually any problem where the solution space is finite (e.g., finding a path through a graph or board games like chess).

The brute-force approach of enumerating all possible combinations has been successfully applied to optimization problems where only a few desirable solutions are available, but there are many applications that require solving very large problems with thousands or millions of potential solutions. Examples include the Traveling Salesman Problem and scheduling tasks for an airline crew using dozens of variables (e.g., number of passengers flying, weight, the distance between origin and destination cities), a task which becomes more difficult because it must deal with occasional breakdowns in equipment. Any feasible algorithm will require shortcuts that often involve approximations or heuristics. Source.

What is the main purpose of using PCA on a dataset, and what are some examples of its application?

PCA is short for Principal Component Analysis, and it’s a technique used to reduce the dimensionality of a dataset. In other words, it helps you to find the important Variables in a dataset and get rid of the noise. PCA is used in a variety of fields, from image recognition to facial recognition to machine learning.

PCA has a few main applications:
– Reducing the number of features in a dataset
– Finding relationships between features
– Identifying clusters in data
– Visualizing data

Let’s take a look at an example. Say you have a dataset with 1000 features (variables). PCA can help you reduce that down to, say, 10 features that explain the majority of variance in the data. This is helpful because it means you can build a model with far fewer features, which makes it simpler and faster. In addition, PCA can help you to find relationships between features and identify clusters in data. All of this can be extremely helpful in understanding and using your data.

PCA is an important tool in Machine Learning, and has a number of applications. The main purpose of PCA is to reduce the dimensionality of a dataset, while still retaining as much information as possible. This can be useful when dealing with very large datasets, as it can make training and testing faster and more efficient. PCA is also often used for data visualization, as it can help to create clear and concise visualizations of high-dimensional data. Finally, PCA can be used for feature selection, as it can help to identify the most important features in a dataset. PCA is a powerful tool that can be applied in many different ways, and is an essential part of any Machine Learning workflow.

What are subservient sounding male names suitable for an automated assistant?

Artificial intelligence is increasingly becoming a staple in our lives, with everything from our homes to our workplaces being automated to some degree. And as AI becomes more ubiquitous, we are starting to see a trend of subservient-sounding names being given to male automated assistants. This is likely due to a combination of factors, including the fact that women are still primarily seen as domestic servants and the fact that many people find it easier to relate to a male voice. Whatever the reason, it seems that subservient-sounding names are here to stay when it comes to male AI. So if you’re looking for a name for your new automated assistant, here are some subservient-sounding male names to choose from:

– Jasper: A popular name meaning “treasurer” or “bringer of riches.”
– Custer: A name derived from the Latin word for “servant.”
– Luther: A Germanic name meaning “army of warriors.”
– Benson: A name of English origin meaning “son of Ben.”
– Wilfred: A name of Germanic origin meaning “desires peace.”

In recent years, there has been an increasing trend of using subservient sounding male names for automated assistants. Artificial intelligence is becoming more prevalent in our everyday lives, and automation is slowly but surely taking over many routine tasks. As such, it’s no surprise that we’re seeing a name trend emerge that reflects our growing dependence on these technologies. So what are some suitable names for an automated assistant? How about “Robo-Bob”? Or “Mecha-Mike”? Perhaps even “Cyber-Steve”? Whatever you choose, just be sure to pick a name that sounds suitably subservient! After all, your automated assistant should reflect your growing dependency on technology… and not your growing dominance over it!

How do you calculate user churn rate?

Churn rate is a metric that measures the percentage of users who leave or discontinue using a service within a given time period. The churn rate is an important metric for businesses to track because it can help them identify areas where their product or service is losing users. There are many ways to calculate the churn rate, but one of the most popular methods is to use machine learning or artificial intelligence. Artificial intelligence can help identify patterns in user behavior that may indicate that someone is about to leave the service. By tracking these patterns, businesses can be proactive in addressing user needs and reducing the chances of losing them. In addition, automation can also help reduce the churn rate by making it easier for users to stay with the service. Automation can handle tasks like customer support and billing, freeing up users’ time and making it less likely that they will discontinue their subscription. By using machine learning and artificial intelligence, businesses can more accurately predict and prevent user churn.

There are a few different ways to calculate the user churn rate using artificial intelligence. One way is to use a technique called Artificial Neural Networks. This involves training a computer to recognize patterns in data. Once the computer has learned to recognize these patterns, it can then make predictions about future data. Another way to calculate the user churn rate is to use a technique called Support Vector Machines. This approach uses algorithms to find the boundaries between different groups of data. Once these boundaries have been found, the algorithm can then make predictions about new data points. Finally, there is a technique called Bayesian inference. This approach uses probability theory to make predictions about future events. By using these three techniques, it is possible to calculate the user churn rate with a high degree of accuracy.

 

#datascience #machinelearning

Machine Learning Techniques illustrated

How to confuse Machine Learning and AI?

Folks with no educational background taking a MOOC or two in deep learning, entering the field, and skipping over basic concepts in machine learning–specificity/sensitivity, the difference between supervised and unsupervised learning, linear regression, ensembles, proper design of a study/test, probability distributions… With enough MOOCs, you can sound like you know what you are doing, but as soon as something goes wrong or changes slightly, there’s no knowledge about how to fix it. Big problem in employment, particularly when hiring a first machine learning engineer/data scientist.. Source: Colleen Farrelly

With rapid developments of artificial intelligence (AI) technology, the use of AI technology to mine clinical data has become a major trend in medical industry. Utilizing advanced AI algorithms for medical image analysis, one of the critical parts of clinical diagnosis and decision-making, has become an active research area both in industry and academia. Recent applications of deep leaning in medical image analysis involve various computer vision-related tasks such as classification, detection, segmentation, and registration. Among them, classification, detection, and segmentation are fundamental and the most widely used tasks that can be done with Scale but the rest of the more demanding methods require a more sophisticated platform for example Tasq.

Although there exist a number of reviews on deep learning methods on medical image analysis, most of them emphasize either on general deep learning techniques or on specific clinical applications. The most comprehensive review paper is the work of Litjens et al. published in 2017. Deep learning is such a quickly evolving research field; numerous state-of-the-art works have been proposed since then.

AI Technologies in Medical Image Analysis

Different medical imaging modalities have their unique characteristics and different responses to human body structure and organ tissue and can be used in different clinical purposes. The commonly used image modalities for diagnostic analysis in clinic include projection imaging (such as X-ray imaging), computed tomography (CT), ultrasound imaging, and magnetic resonance imaging (MRI). MRI sequences include T1, T1-w, T2, T2-w, diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC), and fluid attenuation inversion recovery (FLAIR). Figure 1 demonstrates a few examples of medical image modalities and their corresponding clinical applications.

Image Classification for Medical Image Analysis

As a fundamental task in computer vision, image classification plays an essential role in computer-aided diagnosis. A straightforward use of image classification for medical image analysis is to classify an input image or a series of images as either containing one (or a few) of predefined diseases or free of diseases (i.e., healthy case). Typical clinical applications of image classification tasks include skin disease identification in dermatology, eye disease recognition in ophthalmology (such as diabetic retinopathy, glaucoma, and corneal diseases). Classification of pathological images for various cancers such as breast cancer and brain cancer also belongs to this area.

Convolutional neural network (CNN) is the dominant classification framework for image analysis. With the development of deep learning, the framework of CNN has continuously improved. AlexNet was a pioneer convolutional neural network, which was composed of repeated convolutions, each followed by ReLU and max pooling operation with stride for downsampling. The proposed VGGNet used convolution kernels and maximum pooling to simplify the structure of AlexNet and showed improved performance by simply increasing the number and depth of the network. Via combining and stacking , and convolution kernels and pooling, the inception network and its variants increased the width and the adaptability of the network. ResNet and DenseNet both used skip connections to relieve the gradient vanishing. SENet proposed a squeeze-and-excitation module which enabled the model to pay more attention to the most informative channel features. The family of EfficientNet applied AUTOML and a compound scaling method to uniformly scale the width, depth, and resolution of the network in a principled way, resulting in improved accuracy and efficiency. Source: Kelly Holland

GPUs also process things. It’s just that they’re better and faster at “specific” things.

The main stuff a GPU is “awesome” at, exactly because it is designed to be specific with those: Matrix maths. The sorts of calculation used when converting a bunch of 3d points (XYZ values) into an approximation of how such a shape would look from a camera. I.e. rendering a 2d picture from a 3d object – exactly why a GPU is made in the first place: https://www.3dgep.com/3d-math-primer-for-game-programmers-matrices/

The sorts of calculations used in current “AI” ? Guess what? Matrix maths:

https://rossbulat.medium.com/ai-essentials-working-with-matrices-2ceb9ca3bd1b

By Irne Barnard

This is perhaps the most important question in computational learning theory. In fact, some of the most important theorems of machine learning like the No Free Lunch Theorem and the Fundamental Theorem of Statistical Learning are aimed at answering this very question.

Formally, the smallest number of data points needed for successfully learning a classification rule using a machine learning (ML) algorithm is called the sample complexity of the algorithm. Now, you might wonder why sample complexity is such a big deal. It’s because sample complexity is to ML algorithms what computational complexity is to any algorithm. It measures the minimum amount of resource (i.e. the data) that is required to achieve the desired goal.

There are several interesting answers to the question of sample complexity, that arise from various assumptions on the learner. In what follows, I will give the answer under some popular assumptions/scenarios.

Scenario 1: Perfect Learning

In our first scenario, we consider the problem of learning the correct hypothesis (classification rule) amongst a set of plausible hypotheses. The data is sampled independently from an unknown probability distribution.

It turns out that under no further assumptions on the data-generating probability distribution, the problem is impossible. In other words, there is no algorithm that can learn the correct classification rule perfectly from any finite amount of data. This result is called the No Free Lunch Theorem in machine learning. I’ve discuss this result in more detail here.

Scenario 2: Probably Approximately Correct (PAC) Learning

For the second scenario, we consider the problem of learning the correct hypothesis approximately, with high probability. That is, our algorithm may fail to identify even an approximately correct hypothesis with some small probability. This relaxation allows us to give a slightly more useful answer to the question.

The answer to this question is of the order of the VC-dimension of the hypothesis class. More precisely, if we want the algorithm to be approximately correct with an error of at most ϵϵ with a probability of at least 1δ1−δ, then we need a minimum of dϵlog(1ϵδ)dϵlog⁡(1ϵδ), where dd is the VC-dimension of the hypothesis class. Note that dd can be infinite for certain hypothesis classes. In that case, it is not possible to succeed in the learning task even approximately, even with high probability. On the other hand, if dd is finite, we say that the hypothesis class is (ϵ,δ)(ϵ,δ)−PAC learnable. (I explain PAC-learnability in more detail in this answer.)

Scenario 3: Learning with a Teacher

In the previous two scenarios, we assume that the data that is presented to the learner is randomly sampled from an unknown probability distribution. For this scenario, we do away with the randomness. Instead, we assume that the learner is presented with a carefully chosen set of training data points that are picked by a benevolent teacher. (By benevolent teacher, I mean that the teacher tries to make the learner guess the correct hypothesis with the fewest number of data points.)

In this case, the answer to the question is the teaching dimension. It is interesting to note that there is no straightforward relation between the teaching dimension and VC-dimension of a hypothesis class. They can be arbitrarily far from each other. (If you’re curious to know the relation between the two, here is a nice paper.)


In addition to these, there are other notions of “dimension” that characterize the sample complexity of a learning task under different scenarios. For example, there is the Littlestone dimension for online learning and Natarajan dimension for multi-class learning. Intuitively, these dimensions capture the inherent hardness of a machine learning task. The harder the task, the higher the dimension and the corresponding sample complexity.


To those of you seeking for exact numbers, here’s a note I added in the comments section: I wish I could add some useful empirical results, but the sample complexity bounds obtained by the PAC-learning approach are really loose to the point of being useless in case of most state-of-the-art ML algorithms like deep learning. So, the results I presented are basically a theoretical curiosity at this point. However, this might change in the near future as lots of researchers are working on strengthening this framework.

Source: Muni Sreenivas Pydi

As mentioned in the other answer, this can be understood using the concept of bias-variance tradeoff.

For any machine learning model, want to find a function that approximately fits your data. So, you essentially define the following:

  • Class of functions : Instead of searching in the space of all possible functions, you restrict the space of functions that the algorithm searches over. For example, a linear classifier will search among all possible lines, but will not consider more complex curves.
  • Loss function : This is used to compare two functions from the above class of functions. For instance, in SVM, you would prefer line 1 to line 2 if line 1 has a larger margin than line 2.

Now, the simpler your class of functions is, the smaller the amount of data required. To get some intuition for this, think about a regression problem that has three features. So, a linear function class will have the following form:

y=a0+a1x1+a2x2+a3x3y=a0+a1x1+a2x2+a3x3

Every point (p, q, r, s) in the 4-dimensional space corresponds to a function of the above form, namely y=p+qx1+rx2+sx3y=p+qx1+rx2+sx3. So, you need to find one point in that 4D space that fits your data well.

Now, if instead of the class of linear functions, you chose quadratic functions, your functions would be of the following form:

y=a0+a1x1+a2x2+ a3x3+a4x1x2+ a5x2x3+a6x1x3+ a7x21+a8x22+a9x23y = a0+a1x1+a2x2+a3x3 + a4x1x2+a5x2x3+a6x1x3+a7x12+a8x22+a9x32

So now, you have to search for the best point in a 10D space! Therefore, you need more data to distinguish these larger number of points from each other.

With that intuition, we can say that to learn from small amount of data, you want to define a small enough function class.

Note: While in the above example, we simply look at the no. of parameters to get a sense of complexity of the function class, in general, more parameters does not necessarily mean more complexity [for instance, if a lot of the parameters are strongly correlated].

Source: Prasoon Goyal

Best Machine Learning Books That All Data Scientists Must Read.

best ml books

1. Artificial Intelligence: A Modern Approach

Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach

Experts Opinions

I like this book very much. When in doubt I look there, and usually find what I am looking for, or I find references on where to go to study the problem more in depth. I like that it tries to show how various topics are interrelated, and to give general architectures for general problems … It is a jump in quality with respect to the AI books that were previously available. — Prof. Giorgio Ingargiola (Temple).

Really excellent on the whole and it makes teaching AI a lot easier. — Prof. Ram Nevatia (USC).

It is an impressive book, which begins just the way I want to teach, with a discussion of agents, and ties all the topics together in a beautiful way. — Prof. George Bekey (USC). Buy it now

2. Deep Learning (Adaptive Computation and Machine Learning series)

Experts Opinions

“Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX.

“If you want to know here deep learning came from, what it is good for, and where it is going, read this book.” —Geoffrey Hinton FRS, Professor, University of Toronto, Research Scientist at Google. Buy it

3. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems 2nd Edition

Experts Opinions

“An exceptional resource to study Machine Learning. You will find clear-minded, intuitive explanations, and a wealth of practical tips.” —François Chollet, Author of Keras, author of Deep Learning with Python.

“This book is a great introduction to the theory and practice of solving problems with neural networks; I recommend it to anyone interested in learning about practical ML.” — Peter Warden, Mobile Lead for TensorFlow. Buy it.

4. Python Machine Learning – Second Edition: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2nd Edition

 

First things first, I don’t think there are many questions of the form “Is it a good practice to always X in machine learning” where the answer is going to be definitive. Always? Always always? Across parametric, non-parametric, Bayesian, Monte Carlo, social science, purely mathematic, and million feature models? That’d be nice, wouldn’t it! Anyway feel free to check out this interactive demo from deepchecks.

Concretely though, here are a few ways in which: it just depends.

Some times when normalizing is good:

1) Several algorithms, in particular SVMs come to mind, can sometimes converge far faster on normalized data (although why, precisely, I can’t recall).

2) When your model is sensitive to magnitude, and the units of two different features are different, and arbitrary. This is like the case you suggest, in which something gets more influence than it should.

But of course — not all algorithms are sensitive to magnitude in the way you suggest. Linear regression coefficients will be identical if you do, or don’t, scale your data, because it’s looking at proportional relationships between them.

Some times when normalizing is bad:

1) When you want to interpret your coefficients, and they don’t normalize well. Regression on something like dollars gives you a meaningful outcome. Regression on proportion-of-maximum-dollars-in-sample might not.

2) When, in fact, the units on your features are meaningful, and distance does make a difference! Back to SVMs — if you’re trying to find a max-margin classifier, then the units that go into that ‘max’ matter. Scaling features for clustering algorithms can substantially change the outcome. Imagine four clusters around the origin, each one in a different quadrant, all nicely scaled. Now, imagine the y-axis being stretched to ten times the length of the the x-axis. instead of four little quadrant-clusters, you’re going to get the long squashed baguette of data chopped into four pieces along its length! (And, the important part is, you might prefer either of these!)

In I’m sure unsatisfying summary, the most general answer is that you need to ask yourself seriously what makes sense with the data, and model, you’re using. Source: ABC of Data Science and ML

How do you prepare data for XGBoost?

Data preparation is a critical step in the data science process, and it is especially important when working with XGBoost. XGBoost is a powerful machine learning algorithm that can provide accurate predictions on data sets of all sizes. However, in order to get the most out of XGBoost, it is important to prepare the data in a way that is conducive to machine learning. This means ensuring that the data is clean, feature engineering has been performed, and that the data is in a format that can be easily consumed by the algorithm. By taking the time to prepare the data properly, data scientists can significantly improve the performance of their machine learning models.

When preparing the dataset for your machine learning model, you should use one-hot encoding on what type of data?

In machine learning and data science, one-hot encoding is a process by which categorical data is converted into a format that is suitable for use with machine learning algorithms. The categorical data is first grouped by type, and then a binary value is assigned to each group. This binary value corresponds to the group’s position in the encoding scheme. For example, if there are three groups, the first group would be assigned a value of ‘0’, the second group would be assigned a value of ‘1’, and the third group would be assigned a value of ‘2’. One-hot encoding is often used when working with categorical data, as it can help to improve the performance of machine learning models. In addition, one-hot encoding can also make it easier to visualize the relationship between different categories.

In machine learning and data science, one-hot encoding is a method used to convert categorical features into numerical features. This is often necessary when working with machine learning models, as many models can only accept numerical input. However, one-hot encoding is not without its problems. The most significant issue is the potential for increased dimensionality – if a dataset has too many features, it can be difficult for the model to learn from the data. In addition, one-hot encoding can create sparse datasets, which can also be difficult for some machine learning models to handle. Despite these issues, one-hot encoding remains a popular method for preparing data for machine learning models.

A retail company wants to start personalizing product recommendations to visitors of their website. They have historical data of what products the users have purchased and want to implement the system for new users, prior to them purchasing a product. What’s one way of phrasing a machine learning problem for this situation?

For this retail company, a machine learning problem could be phrased as a prediction problem. The goal would be to build a model that can take in data about a new user (such as demographic information and web browsing history) and predict which products they are likely to purchase. This would allow the company to give each new user personalized product recommendations, increasing the chances of making a sale. Data science techniques such as feature engineering and model selection would be used to build the best possible prediction model. By phrasing the machine learning problem in this way, the retail company can make the most of their historical data and improve the user experience on their website.

There are many ways to frame a machine learning problem for a retail company that wants to start personalizing product recommendations to visitors of their website. One way is to focus on prediction: using historical data of what products users have purchased, can we predict which products new users will be interested in? This is a task that machine learning is well suited for, and with enough data, we can build a model that accurately predicts product interests for new users. Another way to frame the problem is in terms of classification: given data on past purchases, can we classify new users into groups based on their product interests? This would allow the retail company to more effectively target personalization efforts. There are many other ways to frame the machine learning problem, depending on the specific goals of the company. But no matter how it’s framed, machine learning can be a powerful tool for personalizing product recommendations.

A data scientist is trying to determine how a model is doing based on training evaluation. The train accuracy plateaus out at around 70% and the validation accuracy is 67%. How should the data scientist interpret these results?

When working with machine learning models, it is important to evaluate how well the model is performing. This can be done by looking at the train and validation accuracy. In this case, the train accuracy has plateaued at around 70% and the validation accuracy is 67%. There are a few possible explanations for this. One possibility is that the model is overfitting on the training data. This means that the model is able to accurately predict labels for the training data, but it is not as effective at generalizing to new data. Another possibility is that there is a difference in the distribution of the training and validation data. If the validation data is different from the training data, then it makes sense that the model would have a lower accuracy on the validation data. To determine which of these explanations is most likely, the data scientist should look at the confusion matrix and compare the results of the training and validation sets. If there are large differences between the two sets, then it is likely that either overfitting or a difference in distributions is to blame. However, if there isn’t a large difference between the sets, then it’s possible that 70% is simply the best accuracy that can be achieved given the data.

One important consideration in machine learning is how well a model is performing. This can be determined in a number of ways, but one common method is to split the data into a training set and a validation set. The model is then trained on the training data and evaluated on the validation data. If the model is performing well, we would expect to see a similar accuracy on both the training and validation sets. However, in this case the training accuracy plateaus out at around 70% while the validation accuracy is only 67%. This could be indicative of overfitting, where the model has fit the training data too closely and does not generalize well to new data. In this case, the data scientist should look for ways to improve the model so that it performs better on the validation set.

When updating your weights using the loss function, what dictates how much change the weights should have?

In machine learning and data science, the learning rate is a parameter that dictates how much change the weights should have when updating them using the loss function. The learning rate is typically a small value between 0 and 1. A higher learning rate means that the weights are updated more quickly, which can lead to faster convergence but can also lead to instability. A lower learning rate means that the weights are updated more slowly, which can lead to slower convergence but can also help avoid overfitting. The optimal learning rate for a given problem can be found through trial and error. The bias term is another parameter that can affect the weight updates. The bias term is used to prevent overfitting by penalizing models that make too many assumptions about the data. The initial weights are also important, as they determine where the model starts on the optimization landscape. The batch size is another important parameter, as it defines how many training examples are used in each iteration of weight updates. A larger batch size can lead to faster convergence, but a smaller batch size can help avoid overfitting. Finding the optimal values for all of these parameters can be a challenge, but doing so is essential for training high-quality machine learning models.

An ad tech company is using an XGBoost model to classify its clickstream data. The company’s Data Scientist is asked to explain how the model works to a group of non-technical colleagues. What is a simple explanation the Data Scientist can provide?

Machine learning is a form of artificial intelligence that allows computers to learn from data, without being explicitly programmed. machine learning is a powerful tool for solving complex problems, and XGBoost is a popular machine learning algorithm. machine learning algorithms like XGBoost work by building a model based on training data, and then using that model to make predictions on new data. In the case of the ad tech company, the Data Scientist has used XGBoost to build a model that can classify clickstream data. This means that the model can look at new data and predict which category it belongs to. For example, the model might be able to predict whether a user is likely to click on an ad or not. The Data Scientist can explain how the model works by showing how it makes predictions on new data.

Machine learning is a method of teaching computers to learn from data, without being explicitly programmed. machine learning is a subset of artificial intelligence (AI). The XGBoost algorithm is a machine learning technique used to create models that predict outcomes by learning from past data. XGBoost is an implementation of gradient boosting, which is a machine learning technique for creating models that make predictions by combining the predictions of multiple individual models. The XGBoost algorithm is highly effective and is used by many organizations, including ad tech companies, to classify their data. The Data Scientist can explain how the XGBoost model works by providing a simple explanation of machine learning and how the XGBoost algorithm works. machine learning is a method of teaching computers to learn from data, without being explicitly programmed. 

 

An ML Engineer at a real estate startup wants to use a new quantitative feature for an existing ML model that predicts housing prices. Before adding the feature to the cleaned dataset, the Engineer wants to visualize the feature in order to check for outliers and overall distribution and skewness of the feature. What visualization technique should the ML Engineer use? 

The machine learning engineer at the real estate startup should use a visualization technique in order to check for outliers and overall distribution and skewness of the new quantitative feature. There are many different visualization techniques that could be used for this purpose, but two of the most effective are histograms and scatterplots. A histogram can show the distribution of values for the new feature, while a scatterplot can help to identify any outliers. By visualizing the data, the engineer will be able to ensure that the new feature is of high quality and will not impact the performance of the machine learning model.

When updating your weights using the loss function, what dictates how much change the weights should have?

In machine learning and data science, the learning rate is a parameter that dictates how much change the weights should have when updating them using the loss function. The learning rate is typically a small value between 0 and 1. A higher learning rate means that the weights are updated more quickly, which can lead to faster convergence but can also lead to instability. A lower learning rate means that the weights are updated more slowly, which can lead to slower convergence but can also help avoid overfitting. The optimal learning rate for a given problem can be found through trial and error. The bias term is another parameter that can affect the weight updates. The bias term is used to prevent overfitting by penalizing models that make too many assumptions about the data. The initial weights are also important, as they determine where the model starts on the optimization landscape. The batch size is another important parameter, as it defines how many training examples are used in each iteration of weight updates. A larger batch size can lead to faster convergence, but a smaller batch size can help avoid overfitting. Finding the optimal values for all of these parameters can be a challenge, but doing so is essential for training high-quality machine learning models.

The loss function is a key component of machine learning algorithms, as it determines how well the model is performing. When updating the weights using the loss function, the learning rate dictates how much change the weights should have. The learning rate is a hyperparameter that can be tuned to find the optimal value for the model. The bias term is another important factor that can influence the weights. The initial weights can also play a role in how much change the weights should have. The batch size is another important factor to consider when updating the weights using the loss function.

A data scientist wants to clean and merge two small datasets stored in CSV format. What tool can they use to merge these datasets together?

As a data scientist, you often need to work with multiple datasets in order to glean insights that would be hidden in any one dataset on its own. In order to do this, you need to be able to clean and merge datasets quickly and efficiently. One tool that can help you with this task is Pandas. Pandas is a Python library that is specifically designed for data analysis. It offers a wide range of features that make it well-suited for merging datasets, including the ability to read in CSV format, clean data, and merge datasets with ease. In addition, Pandas integrates well with other machine learning libraries such as Scikit-learn, making it a valuable tool for data scientists.

As a data scientist, one of the most important skills is knowing how to clean and merge datasets. This can be a tedious and time-consuming process, but it is essential for machine learning and data science projects. There are several tools that data scientists can use to merge datasets, but one of the most popular options is pandas. Pandas is a Python library that offers a wide range of functions for data manipulation and analysis. Additionally, pandas has built-in support for reading and writing CSV files. This makes it an ideal tool for merging small datasets stored in CSV format. With pandas, data scientists can quickly and easily clean and merge their data, giving them more time to focus on other aspects of their projects.

A real estate company is building a linear regression model to predict housing prices for different cities in the US. Which of the following is NOT a good metric to measure performance of their regression model?

Machine learning is a subset of data science that deals with the design and development of algorithms that can learn from and make predictions on data. Linear regression is a machine learning algorithm used to predict numerical values based on a linear relationship between input variables. When building a linear regression model, it is important to choose an appropriate metric to measure the performance of the model. The F1 score, R-squared value, and mean-squared error are all valid metrics for measuring the performance of a linear regression model. However, the mean absolute error is not a good metric to use for this purpose, as it does not take into account the direction of the prediction error (i.e., whether the predicted value is higher or lower than the actual value). As such, using the mean absolute error as a metric for evaluating the performance of a linear regression model could lead to inaccurate results.

A real estate company wants to provide its customers with a more accurate prediction of the final sale price for houses they are considering in various cities. To do this, the company wants to use a fully connected neural network trained on data from the previous ten years of home sales, as well as other features. What kind of machine learning problem does this situation most likely represent?

Answer: Regression

Which feature of Amazon SageMaker can you use for preprocessing the data?

 
 

Answer: Amazon Sagemaker Notebook instances

Amazon SageMaker enables developers and data scientists to build, train, tune, and deploy machine learning (ML) models at scale. You can deploy trained ML models for real-time or batch predictions on unseen data, a process known as inference. However, in most cases, the raw input data must be preprocessed and can’t be used directly for making predictions. This is because most ML models expect the data in a predefined format, so the raw data needs to be first cleaned and formatted in order for the ML model to process the data.  You can use the Amazon SageMaker built-in Scikit-learn library for preprocessing input data and then use the Amazon SageMaker built-in Linear Learner algorithm for predictions.

What setting, when creating an Amazon SageMaker notebook instance, can you use to install libraries and import data?

Answer: LifeCycle Configuration

You work for the largest coffee chain in the world. You’ve recently decided to source beans from a new market to create new blends and flavors. These beans come from 30 different growers, in 3 different countries. In order to keep a consistent flavor, you have each grower send samples of their beans to your tasting baristas who rate the beans on 20 different dimensions. You now need to group the beans together so the supply can be diversified yet the flavor of the final product kept as consistent as possible.
What is one way you could convert this business situation into a machine learning problem?

 
 

Answer:

In which phase of the ML pipeline does the machine learn from the data?

 
 
 

Answer: Model Training

A text analytics company is developing a text classification model to detect whether a document involves offensive content or not. The training dataset included ten non-offensive documents for every one offensive document. Their model resulted in an accuracy score of 94%.
What can we conclude from this result?

Answer: Accuracy is the wrong metric here, because it can be heavily influenced by the large class (non-offensive documents).

A Machine Learning Engineer is creating a regression model for forecasting company revenue based on an internal dataset made up of past sales and other related data.

 

What metric should the Engineer use to evaluate the ML model?

 
Answer: Root Mean Squared error (RMSE)
Root Mean Square Error (RMSE) is the standard deviation of the residuals (prediction errors). Residuals are a measure of how far from the regression line data points are; RMSE is a measure of how spread out these residuals are. In other words, it tells you how concentrated the data is around the line of best fit.

An ML scientist has built a decision tree model using scikit-learn with 1,000 trees. The training accuracy for the model was 99.2% and the test accuracy was 70.3%. Should the Scientist use this model in production?

 
Answer:  No, because it is not generalizing well on the test set
 
 
 

The curse of dimensionality relates to which of the following?

 
Answer: A – A high number of features in a dataset

 

The curse of dimensionality relates to a high number of features in a dataset.

Curse of Dimensionality describes the explosive nature of increasing data dimensions and its resulting exponential increase in computational efforts required for its processing and/or analysis. This term was first introduced by Richard E.

A Data Scientist wants to include “month” as a categorical column in a training dataset for an ML model that is being built. However, the ML algorithm gives an error when the column is added to the training data. What should the Data Scientist do to add this column?

 

Answer:

StandardScaler standardizes a feature by subtracting the mean and then scaling to unit variance. Unit variance means dividing all the values by the standard deviation. StandardScaler does not meet the strict definition of scale I introduced earlier.

What is the primary reason that one might want to pick either random search or Bayesian optimization over grid search when performing hyperparameter optimization?

 
 
Answer: Random search and Bayesian methods leave smaller unexplored regions than grid searches
 
 
 

A Data Scientist trained an XGBoost model to classify internal documents for further inquiry, and now wants to evaluate the model’s performance by looking at the results visually. What technique should the Data Scientist use in this situation?

 
 
 

Machine Learning Pipeline Goals

Machine Learning Pipeline Goals
Machine Learning Pipeline Goals

Common Machine Learning Use Cases

Common Machine LEarning USe Cases
Common Machine LEarning USe Cases

What is a machine learning model?

What is a machine learning model?
What is a machine learning model?

What are features and weights meaning in Machine Learning?

Features meaning in ML
Features meaning in ML

 

Weights in ML
Weights in ML

 

Machine Learning Features and Weights
Machine Learning Features and Weights

Reference: Wiki

PRE-PROCESSING AND FEATURE ENGINEERING

Visualization

image

image

Outliers

Missing Value

Feature Engineering

 

Source: https://github.com/mortezakiadi/ML-Pipeline/wiki

How to Choose the right Sagemaker built-in algorithm?

How to chose the right built in algorithm in SageMaker?
How to chose the right built in algorithm in SageMaker?
Guide to choosing the right unsupervised learning algorithm
Guide to choosing the right unsupervised learning algorithm

 

Choosing the right  ML algorithm based on Data Type
Choosing the right ML algorithm based on Data Type

 

Choosing the right ML algo based on data type
Choosing the right ML algo based on data type

This is a general guide for choosing which algorithm to use depending on what business problem you have and what data you have. 

Machine Learning Deployment and Monitoring

Deployment and Monitoring

Machine Learning Model Performance Evaluation Metrics

image

Machine Learning Breaking News and Top Stories

  • [D] Thoughts on a blockchain based robot authorisation system
    by /u/d41_fpflabs (Machine Learning) on March 27, 2024 at 6:26 pm

    Robots intended to be used by the general public, with the ability to execute critical tasks must be governed by a trustless, transparent, auditable authorisation system. There are 3 main points of vulnerability for a robot deployed into the real world. Malicious intent from the robot Malicious intent from the robot manufacturer 3.Malicious intent from hackers A blockchain based authorisation system seems like the perfect solution. The blockchain authorisation control system will have 4 fundamental aspects: 1.Soul-bound NFTs Multi-Sig Roles Smart contract events Read the full proposed approach here: https://github.com/dev-diaries41/robo-auth What are you thoughts? submitted by /u/d41_fpflabs [link] [comments]

  • [D] Dataloading from external disk
    by /u/bkffadia (Machine Learning) on March 27, 2024 at 6:17 pm

    Hey there, I am training a deep lesrning model using a dataset of 400Go in an external SSD disk and I noticed that training is very slow, any tricks to make dataloading faster ? PS : I have to use the external disk submitted by /u/bkffadia [link] [comments]

  • [D] How do you measure performance of AI copilot/assistant?
    by /u/n2parko (Machine Learning) on March 27, 2024 at 5:38 pm

    Curious to hear from those that are building and deploying products with AI copilots. How are you tracking the interactions? And are you feeding the interaction back into the model for retraining? Put together a how-to to do this with an OS Copilot (Vercel AI SDK) and Segment and would love any feedback to improve the spec: https://segment.com/blog/instrumenting-user-insights-for-your-ai-copilot/ submitted by /u/n2parko [link] [comments]

  • [D] What is the state-of-the-art for 1D signal cleanup?
    by /u/XmintMusic (Machine Learning) on March 27, 2024 at 4:52 pm

    I have the following problem. Imagine I have a 'supervised' dataset of 1D curves with inputs and outputs, where the input is a modulated noisy signal and the output is the cleaned desired signal. Is there a consensus in the machine learning community on how to tackle this simple problem? Have you ever worked on anything similar? What algorithm did you end up using? Example: https://imgur.com/JYgkXEe submitted by /u/XmintMusic [link] [comments]

  • [D] State of the art TTS
    by /u/Zireaone (Machine Learning) on March 27, 2024 at 3:04 pm

    State of the art Tts question Hey! I'm currently working on a project and I'd like to implement speech using TTS, I tried many things and I can't seem to find something that fits my needs, I haven't worked on TTS for a while now so I was wondering if maybe they were newer technologies I could use. Here is what I'm looking for : I need to be be quite fast and without too many sound artifacts (I tried bark and while the possibility of manipulating emotion is quite remarkable the generated voice is full of artifacts and noise) It'd be a bonus if I could stream the audio and pipe it through other things, I'd like to apply an RVC Model on top of it (live) Another 'nice to have' is to have some controls over the emotions or tone of the voice. I tried these so far (either myself or through demos) : TORTOISETTS and EDGETTS seem to have a nice voice quality but are relatively monotone. Bark as I said is very good at emotions and controls but lots of artifacts in the voice, if I have time I'd try to apply postprocessing but idk to what extent it can help OpenAI models don't have much emotions IMO Same as eleven labs I used Uber duck in the past but it seems a lot of fun functionalities disappeared. If you have any advice, suggestion or if you think I should try somethings further feel free to reply! I also want to thanks everyone in advance! Have a nice day! submitted by /u/Zireaone [link] [comments]

  • [D] Data cleaning for classification model
    by /u/fardin__khan (Machine Learning) on March 27, 2024 at 2:42 pm

    Currently working on a classification model, which entails data cleaning. We've got 8000 images categorized into 3 classes. After removing duplicates and corrupted images, what else should we consider? submitted by /u/fardin__khan [link] [comments]

  • [D] Seeking guidance/advice
    by /u/qheeeee (Machine Learning) on March 27, 2024 at 2:14 pm

    Hi, I've finished Andrew Ng's course on Coursera. I think I've got the basics. I've started learning ML for my master's thesis. I want to develop a method to estimate scope 3 emissions. I studied business and I do not have any python background except for a 6-month data analytics bootcamp. I've got the data needed for my thesis, but when I try to work on it, I'm not sure what I'm doing, and ofc a sh*t ton of bugs and errors. Do I need to just keep trying to push through and learn through the experience by working on my thesis or do I need to study more? I've been considering to by a book <\Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow> by Aurelien Geron. Any guidance/recommendation would be much appreciated! submitted by /u/qheeeee [link] [comments]

  • [P] Insta Face Swap
    by /u/abdullahozmntr (Machine Learning) on March 27, 2024 at 2:03 pm

    ComfyUI node repo: https://github.com/abdozmantar/ComfyUI-InstaSwap Standalone repo: https://github.com/abdozmantar/Standalone-InstaSwap ​ ​ https://i.redd.it/9d4ti20fvvqc1.gif submitted by /u/abdullahozmntr [link] [comments]

  • [D] Seeking Advice
    by /u/MD24IB (Machine Learning) on March 27, 2024 at 1:45 pm

    I'm currently pursuing my undergraduate degree in robotics engineering and have been immersing myself in concepts related to machine learning, deep learning, and computer vision, both modern and traditional. With strong programming skills and a habit of regularly reading research papers, I'm eager to understand the job landscape in my field and pursue a Phd. Are there ample opportunities available? What can I expect in terms of salaries and future prospects? Additionally, I'm curious about the comparative job market between natural language processing (NLP) and computer vision. Given my background and interests, what areas or skills should I focus on learning to enhance my career prospects? Thanks in advance for your time and advice. submitted by /u/MD24IB [link] [comments]

  • [N] Introducing DBRX: A New Standard for Open LLM
    by /u/artificial_intelect (Machine Learning) on March 27, 2024 at 1:35 pm

    https://x.com/vitaliychiley/status/1772958872891752868?s=20 Shill disclaimer: I was the pretraining lead for the project DBRX deets: 16 Experts (12B params per single expert; top_k=4 routing) 36B active params (132B total params) trained for 12T tokens 32k sequence length training submitted by /u/artificial_intelect [link] [comments]

  • [D] Seeking Advice: Transitioning to Low-Level Implementations in AIoT Systems - Where to Start?
    by /u/MaTwickenham (Machine Learning) on March 27, 2024 at 1:20 pm

    Hello everyone, I'm a prospective graduate student who will be starting my studies in September this year, specializing in AIoT (Artificial Intelligence of Things) Systems. Recently, I've been reading papers from journals like INFOCOM and SIGCOMM, and I've noticed that they mostly focus on relatively low-level aspects of operating systems, including GPU/CPU scheduling, optimization of deep learning model inference, operator optimization, cross-platform migration, and deployment. I find it challenging to grasp the implementation details of these works at the code level. When I looked at the implementations of these works uploaded on GitHub, I found it relatively difficult to understand. My primary programming languages are Java and Python. During my undergraduate studies, I gained proficiency in implementing engineering projects and ideas using Python, especially in the fields of deep learning and machine learning. However, I lack experience and familiarity with C/C++ (many of the aforementioned works are based on C/C++). Therefore, I would like to ask for advice from senior professionals and friends on which areas of knowledge I should focus on. Do I need to learn CUDA programming, operating system programming, or other directions? Any recommended learning paths would be greatly appreciated. PS: Recently, I have started studying the MIT 6.S081 Operating System Engineering course. Thank you all sincerely for your advice. submitted by /u/MaTwickenham [link] [comments]

  • [P] Run AI & ML workflows locally from your Mac desktop
    by /u/creatorai (Machine Learning) on March 27, 2024 at 1:08 pm

    Hi all - I wanted to share an app I’ve been working on with a small team over the past year that I thought this community would be interested in. Odyssey is a completely native Mac app for creating remarkable art, getting work done, and automating repetitive tasks with the power of AI and machine learning models. We just made a major feature update and added the ability to create your own Widgets. Odyssey Widgets are fully interactive mini applications that live in their own windows or panels and are driven by a workflow. This means you can take a workflow you create with Odyssey and add it directly to your desktop. So, as an example, you could generate an image, chat with locally run chatbot, run bulk image processing, etc. straight from your desktop without even opening the Odyssey app. Widgets can be built with Odyssey and triggered from the Odyssey logo in your Mac’s menu. https://i.redd.it/8s9s6i0clvqc1.gif We're in public beta but here's a full list of everything Odyssey supports: Image generation and processing Run Stable Diffusion 1.5, SDXL, SDXL Lightning, and SDXL Turbo locally or connect your Stable Diffusion API key Add custom models & LoRAs ControlNet support including canny edges, pose detection, depth estimation, and QR Code Monster Inpainting and outpainting Super resolution models (Best Buddy GAN, Ultrasharp 4x, Remacri, and ESRGAN) Multiple image segmentation models Erase objects Dozens of image processing nodes including aspect ratio, resizing, and extracting dominant colors Custom image transitions for powerful slideshows Large language models and math equations Run Llama2 locally or connect your ChatGPT API key Supports both chatbot mode and instructions mode Solver node for word problems and math nodes for complex equations Lots of updates coming here in the next few weeks Automation and batch workflows Batch image and text nodes support hundreds of images and lines of text at once Remove backgrounds, upscale, change aspect ratios, and run dozens of image processors in bulk Private, customizable, and shareable No images, chats, or inputs are stored or accessible by the Odyssey team Completely private and secure. The only tracking is anonymized usage data to help us improve Odyssey Process your own data entirely locally No internet connection required to run local models Use your own API keys for ChatGPT and Stable Diffusion Easily save and share custom workflows What’s coming soon: Custom LLMs & more text processing nodes - we are adding support for bringing in custom LLMs, document uploads, and more Batch text and workflow automation - we are building in document upload, batch text support, and an integration with Apple shortcuts Plug-in support - we are opening up the Odyssey to 3P developers. If you’re interested, please reach out - would love to learn more from you as we work on building this out Feel free to reach out to [john@odysseyapp.io](mailto:john@odysseyapp.io) if you have any questions or feedback. submitted by /u/creatorai [link] [comments]

  • [P] Hybrid-Net: Real-time audio source separation, generate lyrics, chords, beat.
    by /u/CheekProfessional146 (Machine Learning) on March 27, 2024 at 12:11 pm

    Project: https://github.com/DoMusic/Hybrid-Net A transformer-based hybrid multimodal model, various transformer models address different problems in the field of music information retrieval, these models generate corresponding information dependencies that mutually influence each other. An AI-powered multimodal project focused on music, generate chords, beats, lyrics, melody, and tabs for any song. submitted by /u/CheekProfessional146 [link] [comments]

  • [P] Visualize RAG Data
    by /u/DocBrownMS (Machine Learning) on March 27, 2024 at 10:29 am

    Hey all, I've recently published a tutorial at Towards Data Science that explores a somewhat overlooked aspect of Retrieval-Augmented Generation (RAG) systems: the visualization of documents and questions in the embedding space: https://towardsdatascience.com/visualize-your-rag-data-evaluate-your-retrieval-augmented-generation-system-with-ragas-fc2486308557 While much of the focus in RAG discussions tends to be on the algorithms and data processing, I believe that visualization can help to explore the data and to gain insights into problematic subgroups within the data. This might be interesting for some of you, although I'm aware that not everyone is keen on this kind of visualization. I believe it can add a unique dimension to understanding RAG systems. submitted by /u/DocBrownMS [link] [comments]

  • [D] Any open-source way to make AI lip-syncing this good?
    by /u/MorningHerald (Machine Learning) on March 27, 2024 at 5:23 am

    How can I create AI lip syncing as good as this? https://imgur.com/Uw89El8 Which tools - open-source, free or paid - are best? What options are there currently. submitted by /u/MorningHerald [link] [comments]

  • [D] Is Synthetic Data a Reliable Option for Training Machine Learning Models?
    by /u/Data_Nerd1979 (Machine Learning) on March 27, 2024 at 3:49 am

    "The most obvious advantage of synthetic data is that it contains no personally identifiable information (PII). Consequently, it doesn’t pose the same cybersecurity risks as conventional data science projects. However, the big question for machine learning is whether this information is reliable enough to produce functioning ML models." Very informative blog regarding Using Synthetic Data in Machine Learning, source here https://opendatascience.com/is-synthetic-data-a-reliable-option-for-training-machine-learning-models/ submitted by /u/Data_Nerd1979 [link] [comments]

  • [R] AIOS: LLM Agent Operating System
    by /u/TouchLive4686 (Machine Learning) on March 27, 2024 at 1:00 am

    Paper: https://arxiv.org/abs/2403.16971 Github: https://github.com/agiresearch/AIOS Abstract: The integration and deployment of large language model (LLM)-based intelligent agents have been fraught with challenges that compromise their efficiency and efficacy. Among these issues are sub-optimal scheduling and resource allocation of agent requests over the LLM, the difficulties in maintaining context during interactions between agent and LLM, and the complexities inherent in integrating heterogeneous agents with different capabilities and specializations. The rapid increase of agent quantity and complexity further exacerbates these issues, often leading to bottlenecks and sub-optimal utilization of resources. Inspired by these challenges, this paper presents AIOS, an LLM agent operating system, which embeds large language model into operating systems (OS) as the brain of the OS, enabling an operating system "with soul" -- an important step towards AGI. Specifically, AIOS is designed to optimize resource allocation, facilitate context switch across agents, enable concurrent execution of agents, provide tool service for agents, and maintain access control for agents. We present the architecture of such an operating system, outline the core challenges it aims to resolve, and provide the basic design and implementation of the AIOS. Our experiments on concurrent execution of multiple agents demonstrate the reliability and efficiency of our AIOS modules. Through this, we aim to not only improve the performance and efficiency of LLM agents but also to pioneer for better development and deployment of the AIOS ecosystem in the future. An overview of the AIOS architecture. submitted by /u/TouchLive4686 [link] [comments]

  • PyTorch Dataloader Optimizations [D]
    by /u/MuscleML (Machine Learning) on March 27, 2024 at 12:13 am

    What are some optimizations that one could use for the data loader in PyTorch? The data type could be anything. But I primarily work with images and text. We know you can define your own. But does anyone have any clever tricks to share? Thank you in advance! submitted by /u/MuscleML [link] [comments]

  • [P] Model with multi variable target
    by /u/notSheOrThemOrIt (Machine Learning) on March 26, 2024 at 7:55 pm

    Hi all. I need to train a model in which the target is a probability vector, i,e [0.2, 0.4. 0.1, 0.3] such that the sum of its components is 1. I am thinking of using classifier with cross entropy loss, but i am not sure that this is the right solution because such classifiers are usually fitted against a target with [0,0,1,0...] i.e - only one component equals to 1 and then it can be shown that the classifier in fact learn to generate a distribution with highest values on the classifier best candidate. I need something similar but not the same, i.e a model with a loss function of kl-divergence or something like "distance" between probability vectors, any ideas or references? submitted by /u/notSheOrThemOrIt [link] [comments]

  • ACL 2024 Reviews [Discussion]
    by /u/EDEN1998 (Machine Learning) on March 26, 2024 at 5:55 pm

    Discussion thread of ACL 2024 (ARR Feb) reviews. I got 3, 3, 4 for soundness. How about you guys? submitted by /u/EDEN1998 [link] [comments]




Person climbing a staircase. Learn Data Science from Scratch: online program with 21 courses
Person climbing a staircase. Learn Data Science from Scratch: online program with 21 courses

How do you export data from BigQuery to a CSV file?

Google Cloud’s BigQuery is a powerful tool for storing and querying large data sets. However, sometimes you may need to export data from BigQuery in order to perform additional analysis or simply to have a backup. Thankfully, Google Cloud makes it easy to export data from BigQuery to a CSV file.

  • The first step is to select the dataset that you want to export.
  • Next, click on the “Export Table” button. In the pop-up window, select “CSV” as the file format and choose a location to save the file.
  • Finally, click on the “Export” button and Google Cloud will begin exporting the data.
  • Depending on the size of the data set, this may take several minutes. Once the export is complete, you will have a CSV file containing all of the data from BigQuery.

Alternatively, Simply run the following command:

“bq extract –destination_format=CSV [dataset_name] [table_name]”.

This will export your data to a CSV file in Google Cloud Storage. You can then download the file from Google Cloud Storage and use it in another program. Alternatively, you can use the “bq load” command to load your data directly into another Google Cloud service, such as Google Sheets.

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What is the Difference Between Mini-Batch and Full-Batch in Machine Learning?

In the field of machine learning, there are two types of batch sizes that are commonly used: mini-batch and full-batch. Both have their pros and cons, and the choice of which to use depends on the situation. Here’s a quick rundown of the differences between mini-batch and full-batch in machine learning.

Mini-Batch Machine Learning
Mini-batch machine learning is a type of batch processing where the data is divided into small batches before being fed into the machine learning algorithm. The advantage of mini-batch machine learning is that it can provide more accurate results than full-batch machine learning, since the data is less likely to be affected by outliers. However, the disadvantage of mini-batch machine learning is that it can be slower than full-batch machine learning, since each batch has to be processed separately.

Full-Batch Machine Learning
Full-batch machine learning is a type of batch processing where the entire dataset is fed into the machine learning algorithm at once. The advantage of full-batch machine learning is that it is faster than mini-batch machine learning, since all the data can be processed simultaneously. However, the disadvantage of full-batch machine learning is that it can be less accurate than mini-batch machine learning, since outliers in the dataset can have a greater impact on the results.

So, which should you use? It depends on your needs. If accuracy is more important than speed, then mini-batch machine learning is the way to go. On the other hand, if speed is more important than accuracy, then full-batch machine learning is the way to go.

The Difference Between Mini-Batch and Full-Batch Learning

In machine learning, there are two main types of batch learning: mini-batch and full-batch. Both types of batch learning algorithms have their own pros and cons that data scientists should be aware of. In this blog post, we’ll take a look at the difference between mini-batch and full-batch learning so you can make an informed decision about which type of algorithm is right for your project.

Mini-batch learning is a type of batch learning that operates on small subsets of the training data, typically referred to as mini-batches. The advantage of mini-batch learning is that it can be parallelized across multiple processors or devices, which makes training much faster than full-batch training. Another advantage is that mini-batches can be generated on the fly from a larger dataset, which is especially helpful if the entire dataset doesn’t fit into memory. However, one downside of mini-batch learning is that it can sometimes lead to suboptimal results due to its stochastic nature.

Full-Batch Learning
Full-batch learning is a type of batch learning that operates on the entire training dataset at once. The advantage of full-batch learning is that it converges to the global optimum more quickly than mini-batch or stochastic gradient descent (SGD) methods. However, the disadvantage of full-batch learning is that it is very slow and doesn’t scale well to large datasets. Additionally, full-batch methods can’t be parallelized across multiple processors or devices due to their sequential nature.

So, which type of batch learning algorithm is right for your project? If you’re working with a small dataset, then full-batch learning might be your best bet. However, if you’re working with a large dataset or need to train your model quickly, then mini=batch or SGD might be better suited for your needs. As always, it’s important to experiment with different algorithms and tuning parameters to see what works best for your particular problem.

2023 AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams

Welcome to AWS Certification Machine Learning Specialty (MLS-C01) Practice Exams! 

This book is designed to help you prepare for the AWS Certified Machine Learning – Specialty (MLS-C01) exam and earn your AWS certification. The AWS Certified Machine Learning – Specialty (MLS-C01) exam is designed for individuals who have a strong understanding of machine learning concepts and techniques, and who can design, build, and deploy machine learning models on the AWS platform.

In this book, you will find a series of practice exams that are designed to mimic the format and content of the actual MLS-C01 exam. Each practice exam includes a set of multiple choice and multiple response questions that cover a range of topics, including machine learning concepts, techniques, and algorithms, as well as the AWS services and tools used to build and deploy machine learning models.

By working through these practice exams, you can test your knowledge, identify areas where you need further study, and gain confidence in your ability to pass the MLS-C01 exam. Whether you are a machine learning professional looking to earn your AWS certification or a student preparing for a career in machine learning, this book is an essential resource for your exam preparation.

2023 AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams
2023 AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams

What is the best Japanese natural language processing (NLP) library?

NLP is a field of computer science and artificial intelligence that deals with the interactions between computers and human languages. NLP algorithms are used to process and analyze large amounts of natural language data. Japanese NLP libraries are used to develop applications that can understand and respond to Japanese text.

The best Japanese NLP library depends on your application’s needs.

For example, if you are developing a machine translation application, you will need a library that supports word sense disambiguation and part-of-speech tagging. If you are developing a chatbot, you will need a library that supports sentence analysis and dialogue management. In general, Japanese NLP libraries can be divided into three categories: rule-based systems, statistical systems, and hybrid systems.

Rule-based systems rely on linguistic rules to process language data.

Statistical systems use statistical models to process language data.

Hybrid systems use a combination of linguistic rules and statistical models to process language data.

The best Japanese NLP library for your application will depend on the type of NLP tasks you need to perform and your resources (e.g., time, data, computing power).

XGBoost is a powerful tool that has a wide range of applications in the real world. XGBoost is a machine learning algorithm that is used to improve the performance of other machine learning algorithms.

XGBoost has been used to improve the performance of data science models in a variety of fields, including healthcare, finance, and retail.

In healthcare, XGBoost has been used to predict patient outcomes, such as length of stay in the hospital and mortality rates.

In finance, XGBoost has been used to predict stock prices and credit card fraud.

In retail, XGBoost has been used to improve customer segmentation and product recommendations.

XGBoost is a versatile tool that can be used to improve the performance of machine learning models in many different fields.

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Machine Learning 101 – Top 200 AWS and Google Certified Machine Learning Specialty Questions and Answers Dumps

AWS machine Learning Specialty Exam Prep MLS-C01

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What are the Top 200 AWS and Google Certified Machine Learning Specialty Questions and Answers Dumps?

This blog is the best way  is the best way to prepare for your upcoming  AWS Certified Machine Learning Specialty and Google Certified Professional Machine Learning Engineer exam. With over 100 questions and answers, this blog provides quizzes similar  that are very similar to the real exam. It also includes  the option to show and hide answers. Additionally, there are machine learning interview questions and detailed answers, as well as cheat sheets and illustrations. This blog is the best way to make sure you are well-prepared for your AWS Certified Machine Learning Specialty Exam.

2023 AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams
2023 AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams

The typical Google Machine Learning Engineer salary is $147,218. Machine Learning Engineer salaries at Google can range from $110,000 – $152,183.

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.

  • By the end of 2020, 85% of customer interactions will be handled without a human (Call Center, Chatbot, etc…)
  • 61% of marketers say artificial intelligence is the most important aspect of their data strategy.
  • 80% of business and tech leaders say AI already boosts productivity (Robotic Process Automation, Power Automate, etc..)
  • Current AI technology can boost business productivity by up to 40%

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AWS Certified machine Learning Specialty Exam Prep MLS-C01 - Top 200 AWS and Google Certified Machine Learning Specialty Questions and Answers Dumps
AWS machine Learning Specialty Exam Prep MLS-C01

GCP Professional Machine Learning Engineer for iOs, Android, Windows 10/11

Quizzes, Practice Exams: Framing, Architecting, Designing, Developing ML Problems & Solutions, ML Jobs Interview Q&A

GCP Professional Machine Learning Engineer
GCP Professional Machine Learning Engineer

 

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Basics and Advanced Machine Learning Quizzes on Azure, Azure Machine Learning Job Interviews Questions and Answer, ML Cheat Sheets

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Azure AI Fundamentals AI-900 Exam Prep

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What does a Professional Machine Learning Engineer do?

Professional Machine Learning Engineer designs, builds, and productionizes ML models to solve business challenges using Google Cloud technologies and knowledge of proven ML models and techniques. The ML Engineer collaborates closely with other job roles to ensure long-term success of models. The ML Engineer should be proficient in all aspects of model architecture, data pipeline interaction, and metrics interpretation. The ML Engineer needs familiarity with application development, infrastructure management, data engineering, and security. Through an understanding of training, retraining, deploying, scheduling, monitoring, and improving models, they design and create scalable solutions for optimal performance.


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The AWS Certified Machine Learning – Specialty certification is intended for individuals who perform a development or data science role. It validates a candidate’s ability to design, implement, deploy, and maintain machine learning (ML) solutions for given business problems.

This blog covers Machine Learning 101, Top 20 AWS Certified Machine Learning Specialty Questions and Answers, Top 20 Google Professional Machine Learning Engineer Sample Questions, Machine Learning Quizzes, Machine Learning Q&A, Top 10 Machine Learning Algorithms, Machine Learning Latest Hot News, Machine Learning Demos (Ex: Tensorflow Demos)

Question1: A machine learning team has several large CSV datasets in Amazon S3. Historically, models built with the Amazon SageMaker Linear Learner algorithm have taken hours to train on similar-sized datasets. The team’s leaders need to accelerate the training process. What can a machine learning specialist do to address this concern?

A) Use Amazon SageMaker Pipe mode.
B) Use Amazon Machine Learning to train the models.
C) Use Amazon Kinesis to stream the data to Amazon SageMaker.
D) Use AWS Glue to transform the CSV dataset to the JSON format.
ANSWER1:

A

Notes/Hint1:


Amazon SageMaker Pipe mode streams the data directly to the container, which improves the performance of training jobs. (Refer to this link for supporting information.) In Pipe mode, your training job streams data directly from Amazon S3. Streaming can provide faster start times for training jobs and better throughput. With Pipe mode, you also reduce the size of the Amazon EBS volumes for your training instances. B would not apply in this scenario. C is a streaming ingestion solution, but is not applicable in this scenario. D transforms the data structure.

Reference1: Amazon SageMaker

Question 2) A local university wants to track cars in a parking lot to determine which students are parking in the lot. The university is wanting to ingest videos of the cars parking in near-real time, use machine learning to identify license plates, and store that data in an AWS data store. Which solution meets these requirements with the LEAST amount of development effort?

A) Use Amazon Kinesis Data Streams to ingest the video in near-real time, use the Kinesis Data Streams consumer integrated with Amazon Rekognition Video to process the license plate information, and then store results in DynamoDB.

B) Use Amazon Kinesis Video Streams to ingest the videos in near-real time, use the Kinesis Video Streams integration with Amazon Rekognition Video to identify the license plate information, and then store the results in DynamoDB.

C) Use Amazon Kinesis Data Streams to ingest videos in near-real time, call Amazon Rekognition to identify license plate information, and then store results in DynamoDB.

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D) Use Amazon Kinesis Firehose to ingest the video in near-real time and outputs results onto S3. Set up a Lambda function that triggers when a new video is PUT onto S3 to send results to Amazon Rekognition to identify license plate information, and then store results in DynamoDB.

Answer 2)

B

Notes/Hint2)

Kinesis Video Streams is used to stream videos in near-real time. Amazon Rekognition Video uses Amazon Kinesis Video Streams to receive and process a video stream. After the videos have been processed by Rekognition we can output the results in DynamoDB.

Reference: Kinesis Video Streams

Question 3) A term frequency–inverse document frequency (tf–idf) matrix using both unigrams and bigrams is built from a text corpus consisting of the following two sentences:

1. Please call the number below.
2. Please do not call us. What are the dimensions of the tf–idf matrix?
A) (2, 16)
B) (2, 8)
C) (2, 10)
D) (8, 10)

ANSWER3:

A

Notes/Hint3:

There are 2 sentences, 8 unique unigrams, and 8 unique bigrams, so the result would be (2,16). The phrases are “Please call the number below” and “Please do not call us.” Each word individually (unigram) is “Please,” “call,” ”the,” ”number,” “below,” “do,” “not,” and “us.” The unique bigrams are “Please call,” “call the,” ”the number,” “number below,” “Please do,” “do not,” “not call,” and “call us.” The tf–idf vectorizer is described at this link.

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Reference3:  tf-idf vertorizer

Question 4: A company is setting up a system to manage all of the datasets it stores in Amazon S3. The company would like to automate running transformation jobs on the data and maintaining a catalog of the metadata concerning the datasets. The solution should require the least amount of setup and maintenance. Which solution will allow the company to achieve its goals? 

A) Create an Amazon EMR cluster with Apache Hive installed. Then, create a Hive metastore and a script to run transformation jobs on a schedule.
B) Create an AWS Glue crawler to populate the AWS Glue Data Catalog. Then, author an AWS Glue ETL job, and set up a schedule for data transformation jobs.
C) Create an Amazon EMR cluster with Apache Spark installed. Then, create an Apache Hive metastore and a script to run transformation jobs on a schedule. D) Create an AWS Data Pipeline that transforms the data. Then, create an Apache Hive metastore and a script to run transformation jobs on a schedule.
 

ANSWER4:

B

Notes/Hint4:

AWS Glue is the correct answer because this option requires the least amount of setup and maintenance since it is serverless, and it does not require management of the infrastructure. Refer to this link for supporting information. A, C, and D are all solutions that can solve the problem, but require more steps for configuration, and require higher operational overhead to run and maintain.
Reference4:  Glue

Question 5) Which service in the Kinesis family allows you to easily load streaming data into data stores and analytics tools?

A) Kinesis Firehose
B) Kinesis Streams
C) Kinesis Data Analytics
D) Kinesis Video Streams
 

ANSWER5:

A

Notes/Hint5:

Kinesis Firehose is perfect for streaming data into AWS and sending it directly to its final destination – places like S3, Redshift, Elastisearch, and Splunk Instances.

Reference 5): Kinesis Firehose

Question 6) A data scientist is working on optimizing a model during the training process by varying multiple parameters. The data scientist observes that, during multiple runs with identical parameters, the loss function converges to different, yet stable, values. What should the data scientist do to improve the training process? 
A) Increase the learning rate. Keep the batch size the same.
B) Reduce the batch size. Decrease the learning rate.
C) Keep the batch size the same. Decrease the learning rate.
D) Do not change the learning rate. Increase the batch size.
 
Answer  6)
B
 

Notes 6)

It is most likely that the loss function is very curvy and has multiple local minima where the training is getting stuck. Decreasing the batch size would help the data scientist stochastically get out of the local minima saddles. Decreasing the learning rate would prevent overshooting the global loss function minimum. Refer to the paper at this link for an explanation.
Reference 6) : Here

Question 7) Your organization has a standalone Javascript (Node.js) application that streams data into AWS using Kinesis Data Streams. You notice that they are using the Kinesis API (AWS SDK) over the Kinesis Producer Library (KPL). What might be the reasoning behind this?
A) The Kinesis API (AWS SDK) provides greater functionality over the Kinesis Producer Library.
B) The Kinesis API (AWS SDK) runs faster in Javascript applications over the Kinesis Producer Library.
C) The Kinesis Producer Library must be installed as a Java application to use with Kinesis Data Streams.
D) The Kinesis Producer Library cannot be integrated with a Javascript application because of its asynchronous architecture.
Answer 7)
C
Notes/Hint7:
The KPL must be installed as a Java application before it can be used with your Kinesis Data Streams. There are ways to process KPL serialized data within AWS Lambda, in Java, Node.js, and Python, but not if these answers mentions Lambda.
Reference 7) KPL
 
 
Question 8) A data scientist is evaluating different binary classification models. A false positive result is 5 times more expensive (from a business perspective) than a false negative result. The models should be evaluated based on the following criteria: 
1) Must have a recall rate of at least 80%
2) Must have a false positive rate of 10% or less
3) Must minimize business costs After creating each binary classification model, the data scientist generates the corresponding confusion matrix. Which confusion matrix represents the model that satisfies the requirements?
A) TN = 91, FP = 9 FN = 22, TP = 78
 B) TN = 99, FP = 1 FN = 21, TP = 79
C) TN = 96, FP = 4 FN = 10, TP = 90
D) TN = 98, FP = 2 FN = 18, TP = 82
 
Answer 8): 
D
 

Notes/Hint 8)


The following calculations are required: TP = True Positive FP = False Positive FN = False Negative TN = True Negative FN = False Negative Recall = TP / (TP + FN) False Positive Rate (FPR) = FP / (FP + TN) Cost = 5 * FP + FN A B C D Recall 78 / (78 + 22) = 0.78 79 / (79 + 21) = 0.79 90 / (90 + 10) = 0.9 82 / (82 + 18) = 0.82 False Positive Rate 9 / (9 + 91) = 0.09 1 / (1 + 99) = 0.01 4 / (4 + 96) = 0.04 2 / (2 + 98) = 0.02 Costs 5 * 9 + 22 = 67 5 * 1 + 21 = 26 5 * 4 + 10 = 30 5 * 2 + 18 = 28 Options C and D have a recall greater than 80% and an FPR less than 10%, but D is the most cost effective. For supporting information, refer to this link.
Reference 8: Here

 
 
Question 9) A data scientist uses logistic regression to build a fraud detection model. While the model accuracy is 99%, 90% of the fraud cases are not detected by the model. What action will definitely help the model detect more than 10% of fraud cases? 
A) Using undersampling to balance the dataset
B) Decreasing the class probability threshold
C) Using regularization to reduce overfitting
D) Using oversampling to balance the dataset
 

Answer  9)

B

 

Notes 9)


Decreasing the class probability threshold makes the model more sensitive and, therefore, marks more cases as the positive class, which is fraud in this case. This will increase the likelihood of fraud detection. However, it comes at the price of lowering precision. This is covered in the Discussion section of the paper at this link
Reference 9: Here

 

 
Question 10) A company is interested in building a fraud detection model. Currently, the data scientist does not have a sufficient amount of information due to the low number of fraud cases. Which method is MOST likely to detect the GREATEST number of valid fraud cases?
A) Oversampling using bootstrapping
B) Undersampling
C) Oversampling using SMOTE
D) Class weight adjustment
 

Answer  10)

C

 
Notes 10)

With datasets that are not fully populated, the Synthetic Minority Over-sampling Technique (SMOTE) adds new information by adding synthetic data points to the minority class. This technique would be the most effective in this scenario. Refer to Section 4.2 at this link for supporting information.
Reference 10) : Here
 
Question 11) A machine learning engineer is preparing a data frame for a supervised learning task with the Amazon SageMaker Linear Learner algorithm. The ML engineer notices the target label classes are highly imbalanced and multiple feature columns contain missing values. The proportion of missing values across the entire data frame is less than 5%. What should the ML engineer do to minimize bias due to missing values? 
 
A) Replace each missing value by the mean or median across non-missing values in same row.
B) Delete observations that contain missing values because these represent less than 5% of the data.
C) Replace each missing value by the mean or median across non-missing values in the same column.
D) For each feature, approximate the missing values using supervised learning based on other features.
 

Answer  11)

D

 

Notes 11)

Use supervised learning to predict missing values based on the values of other features. Different supervised learning approaches might have different performances, but any properly implemented supervised learning approach should provide the same or better approximation than mean or median approximation, as proposed in responses A and C. Supervised learning applied to the imputation of missing values is an active field of research. Refer to this link for an example.
Reference 11): Here

 
Question 12) A company has collected customer comments on its products, rating them as safe or unsafe, using decision trees. The training dataset has the following features: id, date, full review, full review summary, and a binary safe/unsafe tag. During training, any data sample with missing features was dropped. In a few instances, the test set was found to be missing the full review text field. For this use case, which is the most effective course of action to address test data samples with missing features? 
A) Drop the test samples with missing full review text fields, and then run through the test set.
B) Copy the summary text fields and use them to fill in the missing full review text fields, and then run through the test set.
C) Use an algorithm that handles missing data better than decision trees.
D) Generate synthetic data to fill in the fields that are missing data, and then run through the test set.
 
Answer  12)
B

 

 

Notes 12) 

In this case, a full review summary usually contains the most descriptive phrases of the entire review and is a valid stand-in for the missing full review text field. For supporting information, refer to page 1627 at this link, and this link and this link.

Reference 12) Here

 

 
Question 13) An insurance company needs to automate claim compliance reviews because human reviews are expensive and error-prone. The company has a large set of claims and a compliance label for each. Each claim consists of a few sentences in English, many of which contain complex related information. Management would like to use Amazon SageMaker built-in algorithms to design a machine learning supervised model that can be trained to read each claim and predict if the claim is compliant or not. Which approach should be used to extract features from the claims to be used as inputs for the downstream supervised task? 
A) Derive a dictionary of tokens from claims in the entire dataset. Apply one-hot encoding to tokens found in each claim of the training set. Send the derived features space as inputs to an Amazon SageMaker builtin supervised learning algorithm.
B) Apply Amazon SageMaker BlazingText in Word2Vec mode to claims in the training set. Send the derived features space as inputs for the downstream supervised task.
C) Apply Amazon SageMaker BlazingText in classification mode to labeled claims in the training set to derive features for the claims that correspond to the compliant and non-compliant labels, respectively.
D) Apply Amazon SageMaker Object2Vec to claims in the training set. Send the derived features space as inputs for the downstream supervised task.
 

Answer  13)

D

 

Notes 13)

Amazon SageMaker Object2Vec generalizes the Word2Vec embedding technique for words to more complex objects, such as sentences and paragraphs. Since the supervised learning task is at the level of whole claims, for which there are labels, and no labels are available at the word level, Object2Vec needs be used instead of Word2Vec.

Reference 13)  Amazon SageMaker
Object2Vec 

Question 14) You have been tasked with capturing two different types of streaming events. The first event type includes mission-critical data that needs to immediately be processed before operations can continue. The second event type includes data of less importance, but operations can continue without immediately processing. What is the most appropriate solution to record these different types of events?

A) Capture both events with the PutRecords API call.
B) Capture both event types using the Kinesis Producer Library (KPL).
C) Capture the mission critical events with the PutRecords API call and the second event type with the Kinesis Producer Library (KPL).
D) Capture the mission critical events with the Kinesis Producer Library (KPL) and the second event type with the Putrecords API call.
 

Answer  14)

C

 

Notes 14)

The question is about sending data to Kinesis synchronously vs. asynchronously. PutRecords is a synchronous send function, so it must be used for the first event type (critical events). The Kinesis Producer Library (KPL) implements an asynchronous send function, so it can be used for the second event type. In this scenario, the reason to use the KPL over the PutRecords API call is because: KPL can incur an additional processing delay of up to RecordMaxBufferedTime within the library (user-configurable). Larger values of RecordMaxBufferedTime results in higher packing efficiencies and better performance. Applications that cannot tolerate this additional delay may need to use the AWS SDK directly. For more information about using the AWS SDK with Kinesis Data Streams, see Developing Producers Using the Amazon Kinesis Data Streams API with the AWS SDK for Java. For more information about RecordMaxBufferedTime and other user-configurable properties of the KPL, see Configuring the Kinesis Producer Library.

Reference 14: KCL vs PutRecords

 

Question 15) You are collecting clickstream data from an e-commerce website to make near-real time product suggestions for users actively using the site. Which combination of tools can be used to achieve the quickest recommendations and meets all of the requirements?

A) Use Kinesis Data Streams to ingest clickstream data, then use Kinesis Data Analytics to run real time SQL queries to gain actionable insights and trigger real-time recommendations with AWS Lambda functions based on conditions.
B) Use Kinesis Data Firehose to ingest click stream data, then use Kinesis Data Analytics to run real time SQL queries to gain actionable insights and trigger real-time recommendations with AWS Lambda functions based on conditions, then use Lambda to load these results into S3.
C) Use Kinesis Data Streams to ingest clickstream data, then use Lambda to process that data and write it to S3. Once the data is on S3, use Athena to query based on conditions that data and make real time recommendations to users.
D) Use the Kinesis Data Analytics to ingest the clickstream data directly and run real time SQL queries to gain actionable insights and trigger real-time recommendations with AWS Lambda functions based on conditions.
 

Answer  15)

A

 

Notes 15)

Kinesis Data Analytics gets its input streaming data from Kinesis Data Streams or Kinesis Data Firehose. You can use Kinesis Data Analytics to run real-time SQL queries on your data. Once certain conditions are met you can trigger Lambda functions to make real time product suggestions to users. It is not important that we store or persist the clickstream data.

Reference 15: Kinesis Data Analytics

Question 16) Which service built by AWS makes it easy to set up a retry mechanism, aggregate records to improve throughput, and automatically submits CloudWatch metrics?

A) Kinesis API (AWS SDK)
B) Kinesis Producer Library (KPL)
C) Kinesis Consumer Library
D) Kinesis Client Library (KCL)

Answer  16)

B

 

Notes 16)

Although the Kinesis API built into the AWS SDK can be used for all of this, the Kinesis Producer Library (KPL) makes it easy to integrate all of this into your applications.

Reference 16:  Kinesis Producer Library (KPL) 

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Question 17) You have been tasked with capturing data from an online gaming platform to run analytics on and process through a machine learning pipeline. The data that you are ingesting is players controller inputs every 1 second (up to 10 players in a game) that is in JSON format. The data needs to be ingested through Kinesis Data Streams and the JSON data blob is 100 KB in size. What is the minimum number of shards you can use to successfully ingest this data?

A) 10 shards
B) Greater than 500 shards, so you’ll need to request more shards from AWS
C) 1 shard
D) 100 shards

Answer  17)

C

 

Notes 17)

In this scenario, there will be a maximum of 10 records per second with a max payload size of 1000 KB (10 records x 100 KB = 1000KB) written to the shard. A single shard can ingest up to 1 MB of data per second, which is enough to ingest the 1000 KB from the streaming game play. Therefor 1 shard is enough to handle the streaming data.

Reference 17: shards

Question 18) Which services in the Kinesis family allows you to analyze streaming data, gain actionable insights, and respond to your business and customer needs in real time?

A) Kinesis Streams
B) Kinesis Firehose
C) Kinesis Video Streams
D) Kinesis Data Analytics

Answer  18)

D

 

Notes 18)

Kinesis Data Analytics allows you to run real-time SQL queries on your data to gain insights and respond to events in real time.

Reference 18: Kinesis Data Analytics

 

Question 19) You are a ML specialist needing to collect data from Twitter tweets. Your goal is to collect tweets that include only the name of your company and the tweet body, and store it off into a data store in AWS. What set of tools can you use to stream, transform, and load the data into AWS with the LEAST amount of effort?

A) Setup a Kinesis Data Firehose for data ingestion and immediately write that data to S3. Next, setup a Lambda function to trigger when data lands in S3 to transform it and finally write it to DynamoDB.
B) Setup A Kinesis Data Stream for data ingestion, setup EC2 instances as data consumers to poll and transform the data from the stream. Once the data is transformed, make an API call to write the data to DynamoDB.
C) Setup Kinesis Data Streams for data ingestion. Next, setup Kinesis Data Firehouse to load that data into RedShift. Next, setup a Lambda function to query data using RedShift spectrum and store the results onto DynamoDB.
D) Create a Kinesis Data Stream to ingest the data. Next, setup a Kinesis Data Firehose and use Lambda to transform the data from the Kinesis Data Stream, then use Lambda to write the data to DynamoDB. Finally, use S3 as the data destination for Kinesis Data Firehose.
 

Answer 19)

A

Notes 19)

All of these could be used to stream, transform, and load the data into an AWS data store. The setup that requires the LEAST amount of effort and moving parts involves setting up a Kinesis Data Firehose to stream the data into S3, have it transformed by Lambda with an S3 trigger, and then written to DynamoDB.

Reference 19: Kinesis Data Firehose to stream the data into S3

Question 20) Which service in the Kinesis family allows you to build custom applications that process or analyze streaming data for specialized needs?

A) Kinesis Firehose
B) Kinesis Streams
C) Kinesis Video Streams
D) Kinesis Data Analytics

Answer 20)

B

Notes 20)

Kinesis Streams allows you to stream data into AWS and build custom applications around that streaming data.

Reference 20: Kinesis Streams

Question21:

Answer21:

What are the Top 100 AWS and Google Certified Machine Learning Specialty Questions and Answers Dumps?

This blog is the best way  is the best way to prepare for your upcoming  AWS Certified Machine Learning Specialty and Google Certified Professional Machine Learning Engineer exam. With over 100 questions and answers, this blog provides quizzes similar  that are very similar to the real exam. It also includes  the option to show and hide answers. Additionally, there are machine learning interview questions and detailed answers, as well as cheat sheets and illustrations. This blog is the best way to make sure you are well-prepared for your AWS Certified Machine Learning Specialty Exam.

The typical Google Machine Learning Engineer salary is $147,218. Machine Learning Engineer salaries at Google can range from $110,000 – $152,183.

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.

  • By the end of 2020, 85% of customer interactions will be handled without a human (Call Center, Chatbot, etc…)
  • 61% of marketers say artificial intelligence is the most important aspect of their data strategy.
  • 80% of business and tech leaders say AI already boosts productivity (Robotic Process Automation, Power Automate, etc..)
  • Current AI technology can boost business productivity by up to 40%

AWS Machine Learning Certification Specialty Exam Prep for iOs Android Windows10/11

AWS machine Learning Specialty Exam Prep MLS-C01 - Top 200 AWS and Google Certified Machine Learning Specialty Questions and Answers Dumps
AWS machine Learning Specialty Exam Prep MLS-C01

GCP Professional Machine Learning Engineer for iOs, Android, Windows 10/11

Quizzes, Practice Exams: Framing, Architecting, Designing, Developing ML Problems & Solutions, ML Jobs Interview Q&A

GCP Professional Machine Learning Engineer
GCP Professional Machine Learning Engineer

 

Azure AI Fundamentals AI-900 Exam Prep App for iOS, Android, Windows10/11

Basics and Advanced Machine Learning Quizzes on Azure, Azure Machine Learning Job Interviews Questions and Answer, ML Cheat Sheets

Azure AI Fundamentals AI-900 Exam Prep
Azure AI Fundamentals AI-900 Exam Prep

Machine Learning For Dummies App for iOs, Android, Windows10/11

Use this App to learn about Machine Learning and Elevate your Brain with Machine Learning Quizzes, Cheat Sheets, Ml Jobs Interview Questions and Answers updated daily.

Machine Learning For Dummies
Machine Learning For Dummies

What does a Professional Machine Learning Engineer do?

Professional Machine Learning Engineer designs, builds, and productionizes ML models to solve business challenges using Google Cloud technologies and knowledge of proven ML models and techniques. The ML Engineer collaborates closely with other job roles to ensure long-term success of models. The ML Engineer should be proficient in all aspects of model architecture, data pipeline interaction, and metrics interpretation. The ML Engineer needs familiarity with application development, infrastructure management, data engineering, and security. Through an understanding of training, retraining, deploying, scheduling, monitoring, and improving models, they design and create scalable solutions for optimal performance.

The AWS Certified Machine Learning – Specialty certification is intended for individuals who perform a development or data science role. It validates a candidate’s ability to design, implement, deploy, and maintain machine learning (ML) solutions for given business problems.

This blog covers Machine Learning 101, Top 20 AWS Certified Machine Learning Specialty Questions and Answers, Top 20 Google Professional Machine Learning Engineer Sample Questions, Machine Learning Quizzes, Machine Learning Q&A, Top 10 Machine Learning Algorithms, Machine Learning Latest Hot News, Machine Learning Demos (Ex: Tensorflow Demos)

Below are the Top 100 AWS Certified Machine Learning Specialty Questions and Answers Dumps.

Top

 

Question1: A machine learning team has several large CSV datasets in Amazon S3. Historically, models built with the Amazon SageMaker Linear Learner algorithm have taken hours to train on similar-sized datasets. The team’s leaders need to accelerate the training process. What can a machine learning specialist do to address this concern?

A) Use Amazon SageMaker Pipe mode.
B) Use Amazon Machine Learning to train the models.
C) Use Amazon Kinesis to stream the data to Amazon SageMaker.
D) Use AWS Glue to transform the CSV dataset to the JSON format.
ANSWER1:

A

Notes/Hint1:


Amazon SageMaker Pipe mode streams the data directly to the container, which improves the performance of training jobs. (Refer to this link for supporting information.) In Pipe mode, your training job streams data directly from Amazon S3. Streaming can provide faster start times for training jobs and better throughput. With Pipe mode, you also reduce the size of the Amazon EBS volumes for your training instances. B would not apply in this scenario. C is a streaming ingestion solution, but is not applicable in this scenario. D transforms the data structure.

Reference1: Amazon SageMaker

Question 2) A local university wants to track cars in a parking lot to determine which students are parking in the lot. The university is wanting to ingest videos of the cars parking in near-real time, use machine learning to identify license plates, and store that data in an AWS data store. Which solution meets these requirements with the LEAST amount of development effort?

A) Use Amazon Kinesis Data Streams to ingest the video in near-real time, use the Kinesis Data Streams consumer integrated with Amazon Rekognition Video to process the license plate information, and then store results in DynamoDB.

B) Use Amazon Kinesis Video Streams to ingest the videos in near-real time, use the Kinesis Video Streams integration with Amazon Rekognition Video to identify the license plate information, and then store the results in DynamoDB.

C) Use Amazon Kinesis Data Streams to ingest videos in near-real time, call Amazon Rekognition to identify license plate information, and then store results in DynamoDB.

D) Use Amazon Kinesis Firehose to ingest the video in near-real time and outputs results onto S3. Set up a Lambda function that triggers when a new video is PUT onto S3 to send results to Amazon Rekognition to identify license plate information, and then store results in DynamoDB.

Answer 2)

B

Notes/Hint2)

Kinesis Video Streams is used to stream videos in near-real time. Amazon Rekognition Video uses Amazon Kinesis Video Streams to receive and process a video stream. After the videos have been processed by Rekognition we can output the results in DynamoDB.

Reference: Kinesis Video Streams

Question 3) A term frequency–inverse document frequency (tf–idf) matrix using both unigrams and bigrams is built from a text corpus consisting of the following two sentences:

1. Please call the number below.
2. Please do not call us. What are the dimensions of the tf–idf matrix?
A) (2, 16)
B) (2, 8)
C) (2, 10)
D) (8, 10)

ANSWER3:

A

Notes/Hint3:

There are 2 sentences, 8 unique unigrams, and 8 unique bigrams, so the result would be (2,16). The phrases are “Please call the number below” and “Please do not call us.” Each word individually (unigram) is “Please,” “call,” ”the,” ”number,” “below,” “do,” “not,” and “us.” The unique bigrams are “Please call,” “call the,” ”the number,” “number below,” “Please do,” “do not,” “not call,” and “call us.” The tf–idf vectorizer is described at this link.

Reference3:  tf-idf vertorizer

Question 4: A company is setting up a system to manage all of the datasets it stores in Amazon S3. The company would like to automate running transformation jobs on the data and maintaining a catalog of the metadata concerning the datasets. The solution should require the least amount of setup and maintenance. Which solution will allow the company to achieve its goals? 

A) Create an Amazon EMR cluster with Apache Hive installed. Then, create a Hive metastore and a script to run transformation jobs on a schedule.
B) Create an AWS Glue crawler to populate the AWS Glue Data Catalog. Then, author an AWS Glue ETL job, and set up a schedule for data transformation jobs.
C) Create an Amazon EMR cluster with Apache Spark installed. Then, create an Apache Hive metastore and a script to run transformation jobs on a schedule. D) Create an AWS Data Pipeline that transforms the data. Then, create an Apache Hive metastore and a script to run transformation jobs on a schedule.
 

ANSWER4:

B

Notes/Hint4:

AWS Glue is the correct answer because this option requires the least amount of setup and maintenance since it is serverless, and it does not require management of the infrastructure. Refer to this link for supporting information. A, C, and D are all solutions that can solve the problem, but require more steps for configuration, and require higher operational overhead to run and maintain.
Reference4:  Glue

Question 5) Which service in the Kinesis family allows you to easily load streaming data into data stores and analytics tools?

A) Kinesis Firehose
B) Kinesis Streams
C) Kinesis Data Analytics
D) Kinesis Video Streams
 

ANSWER5:

A

Notes/Hint5:

Kinesis Firehose is perfect for streaming data into AWS and sending it directly to its final destination – places like S3, Redshift, Elastisearch, and Splunk Instances.

Reference 5): Kinesis Firehose

Question 6) A data scientist is working on optimizing a model during the training process by varying multiple parameters. The data scientist observes that, during multiple runs with identical parameters, the loss function converges to different, yet stable, values. What should the data scientist do to improve the training process? 
A) Increase the learning rate. Keep the batch size the same.
B) Reduce the batch size. Decrease the learning rate.
C) Keep the batch size the same. Decrease the learning rate.
D) Do not change the learning rate. Increase the batch size.
 
Answer  6)
B
 

Notes 6)

It is most likely that the loss function is very curvy and has multiple local minima where the training is getting stuck. Decreasing the batch size would help the data scientist stochastically get out of the local minima saddles. Decreasing the learning rate would prevent overshooting the global loss function minimum. Refer to the paper at this link for an explanation.
Reference 6) : Here

Question 7) Your organization has a standalone Javascript (Node.js) application that streams data into AWS using Kinesis Data Streams. You notice that they are using the Kinesis API (AWS SDK) over the Kinesis Producer Library (KPL). What might be the reasoning behind this?
A) The Kinesis API (AWS SDK) provides greater functionality over the Kinesis Producer Library.
B) The Kinesis API (AWS SDK) runs faster in Javascript applications over the Kinesis Producer Library.
C) The Kinesis Producer Library must be installed as a Java application to use with Kinesis Data Streams.
D) The Kinesis Producer Library cannot be integrated with a Javascript application because of its asynchronous architecture.
Answer 7)
C
Notes/Hint7:
The KPL must be installed as a Java application before it can be used with your Kinesis Data Streams. There are ways to process KPL serialized data within AWS Lambda, in Java, Node.js, and Python, but not if these answers mentions Lambda.
Reference 7) KPL
 
 
Question 8) A data scientist is evaluating different binary classification models. A false positive result is 5 times more expensive (from a business perspective) than a false negative result. The models should be evaluated based on the following criteria: 
1) Must have a recall rate of at least 80%
2) Must have a false positive rate of 10% or less
3) Must minimize business costs After creating each binary classification model, the data scientist generates the corresponding confusion matrix. Which confusion matrix represents the model that satisfies the requirements?
A) TN = 91, FP = 9 FN = 22, TP = 78
 B) TN = 99, FP = 1 FN = 21, TP = 79
C) TN = 96, FP = 4 FN = 10, TP = 90
D) TN = 98, FP = 2 FN = 18, TP = 82
 
Answer 8): 
D
 

Notes/Hint 8)


The following calculations are required: TP = True Positive FP = False Positive FN = False Negative TN = True Negative FN = False Negative Recall = TP / (TP + FN) False Positive Rate (FPR) = FP / (FP + TN) Cost = 5 * FP + FN A B C D Recall 78 / (78 + 22) = 0.78 79 / (79 + 21) = 0.79 90 / (90 + 10) = 0.9 82 / (82 + 18) = 0.82 False Positive Rate 9 / (9 + 91) = 0.09 1 / (1 + 99) = 0.01 4 / (4 + 96) = 0.04 2 / (2 + 98) = 0.02 Costs 5 * 9 + 22 = 67 5 * 1 + 21 = 26 5 * 4 + 10 = 30 5 * 2 + 18 = 28 Options C and D have a recall greater than 80% and an FPR less than 10%, but D is the most cost effective. For supporting information, refer to this link.
Reference 8: Here

 
 
Question 9) A data scientist uses logistic regression to build a fraud detection model. While the model accuracy is 99%, 90% of the fraud cases are not detected by the model. What action will definitely help the model detect more than 10% of fraud cases? 
A) Using undersampling to balance the dataset
B) Decreasing the class probability threshold
C) Using regularization to reduce overfitting
D) Using oversampling to balance the dataset
 

Answer  9)

B

 

Notes 9)


Decreasing the class probability threshold makes the model more sensitive and, therefore, marks more cases as the positive class, which is fraud in this case. This will increase the likelihood of fraud detection. However, it comes at the price of lowering precision. This is covered in the Discussion section of the paper at this link
Reference 9: Here

 
 
Question 10) A company is interested in building a fraud detection model. Currently, the data scientist does not have a sufficient amount of information due to the low number of fraud cases. Which method is MOST likely to detect the GREATEST number of valid fraud cases?
A) Oversampling using bootstrapping
B) Undersampling
C) Oversampling using SMOTE
D) Class weight adjustment
 

Answer  10)

C

 
Notes 10)

With datasets that are not fully populated, the Synthetic Minority Over-sampling Technique (SMOTE) adds new information by adding synthetic data points to the minority class. This technique would be the most effective in this scenario. Refer to Section 4.2 at this link for supporting information.
Reference 10) : Here
 
Question 11) A machine learning engineer is preparing a data frame for a supervised learning task with the Amazon SageMaker Linear Learner algorithm. The ML engineer notices the target label classes are highly imbalanced and multiple feature columns contain missing values. The proportion of missing values across the entire data frame is less than 5%. What should the ML engineer do to minimize bias due to missing values? 
 
A) Replace each missing value by the mean or median across non-missing values in same row.
B) Delete observations that contain missing values because these represent less than 5% of the data.
C) Replace each missing value by the mean or median across non-missing values in the same column.
D) For each feature, approximate the missing values using supervised learning based on other features.
 

Answer  11)

D

 

Notes 11)

Use supervised learning to predict missing values based on the values of other features. Different supervised learning approaches might have different performances, but any properly implemented supervised learning approach should provide the same or better approximation than mean or median approximation, as proposed in responses A and C. Supervised learning applied to the imputation of missing values is an active field of research. Refer to this link for an example.
Reference 11): Here

 
Question 12) A company has collected customer comments on its products, rating them as safe or unsafe, using decision trees. The training dataset has the following features: id, date, full review, full review summary, and a binary safe/unsafe tag. During training, any data sample with missing features was dropped. In a few instances, the test set was found to be missing the full review text field. For this use case, which is the most effective course of action to address test data samples with missing features? 
A) Drop the test samples with missing full review text fields, and then run through the test set.
B) Copy the summary text fields and use them to fill in the missing full review text fields, and then run through the test set.
C) Use an algorithm that handles missing data better than decision trees.
D) Generate synthetic data to fill in the fields that are missing data, and then run through the test set.
 
Answer  12)
B

 

 

Notes 12) 

In this case, a full review summary usually contains the most descriptive phrases of the entire review and is a valid stand-in for the missing full review text field. For supporting information, refer to page 1627 at this link, and this link and this link.

Reference 12) Here

 

 
Question 13) An insurance company needs to automate claim compliance reviews because human reviews are expensive and error-prone. The company has a large set of claims and a compliance label for each. Each claim consists of a few sentences in English, many of which contain complex related information. Management would like to use Amazon SageMaker built-in algorithms to design a machine learning supervised model that can be trained to read each claim and predict if the claim is compliant or not. Which approach should be used to extract features from the claims to be used as inputs for the downstream supervised task? 
A) Derive a dictionary of tokens from claims in the entire dataset. Apply one-hot encoding to tokens found in each claim of the training set. Send the derived features space as inputs to an Amazon SageMaker builtin supervised learning algorithm.
B) Apply Amazon SageMaker BlazingText in Word2Vec mode to claims in the training set. Send the derived features space as inputs for the downstream supervised task.
C) Apply Amazon SageMaker BlazingText in classification mode to labeled claims in the training set to derive features for the claims that correspond to the compliant and non-compliant labels, respectively.
D) Apply Amazon SageMaker Object2Vec to claims in the training set. Send the derived features space as inputs for the downstream supervised task.
 

Answer  13)

D

 

Notes 13)

Amazon SageMaker Object2Vec generalizes the Word2Vec embedding technique for words to more complex objects, such as sentences and paragraphs. Since the supervised learning task is at the level of whole claims, for which there are labels, and no labels are available at the word level, Object2Vec needs be used instead of Word2Vec.

Reference 13)  Amazon SageMaker
Object2Vec 

Question 14) You have been tasked with capturing two different types of streaming events. The first event type includes mission-critical data that needs to immediately be processed before operations can continue. The second event type includes data of less importance, but operations can continue without immediately processing. What is the most appropriate solution to record these different types of events?

A) Capture both events with the PutRecords API call.
B) Capture both event types using the Kinesis Producer Library (KPL).
C) Capture the mission critical events with the PutRecords API call and the second event type with the Kinesis Producer Library (KPL).
D) Capture the mission critical events with the Kinesis Producer Library (KPL) and the second event type with the Putrecords API call.
 

Answer  14)

C

 

Notes 14)

The question is about sending data to Kinesis synchronously vs. asynchronously. PutRecords is a synchronous send function, so it must be used for the first event type (critical events). The Kinesis Producer Library (KPL) implements an asynchronous send function, so it can be used for the second event type. In this scenario, the reason to use the KPL over the PutRecords API call is because: KPL can incur an additional processing delay of up to RecordMaxBufferedTime within the library (user-configurable). Larger values of RecordMaxBufferedTime results in higher packing efficiencies and better performance. Applications that cannot tolerate this additional delay may need to use the AWS SDK directly. For more information about using the AWS SDK with Kinesis Data Streams, see Developing Producers Using the Amazon Kinesis Data Streams API with the AWS SDK for Java. For more information about RecordMaxBufferedTime and other user-configurable properties of the KPL, see Configuring the Kinesis Producer Library.

Reference 14: KCL vs PutRecords

 

Question 15) You are collecting clickstream data from an e-commerce website to make near-real time product suggestions for users actively using the site. Which combination of tools can be used to achieve the quickest recommendations and meets all of the requirements?

A) Use Kinesis Data Streams to ingest clickstream data, then use Kinesis Data Analytics to run real time SQL queries to gain actionable insights and trigger real-time recommendations with AWS Lambda functions based on conditions.
B) Use Kinesis Data Firehose to ingest click stream data, then use Kinesis Data Analytics to run real time SQL queries to gain actionable insights and trigger real-time recommendations with AWS Lambda functions based on conditions, then use Lambda to load these results into S3.
C) Use Kinesis Data Streams to ingest clickstream data, then use Lambda to process that data and write it to S3. Once the data is on S3, use Athena to query based on conditions that data and make real time recommendations to users.
D) Use the Kinesis Data Analytics to ingest the clickstream data directly and run real time SQL queries to gain actionable insights and trigger real-time recommendations with AWS Lambda functions based on conditions.
 

Answer  15)

A

 

Notes 15)

Kinesis Data Analytics gets its input streaming data from Kinesis Data Streams or Kinesis Data Firehose. You can use Kinesis Data Analytics to run real-time SQL queries on your data. Once certain conditions are met you can trigger Lambda functions to make real time product suggestions to users. It is not important that we store or persist the clickstream data.

Reference 15: Kinesis Data Analytics

Question 16) Which service built by AWS makes it easy to set up a retry mechanism, aggregate records to improve throughput, and automatically submits CloudWatch metrics?

A) Kinesis API (AWS SDK)
B) Kinesis Producer Library (KPL)
C) Kinesis Consumer Library
D) Kinesis Client Library (KCL)

Answer  16)

B

 

Notes 16)

Although the Kinesis API built into the AWS SDK can be used for all of this, the Kinesis Producer Library (KPL) makes it easy to integrate all of this into your applications.

Reference 16:  Kinesis Producer Library (KPL) 

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Question 17) You have been tasked with capturing data from an online gaming platform to run analytics on and process through a machine learning pipeline. The data that you are ingesting is players controller inputs every 1 second (up to 10 players in a game) that is in JSON format. The data needs to be ingested through Kinesis Data Streams and the JSON data blob is 100 KB in size. What is the minimum number of shards you can use to successfully ingest this data?

A) 10 shards
B) Greater than 500 shards, so you’ll need to request more shards from AWS
C) 1 shard
D) 100 shards

Answer  17)

C

 

Notes 17)

In this scenario, there will be a maximum of 10 records per second with a max payload size of 1000 KB (10 records x 100 KB = 1000KB) written to the shard. A single shard can ingest up to 1 MB of data per second, which is enough to ingest the 1000 KB from the streaming game play. Therefor 1 shard is enough to handle the streaming data.

Reference 17: shards

Question 18) Which services in the Kinesis family allows you to analyze streaming data, gain actionable insights, and respond to your business and customer needs in real time?

A) Kinesis Streams
B) Kinesis Firehose
C) Kinesis Video Streams
D) Kinesis Data Analytics

Answer  18)

D

 

Notes 18)

Kinesis Data Analytics allows you to run real-time SQL queries on your data to gain insights and respond to events in real time.

Reference 18: Kinesis Data Analytics

 

Question 19) You are a ML specialist needing to collect data from Twitter tweets. Your goal is to collect tweets that include only the name of your company and the tweet body, and store it off into a data store in AWS. What set of tools can you use to stream, transform, and load the data into AWS with the LEAST amount of effort?

A) Setup a Kinesis Data Firehose for data ingestion and immediately write that data to S3. Next, setup a Lambda function to trigger when data lands in S3 to transform it and finally write it to DynamoDB.
B) Setup A Kinesis Data Stream for data ingestion, setup EC2 instances as data consumers to poll and transform the data from the stream. Once the data is transformed, make an API call to write the data to DynamoDB.
C) Setup Kinesis Data Streams for data ingestion. Next, setup Kinesis Data Firehouse to load that data into RedShift. Next, setup a Lambda function to query data using RedShift spectrum and store the results onto DynamoDB.
D) Create a Kinesis Data Stream to ingest the data. Next, setup a Kinesis Data Firehose and use Lambda to transform the data from the Kinesis Data Stream, then use Lambda to write the data to DynamoDB. Finally, use S3 as the data destination for Kinesis Data Firehose.
 

Answer 19)

A

Notes 19)

All of these could be used to stream, transform, and load the data into an AWS data store. The setup that requires the LEAST amount of effort and moving parts involves setting up a Kinesis Data Firehose to stream the data into S3, have it transformed by Lambda with an S3 trigger, and then written to DynamoDB.

Reference 19: Kinesis Data Firehose to stream the data into S3

Question 20) Which service in the Kinesis family allows you to build custom applications that process or analyze streaming data for specialized needs?

A) Kinesis Firehose
B) Kinesis Streams
C) Kinesis Video Streams
D) Kinesis Data Analytics

Answer 20)

B

Notes 20)

Kinesis Streams allows you to stream data into AWS and build custom applications around that streaming data.

Reference 20: Kinesis Streams

Question21

Answer21:

 

Notes 21: 

Question22

Answer22:

 

Notes 22: 

Question23

Answer23:

 

Notes 23: 

Question24

Answer24:

 

Notes 24: 

What are the Top 100 AWS and Google Certified Machine Learning Specialty Questions and Answers Dumps?

This blog is the best way  is the best way to prepare for your upcoming  AWS Certified Machine Learning Specialty and Google Certified Professional Machine Learning Engineer exam. With over 100 questions and answers, this blog provides quizzes similar  that are very similar to the real exam. It also includes  the option to show and hide answers. Additionally, there are machine learning interview questions and detailed answers, as well as cheat sheets and illustrations. This blog is the best way to make sure you are well-prepared for your AWS Certified Machine Learning Specialty Exam.

The typical Google Machine Learning Engineer salary is $147,218. Machine Learning Engineer salaries at Google can range from $110,000 – $152,183.

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.

  • By the end of 2020, 85% of customer interactions will be handled without a human (Call Center, Chatbot, etc…)
  • 61% of marketers say artificial intelligence is the most important aspect of their data strategy.
  • 80% of business and tech leaders say AI already boosts productivity (Robotic Process Automation, Power Automate, etc..)
  • Current AI technology can boost business productivity by up to 40%

AWS Machine Learning Certification Specialty Exam Prep for iOs Android Windows10/11

AWS machine Learning Specialty Exam Prep MLS-C01
AWS machine Learning Specialty Exam Prep MLS-C01

GCP Professional Machine Learning Engineer for iOs, Android, Windows 10/11

Quizzes, Practice Exams: Framing, Architecting, Designing, Developing ML Problems & Solutions, ML Jobs Interview Q&A

GCP Professional Machine Learning Engineer
GCP Professional Machine Learning Engineer

 

Azure AI Fundamentals AI-900 Exam Prep App for iOS, Android, Windows10/11

Basics and Advanced Machine Learning Quizzes on Azure, Azure Machine Learning Job Interviews Questions and Answer, ML Cheat Sheets

Azure AI Fundamentals AI-900 Exam Prep
Azure AI Fundamentals AI-900 Exam Prep

Machine Learning For Dummies App for iOs, Android, Windows10/11

Use this App to learn about Machine Learning and Elevate your Brain with Machine Learning Quizzes, Cheat Sheets, Ml Jobs Interview Questions and Answers updated daily.

Machine Learning For Dummies
Machine Learning For Dummies

What does a Professional Machine Learning Engineer do?

Professional Machine Learning Engineer designs, builds, and productionizes ML models to solve business challenges using Google Cloud technologies and knowledge of proven ML models and techniques. The ML Engineer collaborates closely with other job roles to ensure long-term success of models. The ML Engineer should be proficient in all aspects of model architecture, data pipeline interaction, and metrics interpretation. The ML Engineer needs familiarity with application development, infrastructure management, data engineering, and security. Through an understanding of training, retraining, deploying, scheduling, monitoring, and improving models, they design and create scalable solutions for optimal performance.

The AWS Certified Machine Learning – Specialty certification is intended for individuals who perform a development or data science role. It validates a candidate’s ability to design, implement, deploy, and maintain machine learning (ML) solutions for given business problems.

This blog covers Machine Learning 101, Top 20 AWS Certified Machine Learning Specialty Questions and Answers, Top 20 Google Professional Machine Learning Engineer Sample Questions, Machine Learning Quizzes, Machine Learning Q&A, Top 10 Machine Learning Algorithms, Machine Learning Latest Hot News, Machine Learning Demos (Ex: Tensorflow Demos)

Below are the Top 100 AWS Certified Machine Learning Specialty Questions and Answers Dumps.

Top

 

Question1: A machine learning team has several large CSV datasets in Amazon S3. Historically, models built with the Amazon SageMaker Linear Learner algorithm have taken hours to train on similar-sized datasets. The team’s leaders need to accelerate the training process. What can a machine learning specialist do to address this concern?

A) Use Amazon SageMaker Pipe mode.
B) Use Amazon Machine Learning to train the models.
C) Use Amazon Kinesis to stream the data to Amazon SageMaker.
D) Use AWS Glue to transform the CSV dataset to the JSON format.
ANSWER1:

A

Notes/Hint1:


Amazon SageMaker Pipe mode streams the data directly to the container, which improves the performance of training jobs. (Refer to this link for supporting information.) In Pipe mode, your training job streams data directly from Amazon S3. Streaming can provide faster start times for training jobs and better throughput. With Pipe mode, you also reduce the size of the Amazon EBS volumes for your training instances. B would not apply in this scenario. C is a streaming ingestion solution, but is not applicable in this scenario. D transforms the data structure.

Reference1: Amazon SageMaker

Question 2) A local university wants to track cars in a parking lot to determine which students are parking in the lot. The university is wanting to ingest videos of the cars parking in near-real time, use machine learning to identify license plates, and store that data in an AWS data store. Which solution meets these requirements with the LEAST amount of development effort?

A) Use Amazon Kinesis Data Streams to ingest the video in near-real time, use the Kinesis Data Streams consumer integrated with Amazon Rekognition Video to process the license plate information, and then store results in DynamoDB.

B) Use Amazon Kinesis Video Streams to ingest the videos in near-real time, use the Kinesis Video Streams integration with Amazon Rekognition Video to identify the license plate information, and then store the results in DynamoDB.

C) Use Amazon Kinesis Data Streams to ingest videos in near-real time, call Amazon Rekognition to identify license plate information, and then store results in DynamoDB.

D) Use Amazon Kinesis Firehose to ingest the video in near-real time and outputs results onto S3. Set up a Lambda function that triggers when a new video is PUT onto S3 to send results to Amazon Rekognition to identify license plate information, and then store results in DynamoDB.

Answer 2)

B

Notes/Hint2)

Kinesis Video Streams is used to stream videos in near-real time. Amazon Rekognition Video uses Amazon Kinesis Video Streams to receive and process a video stream. After the videos have been processed by Rekognition we can output the results in DynamoDB.

Reference: Kinesis Video Streams

Question 3) A term frequency–inverse document frequency (tf–idf) matrix using both unigrams and bigrams is built from a text corpus consisting of the following two sentences:

1. Please call the number below.
2. Please do not call us. What are the dimensions of the tf–idf matrix?
A) (2, 16)
B) (2, 8)
C) (2, 10)
D) (8, 10)

ANSWER3:

A

Notes/Hint3:

There are 2 sentences, 8 unique unigrams, and 8 unique bigrams, so the result would be (2,16). The phrases are “Please call the number below” and “Please do not call us.” Each word individually (unigram) is “Please,” “call,” ”the,” ”number,” “below,” “do,” “not,” and “us.” The unique bigrams are “Please call,” “call the,” ”the number,” “number below,” “Please do,” “do not,” “not call,” and “call us.” The tf–idf vectorizer is described at this link.

Reference3:  tf-idf vertorizer

Question 4: A company is setting up a system to manage all of the datasets it stores in Amazon S3. The company would like to automate running transformation jobs on the data and maintaining a catalog of the metadata concerning the datasets. The solution should require the least amount of setup and maintenance. Which solution will allow the company to achieve its goals? 

A) Create an Amazon EMR cluster with Apache Hive installed. Then, create a Hive metastore and a script to run transformation jobs on a schedule.
B) Create an AWS Glue crawler to populate the AWS Glue Data Catalog. Then, author an AWS Glue ETL job, and set up a schedule for data transformation jobs.
C) Create an Amazon EMR cluster with Apache Spark installed. Then, create an Apache Hive metastore and a script to run transformation jobs on a schedule. D) Create an AWS Data Pipeline that transforms the data. Then, create an Apache Hive metastore and a script to run transformation jobs on a schedule.
 

ANSWER4:

B

Notes/Hint4:

AWS Glue is the correct answer because this option requires the least amount of setup and maintenance since it is serverless, and it does not require management of the infrastructure. Refer to this link for supporting information. A, C, and D are all solutions that can solve the problem, but require more steps for configuration, and require higher operational overhead to run and maintain.
Reference4:  Glue

Question 5) Which service in the Kinesis family allows you to easily load streaming data into data stores and analytics tools?

A) Kinesis Firehose
B) Kinesis Streams
C) Kinesis Data Analytics
D) Kinesis Video Streams
 

ANSWER5:

A

Notes/Hint5:

Kinesis Firehose is perfect for streaming data into AWS and sending it directly to its final destination – places like S3, Redshift, Elastisearch, and Splunk Instances.

Reference 5): Kinesis Firehose

Question 6) A data scientist is working on optimizing a model during the training process by varying multiple parameters. The data scientist observes that, during multiple runs with identical parameters, the loss function converges to different, yet stable, values. What should the data scientist do to improve the training process? 
A) Increase the learning rate. Keep the batch size the same.
B) Reduce the batch size. Decrease the learning rate.
C) Keep the batch size the same. Decrease the learning rate.
D) Do not change the learning rate. Increase the batch size.
 
Answer  6)
B
 

Notes 6)

It is most likely that the loss function is very curvy and has multiple local minima where the training is getting stuck. Decreasing the batch size would help the data scientist stochastically get out of the local minima saddles. Decreasing the learning rate would prevent overshooting the global loss function minimum. Refer to the paper at this link for an explanation.
Reference 6) : Here

Question 7) Your organization has a standalone Javascript (Node.js) application that streams data into AWS using Kinesis Data Streams. You notice that they are using the Kinesis API (AWS SDK) over the Kinesis Producer Library (KPL). What might be the reasoning behind this?
A) The Kinesis API (AWS SDK) provides greater functionality over the Kinesis Producer Library.
B) The Kinesis API (AWS SDK) runs faster in Javascript applications over the Kinesis Producer Library.
C) The Kinesis Producer Library must be installed as a Java application to use with Kinesis Data Streams.
D) The Kinesis Producer Library cannot be integrated with a Javascript application because of its asynchronous architecture.
Answer 7)
C
Notes/Hint7:
The KPL must be installed as a Java application before it can be used with your Kinesis Data Streams. There are ways to process KPL serialized data within AWS Lambda, in Java, Node.js, and Python, but not if these answers mentions Lambda.
Reference 7) KPL
 
 
Question 8) A data scientist is evaluating different binary classification models. A false positive result is 5 times more expensive (from a business perspective) than a false negative result. The models should be evaluated based on the following criteria: 
1) Must have a recall rate of at least 80%
2) Must have a false positive rate of 10% or less
3) Must minimize business costs After creating each binary classification model, the data scientist generates the corresponding confusion matrix. Which confusion matrix represents the model that satisfies the requirements?
A) TN = 91, FP = 9 FN = 22, TP = 78
 B) TN = 99, FP = 1 FN = 21, TP = 79
C) TN = 96, FP = 4 FN = 10, TP = 90
D) TN = 98, FP = 2 FN = 18, TP = 82
 
Answer 8): 
D
 

Notes/Hint 8)


The following calculations are required: TP = True Positive FP = False Positive FN = False Negative TN = True Negative FN = False Negative Recall = TP / (TP + FN) False Positive Rate (FPR) = FP / (FP + TN) Cost = 5 * FP + FN A B C D Recall 78 / (78 + 22) = 0.78 79 / (79 + 21) = 0.79 90 / (90 + 10) = 0.9 82 / (82 + 18) = 0.82 False Positive Rate 9 / (9 + 91) = 0.09 1 / (1 + 99) = 0.01 4 / (4 + 96) = 0.04 2 / (2 + 98) = 0.02 Costs 5 * 9 + 22 = 67 5 * 1 + 21 = 26 5 * 4 + 10 = 30 5 * 2 + 18 = 28 Options C and D have a recall greater than 80% and an FPR less than 10%, but D is the most cost effective. For supporting information, refer to this link.
Reference 8: Here

 
 
Question 9) A data scientist uses logistic regression to build a fraud detection model. While the model accuracy is 99%, 90% of the fraud cases are not detected by the model. What action will definitely help the model detect more than 10% of fraud cases? 
A) Using undersampling to balance the dataset
B) Decreasing the class probability threshold
C) Using regularization to reduce overfitting
D) Using oversampling to balance the dataset
 

Answer  9)

B

 

Notes 9)


Decreasing the class probability threshold makes the model more sensitive and, therefore, marks more cases as the positive class, which is fraud in this case. This will increase the likelihood of fraud detection. However, it comes at the price of lowering precision. This is covered in the Discussion section of the paper at this link
Reference 9: Here

 
 
Question 10) A company is interested in building a fraud detection model. Currently, the data scientist does not have a sufficient amount of information due to the low number of fraud cases. Which method is MOST likely to detect the GREATEST number of valid fraud cases?
A) Oversampling using bootstrapping
B) Undersampling
C) Oversampling using SMOTE
D) Class weight adjustment
 

Answer  10)

C

 
Notes 10)

With datasets that are not fully populated, the Synthetic Minority Over-sampling Technique (SMOTE) adds new information by adding synthetic data points to the minority class. This technique would be the most effective in this scenario. Refer to Section 4.2 at this link for supporting information.
Reference 10) : Here
 
Question 11) A machine learning engineer is preparing a data frame for a supervised learning task with the Amazon SageMaker Linear Learner algorithm. The ML engineer notices the target label classes are highly imbalanced and multiple feature columns contain missing values. The proportion of missing values across the entire data frame is less than 5%. What should the ML engineer do to minimize bias due to missing values? 
 
A) Replace each missing value by the mean or median across non-missing values in same row.
B) Delete observations that contain missing values because these represent less than 5% of the data.
C) Replace each missing value by the mean or median across non-missing values in the same column.
D) For each feature, approximate the missing values using supervised learning based on other features.
 

Answer  11)

D

 

Notes 11)

Use supervised learning to predict missing values based on the values of other features. Different supervised learning approaches might have different performances, but any properly implemented supervised learning approach should provide the same or better approximation than mean or median approximation, as proposed in responses A and C. Supervised learning applied to the imputation of missing values is an active field of research. Refer to this link for an example.
Reference 11): Here

 
Question 12) A company has collected customer comments on its products, rating them as safe or unsafe, using decision trees. The training dataset has the following features: id, date, full review, full review summary, and a binary safe/unsafe tag. During training, any data sample with missing features was dropped. In a few instances, the test set was found to be missing the full review text field. For this use case, which is the most effective course of action to address test data samples with missing features? 
A) Drop the test samples with missing full review text fields, and then run through the test set.
B) Copy the summary text fields and use them to fill in the missing full review text fields, and then run through the test set.
C) Use an algorithm that handles missing data better than decision trees.
D) Generate synthetic data to fill in the fields that are missing data, and then run through the test set.
 
Answer  12)
B

 

 

Notes 12) 

In this case, a full review summary usually contains the most descriptive phrases of the entire review and is a valid stand-in for the missing full review text field. For supporting information, refer to page 1627 at this link, and this link and this link.

Reference 12) Here

 

 
Question 13) An insurance company needs to automate claim compliance reviews because human reviews are expensive and error-prone. The company has a large set of claims and a compliance label for each. Each claim consists of a few sentences in English, many of which contain complex related information. Management would like to use Amazon SageMaker built-in algorithms to design a machine learning supervised model that can be trained to read each claim and predict if the claim is compliant or not. Which approach should be used to extract features from the claims to be used as inputs for the downstream supervised task? 
A) Derive a dictionary of tokens from claims in the entire dataset. Apply one-hot encoding to tokens found in each claim of the training set. Send the derived features space as inputs to an Amazon SageMaker builtin supervised learning algorithm.
B) Apply Amazon SageMaker BlazingText in Word2Vec mode to claims in the training set. Send the derived features space as inputs for the downstream supervised task.
C) Apply Amazon SageMaker BlazingText in classification mode to labeled claims in the training set to derive features for the claims that correspond to the compliant and non-compliant labels, respectively.
D) Apply Amazon SageMaker Object2Vec to claims in the training set. Send the derived features space as inputs for the downstream supervised task.
 

Answer  13)

D

 

Notes 13)

Amazon SageMaker Object2Vec generalizes the Word2Vec embedding technique for words to more complex objects, such as sentences and paragraphs. Since the supervised learning task is at the level of whole claims, for which there are labels, and no labels are available at the word level, Object2Vec needs be used instead of Word2Vec.

Reference 13)  Amazon SageMaker
Object2Vec 

Question 14) You have been tasked with capturing two different types of streaming events. The first event type includes mission-critical data that needs to immediately be processed before operations can continue. The second event type includes data of less importance, but operations can continue without immediately processing. What is the most appropriate solution to record these different types of events?

A) Capture both events with the PutRecords API call.
B) Capture both event types using the Kinesis Producer Library (KPL).
C) Capture the mission critical events with the PutRecords API call and the second event type with the Kinesis Producer Library (KPL).
D) Capture the mission critical events with the Kinesis Producer Library (KPL) and the second event type with the Putrecords API call.
 

Answer  14)

C

 

Notes 14)

The question is about sending data to Kinesis synchronously vs. asynchronously. PutRecords is a synchronous send function, so it must be used for the first event type (critical events). The Kinesis Producer Library (KPL) implements an asynchronous send function, so it can be used for the second event type. In this scenario, the reason to use the KPL over the PutRecords API call is because: KPL can incur an additional processing delay of up to RecordMaxBufferedTime within the library (user-configurable). Larger values of RecordMaxBufferedTime results in higher packing efficiencies and better performance. Applications that cannot tolerate this additional delay may need to use the AWS SDK directly. For more information about using the AWS SDK with Kinesis Data Streams, see Developing Producers Using the Amazon Kinesis Data Streams API with the AWS SDK for Java. For more information about RecordMaxBufferedTime and other user-configurable properties of the KPL, see Configuring the Kinesis Producer Library.

Reference 14: KCL vs PutRecords

 

Question 15) You are collecting clickstream data from an e-commerce website to make near-real time product suggestions for users actively using the site. Which combination of tools can be used to achieve the quickest recommendations and meets all of the requirements?

A) Use Kinesis Data Streams to ingest clickstream data, then use Kinesis Data Analytics to run real time SQL queries to gain actionable insights and trigger real-time recommendations with AWS Lambda functions based on conditions.
B) Use Kinesis Data Firehose to ingest click stream data, then use Kinesis Data Analytics to run real time SQL queries to gain actionable insights and trigger real-time recommendations with AWS Lambda functions based on conditions, then use Lambda to load these results into S3.
C) Use Kinesis Data Streams to ingest clickstream data, then use Lambda to process that data and write it to S3. Once the data is on S3, use Athena to query based on conditions that data and make real time recommendations to users.
D) Use the Kinesis Data Analytics to ingest the clickstream data directly and run real time SQL queries to gain actionable insights and trigger real-time recommendations with AWS Lambda functions based on conditions.
 

Answer  15)

A

 

Notes 15)

Kinesis Data Analytics gets its input streaming data from Kinesis Data Streams or Kinesis Data Firehose. You can use Kinesis Data Analytics to run real-time SQL queries on your data. Once certain conditions are met you can trigger Lambda functions to make real time product suggestions to users. It is not important that we store or persist the clickstream data.

Reference 15: Kinesis Data Analytics

Question 16) Which service built by AWS makes it easy to set up a retry mechanism, aggregate records to improve throughput, and automatically submits CloudWatch metrics?

A) Kinesis API (AWS SDK)
B) Kinesis Producer Library (KPL)
C) Kinesis Consumer Library
D) Kinesis Client Library (KCL)

Answer  16)

B

 

Notes 16)

Although the Kinesis API built into the AWS SDK can be used for all of this, the Kinesis Producer Library (KPL) makes it easy to integrate all of this into your applications.

Reference 16:  Kinesis Producer Library (KPL) 

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Question 17) You have been tasked with capturing data from an online gaming platform to run analytics on and process through a machine learning pipeline. The data that you are ingesting is players controller inputs every 1 second (up to 10 players in a game) that is in JSON format. The data needs to be ingested through Kinesis Data Streams and the JSON data blob is 100 KB in size. What is the minimum number of shards you can use to successfully ingest this data?

A) 10 shards
B) Greater than 500 shards, so you’ll need to request more shards from AWS
C) 1 shard
D) 100 shards

Answer  17)

C

 

Notes 17)

In this scenario, there will be a maximum of 10 records per second with a max payload size of 1000 KB (10 records x 100 KB = 1000KB) written to the shard. A single shard can ingest up to 1 MB of data per second, which is enough to ingest the 1000 KB from the streaming game play. Therefor 1 shard is enough to handle the streaming data.

Reference 17: shards

Question 18) Which services in the Kinesis family allows you to analyze streaming data, gain actionable insights, and respond to your business and customer needs in real time?

A) Kinesis Streams
B) Kinesis Firehose
C) Kinesis Video Streams
D) Kinesis Data Analytics

Answer  18)

D

 

Notes 18)

Kinesis Data Analytics allows you to run real-time SQL queries on your data to gain insights and respond to events in real time.

Reference 18: Kinesis Data Analytics

 

Question 19) You are a ML specialist needing to collect data from Twitter tweets. Your goal is to collect tweets that include only the name of your company and the tweet body, and store it off into a data store in AWS. What set of tools can you use to stream, transform, and load the data into AWS with the LEAST amount of effort?

A) Setup a Kinesis Data Firehose for data ingestion and immediately write that data to S3. Next, setup a Lambda function to trigger when data lands in S3 to transform it and finally write it to DynamoDB.
B) Setup A Kinesis Data Stream for data ingestion, setup EC2 instances as data consumers to poll and transform the data from the stream. Once the data is transformed, make an API call to write the data to DynamoDB.
C) Setup Kinesis Data Streams for data ingestion. Next, setup Kinesis Data Firehouse to load that data into RedShift. Next, setup a Lambda function to query data using RedShift spectrum and store the results onto DynamoDB.
D) Create a Kinesis Data Stream to ingest the data. Next, setup a Kinesis Data Firehose and use Lambda to transform the data from the Kinesis Data Stream, then use Lambda to write the data to DynamoDB. Finally, use S3 as the data destination for Kinesis Data Firehose.
 

Answer 19)

A

Notes 19)

All of these could be used to stream, transform, and load the data into an AWS data store. The setup that requires the LEAST amount of effort and moving parts involves setting up a Kinesis Data Firehose to stream the data into S3, have it transformed by Lambda with an S3 trigger, and then written to DynamoDB.

Reference 19: Kinesis Data Firehose to stream the data into S3

Question 20) Which service in the Kinesis family allows you to build custom applications that process or analyze streaming data for specialized needs?

A) Kinesis Firehose
B) Kinesis Streams
C) Kinesis Video Streams
D) Kinesis Data Analytics

Answer 20)

B

Notes 20)

Kinesis Streams allows you to stream data into AWS and build custom applications around that streaming data.

Reference 20: Kinesis Streams

Question21:

Answer21:

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Below are the Top 100 AWS Certified Machine Learning Specialty Questions and Answers Dumps.

Top

 

Question1: A machine learning team has several large CSV datasets in Amazon S3. Historically, models built with the Amazon SageMaker Linear Learner algorithm have taken hours to train on similar-sized datasets. The team’s leaders need to accelerate the training process. What can a machine learning specialist do to address this concern?

A) Use Amazon SageMaker Pipe mode.
B) Use Amazon Machine Learning to train the models.
C) Use Amazon Kinesis to stream the data to Amazon SageMaker.
D) Use AWS Glue to transform the CSV dataset to the JSON format.
ANSWER1:

A

Notes/Hint1:


Amazon SageMaker Pipe mode streams the data directly to the container, which improves the performance of training jobs. (Refer to this link for supporting information.) In Pipe mode, your training job streams data directly from Amazon S3. Streaming can provide faster start times for training jobs and better throughput. With Pipe mode, you also reduce the size of the Amazon EBS volumes for your training instances. B would not apply in this scenario. C is a streaming ingestion solution, but is not applicable in this scenario. D transforms the data structure.

Reference1: Amazon SageMaker

Question 2) A local university wants to track cars in a parking lot to determine which students are parking in the lot. The university is wanting to ingest videos of the cars parking in near-real time, use machine learning to identify license plates, and store that data in an AWS data store. Which solution meets these requirements with the LEAST amount of development effort?

A) Use Amazon Kinesis Data Streams to ingest the video in near-real time, use the Kinesis Data Streams consumer integrated with Amazon Rekognition Video to process the license plate information, and then store results in DynamoDB.

B) Use Amazon Kinesis Video Streams to ingest the videos in near-real time, use the Kinesis Video Streams integration with Amazon Rekognition Video to identify the license plate information, and then store the results in DynamoDB.

C) Use Amazon Kinesis Data Streams to ingest videos in near-real time, call Amazon Rekognition to identify license plate information, and then store results in DynamoDB.

D) Use Amazon Kinesis Firehose to ingest the video in near-real time and outputs results onto S3. Set up a Lambda function that triggers when a new video is PUT onto S3 to send results to Amazon Rekognition to identify license plate information, and then store results in DynamoDB.

Answer 2)

B

Notes/Hint2)

Kinesis Video Streams is used to stream videos in near-real time. Amazon Rekognition Video uses Amazon Kinesis Video Streams to receive and process a video stream. After the videos have been processed by Rekognition we can output the results in DynamoDB.

Reference: Kinesis Video Streams

Question 3) A term frequency–inverse document frequency (tf–idf) matrix using both unigrams and bigrams is built from a text corpus consisting of the following two sentences:

1. Please call the number below.
2. Please do not call us. What are the dimensions of the tf–idf matrix?
A) (2, 16)
B) (2, 8)
C) (2, 10)
D) (8, 10)

ANSWER3:

A

Notes/Hint3:

There are 2 sentences, 8 unique unigrams, and 8 unique bigrams, so the result would be (2,16). The phrases are “Please call the number below” and “Please do not call us.” Each word individually (unigram) is “Please,” “call,” ”the,” ”number,” “below,” “do,” “not,” and “us.” The unique bigrams are “Please call,” “call the,” ”the number,” “number below,” “Please do,” “do not,” “not call,” and “call us.” The tf–idf vectorizer is described at this link.

Reference3:  tf-idf vertorizer

Question 4: A company is setting up a system to manage all of the datasets it stores in Amazon S3. The company would like to automate running transformation jobs on the data and maintaining a catalog of the metadata concerning the datasets. The solution should require the least amount of setup and maintenance. Which solution will allow the company to achieve its goals? 

A) Create an Amazon EMR cluster with Apache Hive installed. Then, create a Hive metastore and a script to run transformation jobs on a schedule.
B) Create an AWS Glue crawler to populate the AWS Glue Data Catalog. Then, author an AWS Glue ETL job, and set up a schedule for data transformation jobs.
C) Create an Amazon EMR cluster with Apache Spark installed. Then, create an Apache Hive metastore and a script to run transformation jobs on a schedule. D) Create an AWS Data Pipeline that transforms the data. Then, create an Apache Hive metastore and a script to run transformation jobs on a schedule.
 

ANSWER4:

B

Notes/Hint4:

AWS Glue is the correct answer because this option requires the least amount of setup and maintenance since it is serverless, and it does not require management of the infrastructure. Refer to this link for supporting information. A, C, and D are all solutions that can solve the problem, but require more steps for configuration, and require higher operational overhead to run and maintain.
Reference4:  Glue

Question 5) Which service in the Kinesis family allows you to easily load streaming data into data stores and analytics tools?

A) Kinesis Firehose
B) Kinesis Streams
C) Kinesis Data Analytics
D) Kinesis Video Streams
 

ANSWER5:

A

Notes/Hint5:

Kinesis Firehose is perfect for streaming data into AWS and sending it directly to its final destination – places like S3, Redshift, Elastisearch, and Splunk Instances.

Reference 5): Kinesis Firehose

Question 6) A data scientist is working on optimizing a model during the training process by varying multiple parameters. The data scientist observes that, during multiple runs with identical parameters, the loss function converges to different, yet stable, values. What should the data scientist do to improve the training process? 

A) Increase the learning rate. Keep the batch size the same.
B) Reduce the batch size. Decrease the learning rate.
C) Keep the batch size the same. Decrease the learning rate.
D) Do not change the learning rate. Increase the batch size.
 
Answer  6)
B
 

Notes 6)

It is most likely that the loss function is very curvy and has multiple local minima where the training is getting stuck. Decreasing the batch size would help the data scientist stochastically get out of the local minima saddles. Decreasing the learning rate would prevent overshooting the global loss function minimum. Refer to the paper at this link for an explanation.
Reference 6) : Here

Question 7) Your organization has a standalone Javascript (Node.js) application that streams data into AWS using Kinesis Data Streams. You notice that they are using the Kinesis API (AWS SDK) over the Kinesis Producer Library (KPL). What might be the reasoning behind this?

A) The Kinesis API (AWS SDK) provides greater functionality over the Kinesis Producer Library.
B) The Kinesis API (AWS SDK) runs faster in Javascript applications over the Kinesis Producer Library.
C) The Kinesis Producer Library must be installed as a Java application to use with Kinesis Data Streams.
D) The Kinesis Producer Library cannot be integrated with a Javascript application because of its asynchronous architecture.
Answer 7)
C
Notes/Hint7:
The KPL must be installed as a Java application before it can be used with your Kinesis Data Streams. There are ways to process KPL serialized data within AWS Lambda, in Java, Node.js, and Python, but not if these answers mentions Lambda.
Reference 7) KPL
 
 

Question 8) A data scientist is evaluating different binary classification models. A false positive result is 5 times more expensive (from a business perspective) than a false negative result. The models should be evaluated based on the following criteria: 

1) Must have a recall rate of at least 80%
2) Must have a false positive rate of 10% or less
3) Must minimize business costs After creating each binary classification model, the data scientist generates the corresponding confusion matrix. Which confusion matrix represents the model that satisfies the requirements?
A) TN = 91, FP = 9 FN = 22, TP = 78
 B) TN = 99, FP = 1 FN = 21, TP = 79
C) TN = 96, FP = 4 FN = 10, TP = 90
D) TN = 98, FP = 2 FN = 18, TP = 82
 
Answer 8): 
D
 

Notes/Hint 8)


The following calculations are required: TP = True Positive FP = False Positive FN = False Negative TN = True Negative FN = False Negative Recall = TP / (TP + FN) False Positive Rate (FPR) = FP / (FP + TN) Cost = 5 * FP + FN A B C D Recall 78 / (78 + 22) = 0.78 79 / (79 + 21) = 0.79 90 / (90 + 10) = 0.9 82 / (82 + 18) = 0.82 False Positive Rate 9 / (9 + 91) = 0.09 1 / (1 + 99) = 0.01 4 / (4 + 96) = 0.04 2 / (2 + 98) = 0.02 Costs 5 * 9 + 22 = 67 5 * 1 + 21 = 26 5 * 4 + 10 = 30 5 * 2 + 18 = 28 Options C and D have a recall greater than 80% and an FPR less than 10%, but D is the most cost effective. For supporting information, refer to this link.
Reference 8: Here

 
 

Question 9) A data scientist uses logistic regression to build a fraud detection model. While the model accuracy is 99%, 90% of the fraud cases are not detected by the model. What action will definitely help the model detect more than 10% of fraud cases? 

A) Using undersampling to balance the dataset
B) Decreasing the class probability threshold
C) Using regularization to reduce overfitting
D) Using oversampling to balance the dataset
 

Answer  9)

B

 

Notes 9)


Decreasing the class probability threshold makes the model more sensitive and, therefore, marks more cases as the positive class, which is fraud in this case. This will increase the likelihood of fraud detection. However, it comes at the price of lowering precision. This is covered in the Discussion section of the paper at this link
Reference 9: Here

 
 

Question 10) A company is interested in building a fraud detection model. Currently, the data scientist does not have a sufficient amount of information due to the low number of fraud cases. Which method is MOST likely to detect the GREATEST number of valid fraud cases?

A) Oversampling using bootstrapping
B) Undersampling
C) Oversampling using SMOTE
D) Class weight adjustment
 

Answer  10)

C

 
Notes 10)

With datasets that are not fully populated, the Synthetic Minority Over-sampling Technique (SMOTE) adds new information by adding synthetic data points to the minority class. This technique would be the most effective in this scenario. Refer to Section 4.2 at this link for supporting information.
Reference 10) : Here
 

Question 11) A machine learning engineer is preparing a data frame for a supervised learning task with the Amazon SageMaker Linear Learner algorithm. The ML engineer notices the target label classes are highly imbalanced and multiple feature columns contain missing values. The proportion of missing values across the entire data frame is less than 5%. What should the ML engineer do to minimize bias due to missing values? 

 
A) Replace each missing value by the mean or median across non-missing values in same row.
B) Delete observations that contain missing values because these represent less than 5% of the data.
C) Replace each missing value by the mean or median across non-missing values in the same column.
D) For each feature, approximate the missing values using supervised learning based on other features.
 

Answer  11)

D

 

Notes 11)

Use supervised learning to predict missing values based on the values of other features. Different supervised learning approaches might have different performances, but any properly implemented supervised learning approach should provide the same or better approximation than mean or median approximation, as proposed in responses A and C. Supervised learning applied to the imputation of missing values is an active field of research. Refer to this link for an example.
Reference 11): Here

 

Question 12) A company has collected customer comments on its products, rating them as safe or unsafe, using decision trees. The training dataset has the following features: id, date, full review, full review summary, and a binary safe/unsafe tag. During training, any data sample with missing features was dropped. In a few instances, the test set was found to be missing the full review text field. For this use case, which is the most effective course of action to address test data samples with missing features? 

A) Drop the test samples with missing full review text fields, and then run through the test set.
B) Copy the summary text fields and use them to fill in the missing full review text fields, and then run through the test set.
C) Use an algorithm that handles missing data better than decision trees.
D) Generate synthetic data to fill in the fields that are missing data, and then run through the test set.
 
Answer  12)
B

 

 

Notes 12) 

In this case, a full review summary usually contains the most descriptive phrases of the entire review and is a valid stand-in for the missing full review text field. For supporting information, refer to page 1627 at this link, and this link and this link.

Reference 12) Here

 

 

Question 13) An insurance company needs to automate claim compliance reviews because human reviews are expensive and error-prone. The company has a large set of claims and a compliance label for each. Each claim consists of a few sentences in English, many of which contain complex related information. Management would like to use Amazon SageMaker built-in algorithms to design a machine learning supervised model that can be trained to read each claim and predict if the claim is compliant or not. Which approach should be used to extract features from the claims to be used as inputs for the downstream supervised task? 

 
A) Derive a dictionary of tokens from claims in the entire dataset. Apply one-hot encoding to tokens found in each claim of the training set. Send the derived features space as inputs to an Amazon SageMaker builtin supervised learning algorithm.
 
B) Apply Amazon SageMaker BlazingText in Word2Vec mode to claims in the training set. Send the derived features space as inputs for the downstream supervised task.
 
C) Apply Amazon SageMaker BlazingText in classification mode to labeled claims in the training set to derive features for the claims that correspond to the compliant and non-compliant labels, respectively.
 
D) Apply Amazon SageMaker Object2Vec to claims in the training set. Send the derived features space as inputs for the downstream supervised task.
 

Answer  13)

D

 

Notes 13)

Amazon SageMaker Object2Vec generalizes the Word2Vec embedding technique for words to more complex objects, such as sentences and paragraphs. Since the supervised learning task is at the level of whole claims, for which there are labels, and no labels are available at the word level, Object2Vec needs be used instead of Word2Vec.

Reference 13)  Amazon SageMaker
Object2Vec 

Question 14) You have been tasked with capturing two different types of streaming events. The first event type includes mission-critical data that needs to immediately be processed before operations can continue. The second event type includes data of less importance, but operations can continue without immediately processing. What is the most appropriate solution to record these different types of events?

A) Capture both events with the PutRecords API call.
B) Capture both event types using the Kinesis Producer Library (KPL).
C) Capture the mission critical events with the PutRecords API call and the second event type with the Kinesis Producer Library (KPL).
D) Capture the mission critical events with the Kinesis Producer Library (KPL) and the second event type with the Putrecords API call.
 

Answer  14)

C

 

Notes 14)

The question is about sending data to Kinesis synchronously vs. asynchronously. PutRecords is a synchronous send function, so it must be used for the first event type (critical events). The Kinesis Producer Library (KPL) implements an asynchronous send function, so it can be used for the second event type. In this scenario, the reason to use the KPL over the PutRecords API call is because: KPL can incur an additional processing delay of up to RecordMaxBufferedTime within the library (user-configurable). Larger values of RecordMaxBufferedTime results in higher packing efficiencies and better performance. Applications that cannot tolerate this additional delay may need to use the AWS SDK directly. For more information about using the AWS SDK with Kinesis Data Streams, see Developing Producers Using the Amazon Kinesis Data Streams API with the AWS SDK for Java. For more information about RecordMaxBufferedTime and other user-configurable properties of the KPL, see Configuring the Kinesis Producer Library.

Reference 14: KCL vs PutRecords

 

Question 15) You are collecting clickstream data from an e-commerce website to make near-real time product suggestions for users actively using the site. Which combination of tools can be used to achieve the quickest recommendations and meets all of the requirements?

A) Use Kinesis Data Streams to ingest clickstream data, then use Kinesis Data Analytics to run real time SQL queries to gain actionable insights and trigger real-time recommendations with AWS Lambda functions based on conditions.
 
B) Use Kinesis Data Firehose to ingest click stream data, then use Kinesis Data Analytics to run real time SQL queries to gain actionable insights and trigger real-time recommendations with AWS Lambda functions based on conditions, then use Lambda to load these results into S3.
 
C) Use Kinesis Data Streams to ingest clickstream data, then use Lambda to process that data and write it to S3. Once the data is on S3, use Athena to query based on conditions that data and make real time recommendations to users.
 
D) Use the Kinesis Data Analytics to ingest the clickstream data directly and run real time SQL queries to gain actionable insights and trigger real-time recommendations with AWS Lambda functions based on conditions.
 

Answer  15)

A

 

Notes 15)

Kinesis Data Analytics gets its input streaming data from Kinesis Data Streams or Kinesis Data Firehose. You can use Kinesis Data Analytics to run real-time SQL queries on your data. Once certain conditions are met you can trigger Lambda functions to make real time product suggestions to users. It is not important that we store or persist the clickstream data.

Reference 15: Kinesis Data Analytics

Question 16) Which service built by AWS makes it easy to set up a retry mechanism, aggregate records to improve throughput, and automatically submits CloudWatch metrics?

A) Kinesis API (AWS SDK)
B) Kinesis Producer Library (KPL)
C) Kinesis Consumer Library
D) Kinesis Client Library (KCL)

Answer  16)

B

 

Notes 16)

Although the Kinesis API built into the AWS SDK can be used for all of this, the Kinesis Producer Library (KPL) makes it easy to integrate all of this into your applications.

Reference 16:  Kinesis Producer Library (KPL) 

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Question 17) You have been tasked with capturing data from an online gaming platform to run analytics on and process through a machine learning pipeline. The data that you are ingesting is players controller inputs every 1 second (up to 10 players in a game) that is in JSON format. The data needs to be ingested through Kinesis Data Streams and the JSON data blob is 100 KB in size. What is the minimum number of shards you can use to successfully ingest this data?

A) 10 shards
B) Greater than 500 shards, so you’ll need to request more shards from AWS
C) 1 shard
D) 100 shards

Answer  17)

C

 

Notes 17)

In this scenario, there will be a maximum of 10 records per second with a max payload size of 1000 KB (10 records x 100 KB = 1000KB) written to the shard. A single shard can ingest up to 1 MB of data per second, which is enough to ingest the 1000 KB from the streaming game play. Therefor 1 shard is enough to handle the streaming data.

Reference 17: shards

Question 18) Which services in the Kinesis family allows you to analyze streaming data, gain actionable insights, and respond to your business and customer needs in real time?

A) Kinesis Streams
B) Kinesis Firehose
C) Kinesis Video Streams
D) Kinesis Data Analytics

Answer  18)

D

 

Notes 18)

Kinesis Data Analytics allows you to run real-time SQL queries on your data to gain insights and respond to events in real time.

Reference 18: Kinesis Data Analytics

 

Question 19) You are a ML specialist needing to collect data from Twitter tweets. Your goal is to collect tweets that include only the name of your company and the tweet body, and store it off into a data store in AWS. What set of tools can you use to stream, transform, and load the data into AWS with the LEAST amount of effort?

A) Setup a Kinesis Data Firehose for data ingestion and immediately write that data to S3. Next, setup a Lambda function to trigger when data lands in S3 to transform it and finally write it to DynamoDB.
B) Setup A Kinesis Data Stream for data ingestion, setup EC2 instances as data consumers to poll and transform the data from the stream. Once the data is transformed, make an API call to write the data to DynamoDB.
C) Setup Kinesis Data Streams for data ingestion. Next, setup Kinesis Data Firehouse to load that data into RedShift. Next, setup a Lambda function to query data using RedShift spectrum and store the results onto DynamoDB.
D) Create a Kinesis Data Stream to ingest the data. Next, setup a Kinesis Data Firehose and use Lambda to transform the data from the Kinesis Data Stream, then use Lambda to write the data to DynamoDB. Finally, use S3 as the data destination for Kinesis Data Firehose.
 

Answer 19)

A

Notes 19)

All of these could be used to stream, transform, and load the data into an AWS data store. The setup that requires the LEAST amount of effort and moving parts involves setting up a Kinesis Data Firehose to stream the data into S3, have it transformed by Lambda with an S3 trigger, and then written to DynamoDB.

Reference 19: Kinesis Data Firehose to stream the data into S3

Question 20) Which service in the Kinesis family allows you to build custom applications that process or analyze streaming data for specialized needs?

A) Kinesis Firehose
B) Kinesis Streams
C) Kinesis Video Streams
D) Kinesis Data Analytics

Answer 20)

B

Notes 20)

Kinesis Streams allows you to stream data into AWS and build custom applications around that streaming data.

Reference 20: Kinesis Streams

Question21: Of the following, which is an example of machine learning? (Select TWO.)

A) Calculating the shortest route from current location to the destination

B) Optimizing product pricing based on real-time sales data

C) Sentiment analysis of text on product reviews

D) A loan approval system that classifies applicants entirely based on credit score

Answer21:

B and C

Notes 21: 

Optimizing product pricing based on real-time sales data and Sentiment analysis of text on product reviews.
 

Question22:Which of the following is an appropriate use case for unsupervised learning?

A) Partitioning an image of a street scene into multiple segments

B) Finding an optimal path out of a maze

C) Identifying clusters of housing sales based on related data points

D) Analyzing sentiment of social media posts

Answer22:

C

Notes 22: 

Identifying clusters of housing sales based on related data points

Question23

Answer23:

 

Notes 23: 

Question24: A Djamgatech retail company wants to deploy a machine learning model to predict the demand for a product using sales data from the past 5 years. What is the MOST efficient solution that the company should implement first?

A) Regression

B) Multi-class classification

C) Binary class classification

D) N/A

Answer24:

A

Notes 24: 

Question25: In which phase of the ML pipeline do you analyze the business requirements and re-frame that information into a machine learning context.

A) Problem formulation

B) Model training

C) Deployment

D)

Data preprocessing

Answer25:

A

Notes 25:

AWS machine Learning Specialty Exam Prep MLS-C01

iOs: https://apps.apple.com/ca/app/aws-machine-learning-prep-pro/id1611045854

Windows: https://www.microsoft.com/en-ca/p/aws-machine-learning-mls-c01-specialty-certification-exam-prep/9n8rl80hvm4t

Android/Amazon: https://www.amazon.com/gp/product/B09TZ4H8V6

AWS MLS-C01 Machine Learning Exam Prep

Quizzes, Practice Exams: Modeling, Data Engineering, Vision, Exploratory Data Analysis, ML Ops, Cheat Sheets, ML Jobs Interview Q&A

Use this App to learn about Machine Learning on AWS and prepare for the AWS Machine Learning Specialty Certification MLS-C01.

Earning AWS Certified Machine Learning Specialty validates expertise in building, training, tuning, and deploying machine learning (ML) models on AWS.

The App provides hundreds of quizzes and practice exam about:

– Machine Learning Operation on AWS

– Modelling

– Data Engineering

– Computer Vision,

– Exploratory Data Analysis,

– ML implementation & Operations

– Machine Learning Basics Questions and Answers

– Machine Learning Advanced Questions and Answers

– Scorecard

– Countdown timer

– Machine Learning Cheat Sheets

– Machine Learning Interview Questions and Answers

– Machine Learning Latest News

The App covers Machine Learning Basics and Advanced topics including: NLP, Computer Vision, Python, linear regression, logistic regression, Sampling, dataset, statistical interaction, selection bias, non-Gaussian distribution, bias-variance trade-off, Normal Distribution, correlation and covariance, Point Estimates and Confidence Interval, A/B Testing, p-value, statistical power of sensitivity, over-fitting and under-fitting, regularization, Law of Large Numbers, Confounding Variables, Survivorship Bias, univariate, bivariate and multivariate, Resampling, ROC curve, TF/IDF vectorization, Cluster Sampling, etc.

Domain 1: Data Engineering

Create data repositories for machine learning.

Identify data sources (e.g., content and location, primary sources such as user data)

Determine storage mediums (e.g., DB, Data Lake, S3, EFS, EBS)

Identify and implement a data ingestion solution.

Data job styles/types (batch load, streaming)

Data ingestion pipelines (Batch-based ML workloads and streaming-based ML workloads), etc.

Domain 2: Exploratory Data Analysis

Sanitize and prepare data for modeling.

Perform feature engineering.

Analyze and visualize data for machine learning.

Domain 3: Modeling

Frame business problems as machine learning problems.

Select the appropriate model(s) for a given machine learning problem.

Train machine learning models.

Perform hyperparameter optimization.

Evaluate machine learning models.

Domain 4: Machine Learning Implementation and Operations

Build machine learning solutions for performance, availability, scalability, resiliency, and fault tolerance.

Recommend and implement the appropriate machine learning services and features for a given problem.

Apply basic AWS security practices to machine learning solutions.

Deploy and operationalize machine learning solutions.

Machine Learning Services covered:

Amazon Comprehend

AWS Deep Learning AMIs (DLAMI)

AWS DeepLens

Amazon Forecast

Amazon Fraud Detector

Amazon Lex

Amazon Polly

Amazon Rekognition

Amazon SageMaker

Amazon Textract

Amazon Transcribe

Amazon Translate

Other Services and topics covered are:

Ingestion/Collection

Processing/ETL

Data analysis/visualization

Model training

Model deployment/inference

Operational

AWS ML application services

Language relevant to ML (for example, Python, Java, Scala, R, SQL)

Notebooks and integrated development environments (IDEs),

S3, SageMaker, Kinesis, Lake Formation, Athena, Kibana, Redshift, Textract, EMR, Glue, SageMaker, CSV, JSON, IMG, parquet or databases, Amazon Athena

Amazon EC2, Amazon Elastic Container Registry (Amazon ECR), Amazon Elastic Container Service, Amazon Elastic Kubernetes Service , Amazon Redshift

Sagemaker API Explained:

SageMaker API

AWS Certified Machine Learning Engineer Specialty Questions and Answers:

Question1: An advertising and analytics company uses machine learning to predict user response to online advertisements using a custom XGBoost model. The company wants to improve its ML pipeline by porting its training and inference code, written in R, to Amazon SageMaker, and do so with minimal changes to the existing code.

Answer1: Use the Build Your Own Container (BYOC) Amazon Sagemaker option.
Create a new docker container with the existing code. Register the container in Amazon Elastic Container registry. with the existing code. Register the container in Amazon Elastic Container Registry. Finally run the training and inference jobs using this container.

Question2: Which feature of Amazon SageMaker can you use for preprocessing the data?

 

Answer2: Amazon Sagemaker Notebook instances

Amazon SageMaker enables developers and data scientists to build, train, tune, and deploy machine learning (ML) models at scale. You can deploy trained ML models for real-time or batch predictions on unseen data, a process known as inference. However, in most cases, the raw input data must be preprocessed and can’t be used directly for making predictions. This is because most ML models expect the data in a predefined format, so the raw data needs to be first cleaned and formatted in order for the ML model to process the data.  You can use the Amazon SageMaker built-in Scikit-learn library for preprocessing input data and then use the Amazon SageMaker built-in Linear Learner algorithm for predictions.

Question3: What setting, when creating an Amazon SageMaker notebook instance, can you use to install libraries and import data?

Answer3: LifeCycle Configuration

Question4: How to Choose the right Sagemaker built-in algorithm?

How to chose the right built in algorithm in SageMaker?
How to chose the right built in algorithm in SageMaker?
Guide to choosing the right unsupervised learning algorithm
Guide to choosing the right unsupervised learning algorithm

 

Choosing the right  ML algorithm based on Data Type
Choosing the right ML algorithm based on Data Type

 

Choosing the right ML algo based on data type
Choosing the right ML algo based on data type

This is a general guide for choosing which algorithm to use depending on what business problem you have and what data you have. 

 

Top

Top 10 Google Professional Machine Learning Engineer Sample Questions

Question 1: You work for a textile manufacturer and have been asked to build a model to detect and classify fabric defects. You trained a machine learning model with high recall based on high resolution images taken at the end of the production line. You want quality control inspectors to gain trust in your model. Which technique should you use to understand the rationale of your classifier?

A. Use K-fold cross validation to understand how the model performs on different test datasets.

B. Use the Integrated Gradients method to efficiently compute feature attributions for each predicted image.

C. Use PCA (Principal Component Analysis) to reduce the original feature set to a smaller set of easily understood features.

D. Use k-means clustering to group similar images together, and calculate the Davies-Bouldin index to evaluate the separation between clusters.

Answer 1)

B

Notes 1)

B is correct because it identifies the pixel of the input image that leads to the classification of the image itself.

Question 2: You need to write a generic test to verify whether Dense Neural Network (DNN) models automatically released by your team have a sufficient number of parameters to learn the task for which they were built. What should you do?

A. Train the model for a few iterations, and check for NaN values.
B. Train the model for a few iterations, and verify that the loss is constant.
C. Train a simple linear model, and determine if the DNN model outperforms it.
D. Train the model with no regularization, and verify that the loss function is close to zero.
 

Answer 2)

D

Notes 2)

D is correct because the test can check that the model has enough parameters to memorize the task.

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Question 3: Your team is using a TensorFlow Inception-v3 CNN model pretrained on ImageNet for an image classification prediction challenge on 10,000 images. You will use AI Platform to perform the model training. What TensorFlow distribution strategy and AI Platform training job configuration should you use to train the model and optimize for wall-clock time?

 

A. Default Strategy; Custom tier with a single master node and four v100 GPUs.
B. One Device Strategy; Custom tier with a single master node and four v100 GPUs.
C. One Device Strategy; Custom tier with a single master node and eight v100 GPUs.
D. Central Storage Strategy; Custom tier with a single master node and four v100 GPUs.
 

Answer 3)

D

Notes 3)

D is correct because this is the only strategy that can perform distributed training; albeit there is only a single copy of the variables on the CPU host.

Question 4: You work on a team where the process for deploying a model into production starts with data scientists training different versions of models in a Kubeflow pipeline. The workflow then stores the new model artifact into the corresponding Cloud Storage bucket. You need to build the next steps of the pipeline after the submitted model is ready to be tested and deployed in production on AI Platform. How should you configure the architecture before deploying the model to production?

 
A. Deploy model in test environment -> Validate model -> Create a new AI Platform model version
 
B. Validate model -> Deploy model in test environment -> Create a new AI Platform model version
 
C. Create a new AI Platform model version -> Validate model -> Deploy model in test environment
D. Create a new AI Platform model version – > Deploy model in test environment -> Validate model
 
Answer 4)
A
 
Notes 4)
A is correct because the model can be validated after it is deployed to the test environment, and the release version is established before the model is deployed in production.
 
Question 5: You work for a maintenance company and have built and trained a deep learning model that identifies defects based on thermal images of underground electric cables. Your dataset contains 10,000 images, 100 of which contain visible defects. How should you evaluate the performance of the model on a test dataset?
 
A. Calculate the Area Under the Curve (AUC) value.
 
B. Calculate the number of true positive results predicted by the model.
C. Calculate the fraction of images predicted by the model to have a visible defect.
D. Calculate the Cosine Similarity to compare the model’s performance on the test dataset to the model’s performance on the training dataset.
 
Answer 5)
A
 
Notes 5)
A is correct because it is scale-invariant. AUC measures how well predictions are ranked, rather than their absolute values. AUC is also classification-threshold invariant. It measures the quality of the model’s predictions irrespective of what classification threshold is chosen.
 
Question 6: You work for a manufacturing company that owns a high-value machine which has several machine settings and multiple sensors. A history of the machine’s hourly sensor readings and known failure event data are stored in BigQuery. You need to predict if the machine will fail within the next 3 days in order to schedule maintenance before the machine fails. Which data preparation and model training steps should you take?

 

A. Data preparation: Daily max value feature engineering with DataPrep; Model training: AutoML classification with BQML
 
B. Data preparation: Daily min value feature engineering with DataPrep; Model training: Logistic regression with BQML and AUTO_CLASS_WEIGHTS set to True
C. Data preparation: Rolling average feature engineering with DataPrep; Model training: Logistic regression with BQML and AUTO_CLASS_WEIGHTS set to False
D. Data preparation: Rolling average feature engineering with DataPrep; Model training: Logistic regression with BQML and AUTO_CLASS_WEIGHTS set to True
Answer 6)
D
 
Notes 6)
D is correct because it uses the rolling average of the sensor data and balances the weights using the BQML auto class weight balance parameter.
 
 
Question 7: You are an ML engineer at a media company. You need to build an ML model to analyze video content frame-by-frame, identify objects, and alert users if there is inappropriate content. Which Google Cloud products should you use to build this project?

 

A. Pub/Sub, Cloud Function, Cloud Vision API
 
B. Pub/Sub, Cloud IoT, Dataflow, Cloud Vision API, Cloud Logging
C. Pub/Sub, Cloud Function, Video Intelligence API, Cloud Logging
D. Pub/Sub, Cloud Function, AutoML Video Intelligence, Cloud Logging
 
Answer 7)
C
 
Notes 7)
C is correct as Video Intelligence API can find inappropriate components and other components satisfy the requirements of real-time processing and notification.
 
Question 8: You work for a large retailer. You want to use ML to forecast future sales leveraging 10 years of historical sales data. The historical data is stored in Cloud Storage in Avro format. You want to rapidly experiment with all the available data. How should you build and train your model for the sales forecast?
 
A. Load data into BigQuery and use the ARIMA model type on BigQuery ML.
B. Convert the data into CSV format and create a regression model on AutoML Tables.
C. Convert the data into TFRecords and create an RNN model on TensorFlow on AI Platform Notebooks.
D. Convert and refactor the data into CSV format and use the built-in XGBoost algorithm on AI Platform Training.
 
Answer 8)
A
 
Notes 8)
A is correct because BigQuery ML is designed for fast and rapid experimentation and it is possible to use federated queries to read data directly from Cloud Storage. Moreover, ARIMA is considered one of the best in class for time series forecasting.
 
Question 9) You need to build an object detection model for a small startup company to identify if and where the company’s logo appears in an image. You were given a large repository of images, some with logos and some without. These images are not yet labelled. You need to label these pictures, and then train and deploy the model. What should you do?

 

A. Use Google Cloud’s Data Labelling Service to label your data. Use AutoML Object Detection to train and deploy the model.
B. Use Vision API to detect and identify logos in pictures and use it as a label. Use AI Platform to build and train a convolutional neural network.
 
C. Create two folders: one where the logo appears and one where it doesn’t. Manually place images in each folder. Use AI Platform to build and train a convolutional neural network.
D. Create two folders: one where the logo appears and one where it doesn’t. Manually place images in each folder. Use AI Platform to build and train a real time object detection model.
 
Answer 9)
A
 
Notes 9)
A is correct as this will allow you to easily create a request for a labelling task and deploy a high-performance model.
 

Question 10) You work for a large financial institution that is planning to use Dialogflow to create a chatbot for the company’s mobile app. You have reviewed old chat logs and tagged each conversation for intent based on each customer’s stated intention for contacting customer service. About 70% of customer inquiries are simple requests that are solved within 10 intents. The remaining 30% of inquiries require much longer and more complicated requests. Which intents should you automate first?

A. Automate a blend of the shortest and longest intents to be representative of all intents.
B. Automate the more complicated requests first because those require more of the agents’ time.
C. Automate the 10 intents that cover 70% of the requests so that live agents can handle the more complicated requests.
 
D. Automate intents in places where common words such as “payment” only appear once to avoid confusing the software.
Answer 10)
C
 
Notes 10)

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Machine Learning Q&A Part I:

Google.

Azure and AWS are second class citizens in this area.

Sure, AWS has 70% of the market.

Sure, Azure is the easiest turn key and super user friendly.

But, the king of machine learning in the cloud is GCP.

GCP = Google Cloud Platform

Google has the largest data science team in the world, not mention they have Hinton.

Let’s forgot for a minute they created TensorFlow and give it away.

Let’s just talk about building a real world model with data that doesn’t fit into a excel spreadsheet.

The vast majority of applied machine learning is supervised and that means we need data.

Not just normal data, we need very clean highly structured data.

Where’s the easiest place in the world to upload and model a Petabyte of structured dataBigQuery of course.

Why BigQuery? I don’t have to do anything but upload my data. No spinning up RedShit clusters or whatever I have to do in Azure, just upload and massage data with my familiar SQL. If I do have to wrangle my data it won’t take my six months to update 5 rows here, minutes usually.

Then, you’ll need a front end. Cloud datalab is a Jupyter notebook, which is good because I don’t want nor do I need anything else.

Then, with a single line of code I connect by datalab (Jupyter) notebook to my data in BigQuery and build away.

I’ve worked in all three and the only thing I care about is getting to my job the fastest and right now that means I build my models in GCP.

If you’re new to machine learning don’t start in GCP or any cloud vendor for that matter. Start learning Python from the comfort of your laptop.

The course below is free to the first 20.

The Complete Python Course for Machine Learning Engineers

Here, I want to share the best research paper on Machine Learning classification methods, titled ‘Do we Need Hundreds of Classifiers to Solve Real World Classification Problems?’, published in the ‘Journal of Machine Learning Research’.

This paper nicely explained 179 classification techniques and applied them on 121 data sets thus sharing small summary of the paper:

Do we Need Hundreds of Classifiers to Solve Real World Classification Problems?

 
 
 

The paper evaluated 179 classifiers arising from 17 ML families (discriminant analysis, Bayesian, neural networks, support vector machines, decision trees, rule-based classifiers, boosting, bagging, stacking, random forests and other ensembles, generalized linear models, nearest neighbours, partial least squares and principal component regression, logistic and multinomial regression, multiple adaptive regression splines and other methods), implemented in Weka, R ( with and without the caret package), C and Matlab, including all the relevant classifiers available today.

Experiments used total 121 data sets , which represent the whole UCI data base (excluding the large-scale problems) and other own real problems, in order to achieve significant conclusions about the classifier behaviour, not dependent on the data set collection.

The whole data set and partitions are available from: http://persoal.citius.usc.es/manuel.fernandez.delgado/papers/jmlr/data.tar.gz

The classifiers most likely to be the bests are the random forest (RF) versions, the best of which (implemented in R and accessed via caret) achieves 94.1% of the maximum accuracy overcoming 90% in the 84.3% of the data sets. However, the difference is not statistically significant with the second best, the SVM with Gaussian kernel implemented in C using LibSVM, which achieves 92.3% of the maximum accuracy. A few models are clearly better than the remaining ones: random forest, SVM with Gaussian and polynomial kernels, extreme learning machine with Gaussian kernel, C5.0 and avNNet (a committee of multi-layer perceptrons implemented in R with the caret package).

The random forest is clearly the best family of classifiers (3 out of 5 bests classifiers are RF), followed by SVM (4 classifiers in the top-10), neural networks and boosting ensembles (5 and 3 members in the top-20, respectively).

You can see the table with the complete results: http://persoal.citius.usc.es/manuel.fernandez.delgado/papers/jmlr/results.txt

I hope it will be helpful for Statistic and Machine Leaning aspirants!

Thank you!

 
 
 

At a high level, these skills are a combination of software and data engineering.

The persons that are more appropriate to do this job are a data engineer and/or a machine learning engineer.

That being said, if you work at a startup or happen to be in a small company and need to put the models into production yourself, here are the top skills you need to get:

  • Well structured code: it doesn’t need to be perfect but at least can be understood and updated by other team members. Avoid spaghetti code[1] as the plague.
  • Add logs: if you are a Python user, the logging[2] module is your friend. Avoid print statements at any cost.
  • Model versioning: add a hash key to your different models. You will thank me later.
  • Metadata everywhere: save as much data about your models and ML experiments as you can (running time, hyperparameters, used features, CV scores, and so on). You will thank me later, again.
  • Monitor performances: execution time and statistical scores of your models.
  • Data and models management: store the necessary data and models somewhere that is available to everyone (S3[3] for example). Avoid uploading these to your VCS[4] system. Don’t share them using Slack or Drive. I won’t judge you though, I do it sometimes (read often). Read more here …..

Some of the mistakes that might involve during building a machine learning model (I can think of) are listed here:

  1. Not understanding the structure of the dataset
  2. Not giving proper care during features selection
  3. Leaving out categorical features and considering just numerical variables
  4. Falling into dummy variable trap
  5. Selection of inefficient machine learning algorithm
  6. Not trying out various ML algorithms for building the model based on structure of data.
  7. Improper tuning of model parameters
  8. Most importantly: Building an idiotstic imperfect model i.e. suppose we have a classification problem with 99% chances of falling into class1 and remaining to class2. The built model may develop a mapping function which all the time for all data inputs, may predict the result to be class1. Well, one might say his/her model has 99% accuracy. But in reality the 1% class2 case hasn’t been included in the model. So this must be taken into consideration.
  9. Read more here…

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Basically, data mining is a key aspect of data analytics. Some even consider the former as essential to execute before the latter. While data analytics is the complete package and involves most components needed to examine a data set and extract valuable information, data mining focuses specifically on identifying hidden patterns.

That’s just the surface-level comparison though. The image above gives an overview of how the two differ.

One such difference is the presence of a hypothesis. Data analytics usually requires coming up with one, as it aims to find specific answers. Data mining, on the other hand, generally doesn’t need one to test or prove. The expected output are patterns or trends, which doesn’t require coming up with a statement or fact to test.

However, that doesn’t mean you mine data blindly. You still have a goal, whether it’s to come up with a recommender system or identify predictors of a certain dimension. Ultimately though, you strive to come up with data patterns or trends. For data analysis on the other hand, you’re expected to come up with valuable and actionable insights, usually in relation to a predetermined hypothesis. Read more here ….

The data science life cycle is not something well-defined like the software development life-cycle, and there is no ‘one-size-fits-all’ solution for data science projects. Every step in the life-cycle of a data science project depends on various data scientist skills and data science tools. The typical life-cycle of a data science project involves jumping back and forth among various interdependent science tasks using a variety of tools, techniques, programming, etc.

Thus, the data science life-cycle can include the following steps:

  1. Business requirement understanding.
  2. Data collection.
  3. Data cleaning.
  4. Data analysis.
  5. Modeling.
  6. Performance evaluation.
  7. Communicating with stakeholders.
  8. Deployment.
  9. Real-world testing.
  10. Business buy-in.
  11. Support and maintenance.

Looks neat, but here is the scheme to visualize how it is happening in reality:

Agile development processes, especially continuous delivery lends itself well to the data science project life-cycle. The early comparison helps the data science team to change approaches, refine hypotheses and even discard the project if the business case is nonviable or the benefits from the predictive models are not worth the effort to build it.

Read more here….

 

Top

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Machine Learning Q&A -Part II:

 
 
 

At a high level, these skills are a combination of software and data engineering.

The persons that are more appropriate to do this job are a data engineer and/or a machine learning engineer.

That being said, if you work at a startup or happen to be in a small company and need to put the models into production yourself, here are the top skills you need to get:

  • Well structured code: it doesn’t need to be perfect but at least can be understood and updated by other team members. Avoid spaghetti code[1] as the plague.
  • Add logs: if you are a Python user, the logging[2] module is your friend. Avoid print statements at any cost.
  • Model versioning: add a hash key to your different models. You will thank me later.
  • Metadata everywhere: save as much data about your models and ML experiments as you can (running time, hyperparameters, used features, CV scores, and so on). You will thank me later, again.
  • Monitor performances: execution time and statistical scores of your models.
  • Data and models management: store the necessary data and models somewhere that is available to everyone (S3[3] for example). Avoid uploading these to your VCS[4] system. Don’t share them using Slack or Drive. I won’t judge you though, I do it sometimes (read often). Read more here …..

Some of the mistakes that might involve during building a machine learning model (I can think of) are listed here:

  1. Not understanding the structure of the dataset
  2. Not giving proper care during features selection
  3. Leaving out categorical features and considering just numerical variables
  4. Falling into dummy variable trap
  5. Selection of inefficient machine learning algorithm
  6. Not trying out various ML algorithms for building the model based on structure of data.
  7. Improper tuning of model parameters
  8. Most importantly: Building an idiotstic imperfect model i.e. suppose we have a classification problem with 99% chances of falling into class1 and remaining to class2. The built model may develop a mapping function which all the time for all data inputs, may predict the result to be class1. Well, one might say his/her model has 99% accuracy. But in reality the 1% class2 case hasn’t been included in the model. So this must be taken into consideration.
  9. Read more here…

Basically, data mining is a key aspect of data analytics. Some even consider the former as essential to execute before the latter. While data analytics is the complete package and involves most components needed to examine a data set and extract valuable information, data mining focuses specifically on identifying hidden patterns.

That’s just the surface-level comparison though. The image above gives an overview of how the two differ.

One such difference is the presence of a hypothesis. Data analytics usually requires coming up with one, as it aims to find specific answers. Data mining, on the other hand, generally doesn’t need one to test or prove. The expected output are patterns or trends, which doesn’t require coming up with a statement or fact to test.

However, that doesn’t mean you mine data blindly. You still have a goal, whether it’s to come up with a recommender system or identify predictors of a certain dimension. Ultimately though, you strive to come up with data patterns or trends. For data analysis on the other hand, you’re expected to come up with valuable and actionable insights, usually in relation to a predetermined hypothesis. Read more here ….

The data science life cycle is not something well-defined like the software development life-cycle, and there is no ‘one-size-fits-all’ solution for data science projects. Every step in the life-cycle of a data science project depends on various data scientist skills and data science tools. The typical life-cycle of a data science project involves jumping back and forth among various interdependent science tasks using a variety of tools, techniques, programming, etc.

Thus, the data science life-cycle can include the following steps:

  1. Business requirement understanding.
  2. Data collection.
  3. Data cleaning.
  4. Data analysis.
  5. Modeling.
  6. Performance evaluation.
  7. Communicating with stakeholders.
  8. Deployment.
  9. Real-world testing.
  10. Business buy-in.
  11. Support and maintenance.

Looks neat, but here is the scheme to visualize how it is happening in reality:

Agile development processes, especially continuous delivery lends itself well to the data science project life-cycle. The early comparison helps the data science team to change approaches, refine hypotheses and even discard the project if the business case is nonviable or the benefits from the predictive models are not worth the effort to build it.

Read more here….

 

Top

 

AWS machine Learning Specialty Exam Prep MLS-C01

iOs: https://apps.apple.com/ca/app/aws-machine-learning-prep-pro/id1611045854

Windows: https://www.microsoft.com/en-ca/p/aws-machine-learning-mls-c01-specialty-certification-exam-prep/9n8rl80hvm4t

Android/Amazon: https://www.amazon.com/gp/product/B09TZ4H8V6

AWS MLS-C01 Machine Learning Exam Prep

Quizzes, Practice Exams: Modeling, Data Engineering, Vision, Exploratory Data Analysis, ML Ops, Cheat Sheets, ML Jobs Interview Q&A

Use this App to learn about Machine Learning on AWS and prepare for the AWS Machine Learning Specialty Certification MLS-C01.

Earning AWS Certified Machine Learning Specialty validates expertise in building, training, tuning, and deploying machine learning (ML) models on AWS.

The App provides hundreds of quizzes and practice exam about:

– Machine Learning Operation on AWS

– Modelling

– Data Engineering

– Computer Vision,

– Exploratory Data Analysis,

– ML implementation & Operations

– Machine Learning Basics Questions and Answers

– Machine Learning Advanced Questions and Answers

– Scorecard

– Countdown timer

– Machine Learning Cheat Sheets

– Machine Learning Interview Questions and Answers

– Machine Learning Latest News

The App covers Machine Learning Basics and Advanced topics including: NLP, Computer Vision, Python, linear regression, logistic regression, Sampling, dataset, statistical interaction, selection bias, non-Gaussian distribution, bias-variance trade-off, Normal Distribution, correlation and covariance, Point Estimates and Confidence Interval, A/B Testing, p-value, statistical power of sensitivity, over-fitting and under-fitting, regularization, Law of Large Numbers, Confounding Variables, Survivorship Bias, univariate, bivariate and multivariate, Resampling, ROC curve, TF/IDF vectorization, Cluster Sampling, etc.

Domain 1: Data Engineering

Create data repositories for machine learning.

Identify data sources (e.g., content and location, primary sources such as user data)

Determine storage mediums (e.g., DB, Data Lake, S3, EFS, EBS)

Identify and implement a data ingestion solution.

Data job styles/types (batch load, streaming)

Data ingestion pipelines (Batch-based ML workloads and streaming-based ML workloads), etc.

Domain 2: Exploratory Data Analysis

Sanitize and prepare data for modeling.

Perform feature engineering.

Analyze and visualize data for machine learning.

Domain 3: Modeling

Frame business problems as machine learning problems.

Select the appropriate model(s) for a given machine learning problem.

Train machine learning models.

Perform hyperparameter optimization.

Evaluate machine learning models.

Domain 4: Machine Learning Implementation and Operations

Build machine learning solutions for performance, availability, scalability, resiliency, and fault tolerance.

Recommend and implement the appropriate machine learning services and features for a given problem.

Apply basic AWS security practices to machine learning solutions.

Deploy and operationalize machine learning solutions.

Machine Learning Services covered:

Amazon Comprehend

AWS Deep Learning AMIs (DLAMI)

AWS DeepLens

Amazon Forecast

Amazon Fraud Detector

Amazon Lex

Amazon Polly

Amazon Rekognition

Amazon SageMaker

Amazon Textract

Amazon Transcribe

Amazon Translate

Other Services and topics covered are:

Ingestion/Collection

Processing/ETL

Data analysis/visualization

Model training

Model deployment/inference

Operational

AWS ML application services

Language relevant to ML (for example, Python, Java, Scala, R, SQL)

Notebooks and integrated development environments (IDEs),

S3, SageMaker, Kinesis, Lake Formation, Athena, Kibana, Redshift, Textract, EMR, Glue, SageMaker, CSV, JSON, IMG, parquet or databases, Amazon Athena

Amazon EC2, Amazon Elastic Container Registry (Amazon ECR), Amazon Elastic Container Service, Amazon Elastic Kubernetes Service , Amazon Redshift

Sagemaker API Explained:

SageMaker API

AWS Certified Machine Learning Engineer Specialty Questions and Answers:

Question1: An advertising and analytics company uses machine learning to predict user response to online advertisements using a custom XGBoost model. The company wants to improve its ML pipeline by porting its training and inference code, written in R, to Amazon SageMaker, and do so with minimal changes to the existing code.

Answer1: Use the Build Your Own Container (BYOC) Amazon Sagemaker option.
Create a new docker container with the existing code. Register the container in Amazon Elastic Container registry. with the existing code. Register the container in Amazon Elastic Container Registry. Finally run the training and inference jobs using this container.

Question2: Which feature of Amazon SageMaker can you use for preprocessing the data?

 

Answer2: Amazon Sagemaker Notebook instances

Amazon SageMaker enables developers and data scientists to build, train, tune, and deploy machine learning (ML) models at scale. You can deploy trained ML models for real-time or batch predictions on unseen data, a process known as inference. However, in most cases, the raw input data must be preprocessed and can’t be used directly for making predictions. This is because most ML models expect the data in a predefined format, so the raw data needs to be first cleaned and formatted in order for the ML model to process the data.  You can use the Amazon SageMaker built-in Scikit-learn library for preprocessing input data and then use the Amazon SageMaker built-in Linear Learner algorithm for predictions.

Question3: What setting, when creating an Amazon SageMaker notebook instance, can you use to install libraries and import data?

Answer3: LifeCycle Configuration

Question4: How to Choose the right Sagemaker built-in algorithm?

How to chose the right built in algorithm in SageMaker?
How to chose the right built in algorithm in SageMaker?
Guide to choosing the right unsupervised learning algorithm
Guide to choosing the right unsupervised learning algorithm

 

Choosing the right  ML algorithm based on Data Type
Choosing the right ML algorithm based on Data Type

 

Choosing the right ML algo based on data type
Choosing the right ML algo based on data type

This is a general guide for choosing which algorithm to use depending on what business problem you have and what data you have. 

 

Top

Top 10 Google Professional Machine Learning Engineer Sample Questions

Question 1: You work for a textile manufacturer and have been asked to build a model to detect and classify fabric defects. You trained a machine learning model with high recall based on high resolution images taken at the end of the production line. You want quality control inspectors to gain trust in your model. Which technique should you use to understand the rationale of your classifier?

A. Use K-fold cross validation to understand how the model performs on different test datasets.

B. Use the Integrated Gradients method to efficiently compute feature attributions for each predicted image.

C. Use PCA (Principal Component Analysis) to reduce the original feature set to a smaller set of easily understood features.

D. Use k-means clustering to group similar images together, and calculate the Davies-Bouldin index to evaluate the separation between clusters.

Answer 1)

B

Notes 1)

B is correct because it identifies the pixel of the input image that leads to the classification of the image itself.

Question 2: You need to write a generic test to verify whether Dense Neural Network (DNN) models automatically released by your team have a sufficient number of parameters to learn the task for which they were built. What should you do?

A. Train the model for a few iterations, and check for NaN values.
B. Train the model for a few iterations, and verify that the loss is constant.
C. Train a simple linear model, and determine if the DNN model outperforms it.
D. Train the model with no regularization, and verify that the loss function is close to zero.
 

Answer 2)

D

Notes 2)

D is correct because the test can check that the model has enough parameters to memorize the task.

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Question 3: Your team is using a TensorFlow Inception-v3 CNN model pretrained on ImageNet for an image classification prediction challenge on 10,000 images. You will use AI Platform to perform the model training. What TensorFlow distribution strategy and AI Platform training job configuration should you use to train the model and optimize for wall-clock time?

 

A. Default Strategy; Custom tier with a single master node and four v100 GPUs.
B. One Device Strategy; Custom tier with a single master node and four v100 GPUs.
C. One Device Strategy; Custom tier with a single master node and eight v100 GPUs.
D. Central Storage Strategy; Custom tier with a single master node and four v100 GPUs.
 

Answer 3)

D

Notes 3)

D is correct because this is the only strategy that can perform distributed training; albeit there is only a single copy of the variables on the CPU host.

Question 4: You work on a team where the process for deploying a model into production starts with data scientists training different versions of models in a Kubeflow pipeline. The workflow then stores the new model artifact into the corresponding Cloud Storage bucket. You need to build the next steps of the pipeline after the submitted model is ready to be tested and deployed in production on AI Platform. How should you configure the architecture before deploying the model to production?

 
A. Deploy model in test environment -> Validate model -> Create a new AI Platform model version
 
B. Validate model -> Deploy model in test environment -> Create a new AI Platform model version
 
C. Create a new AI Platform model version -> Validate model -> Deploy model in test environment
D. Create a new AI Platform model version – > Deploy model in test environment -> Validate model
 
Answer 4)
A
 
Notes 4)
A is correct because the model can be validated after it is deployed to the test environment, and the release version is established before the model is deployed in production.
 
Question 5: You work for a maintenance company and have built and trained a deep learning model that identifies defects based on thermal images of underground electric cables. Your dataset contains 10,000 images, 100 of which contain visible defects. How should you evaluate the performance of the model on a test dataset?
 
A. Calculate the Area Under the Curve (AUC) value.
 
B. Calculate the number of true positive results predicted by the model.
C. Calculate the fraction of images predicted by the model to have a visible defect.
D. Calculate the Cosine Similarity to compare the model’s performance on the test dataset to the model’s performance on the training dataset.
 
Answer 5)
A
 
Notes 5)
A is correct because it is scale-invariant. AUC measures how well predictions are ranked, rather than their absolute values. AUC is also classification-threshold invariant. It measures the quality of the model’s predictions irrespective of what classification threshold is chosen.
 
Question 6: You work for a manufacturing company that owns a high-value machine which has several machine settings and multiple sensors. A history of the machine’s hourly sensor readings and known failure event data are stored in BigQuery. You need to predict if the machine will fail within the next 3 days in order to schedule maintenance before the machine fails. Which data preparation and model training steps should you take?

 

A. Data preparation: Daily max value feature engineering with DataPrep; Model training: AutoML classification with BQML
 
B. Data preparation: Daily min value feature engineering with DataPrep; Model training: Logistic regression with BQML and AUTO_CLASS_WEIGHTS set to True
C. Data preparation: Rolling average feature engineering with DataPrep; Model training: Logistic regression with BQML and AUTO_CLASS_WEIGHTS set to False
D. Data preparation: Rolling average feature engineering with DataPrep; Model training: Logistic regression with BQML and AUTO_CLASS_WEIGHTS set to True
Answer 6)
D
 
Notes 6)
D is correct because it uses the rolling average of the sensor data and balances the weights using the BQML auto class weight balance parameter.
 
 
Question 7: You are an ML engineer at a media company. You need to build an ML model to analyze video content frame-by-frame, identify objects, and alert users if there is inappropriate content. Which Google Cloud products should you use to build this project?

 

A. Pub/Sub, Cloud Function, Cloud Vision API
 
B. Pub/Sub, Cloud IoT, Dataflow, Cloud Vision API, Cloud Logging
C. Pub/Sub, Cloud Function, Video Intelligence API, Cloud Logging
D. Pub/Sub, Cloud Function, AutoML Video Intelligence, Cloud Logging
 
Answer 7)
C
 
Notes 7)
C is correct as Video Intelligence API can find inappropriate components and other components satisfy the requirements of real-time processing and notification.
 
Question 8: You work for a large retailer. You want to use ML to forecast future sales leveraging 10 years of historical sales data. The historical data is stored in Cloud Storage in Avro format. You want to rapidly experiment with all the available data. How should you build and train your model for the sales forecast?
 
A. Load data into BigQuery and use the ARIMA model type on BigQuery ML.
B. Convert the data into CSV format and create a regression model on AutoML Tables.
C. Convert the data into TFRecords and create an RNN model on TensorFlow on AI Platform Notebooks.
D. Convert and refactor the data into CSV format and use the built-in XGBoost algorithm on AI Platform Training.
 
Answer 8)
A
 
Notes 8)
A is correct because BigQuery ML is designed for fast and rapid experimentation and it is possible to use federated queries to read data directly from Cloud Storage. Moreover, ARIMA is considered one of the best in class for time series forecasting.
 
Question 9) You need to build an object detection model for a small startup company to identify if and where the company’s logo appears in an image. You were given a large repository of images, some with logos and some without. These images are not yet labelled. You need to label these pictures, and then train and deploy the model. What should you do?

 

A. Use Google Cloud’s Data Labelling Service to label your data. Use AutoML Object Detection to train and deploy the model.
B. Use Vision API to detect and identify logos in pictures and use it as a label. Use AI Platform to build and train a convolutional neural network.
 
C. Create two folders: one where the logo appears and one where it doesn’t. Manually place images in each folder. Use AI Platform to build and train a convolutional neural network.
D. Create two folders: one where the logo appears and one where it doesn’t. Manually place images in each folder. Use AI Platform to build and train a real time object detection model.
 
Answer 9)
A
 
Notes 9)
A is correct as this will allow you to easily create a request for a labelling task and deploy a high-performance model.
 

Question 10) You work for a large financial institution that is planning to use Dialogflow to create a chatbot for the company’s mobile app. You have reviewed old chat logs and tagged each conversation for intent based on each customer’s stated intention for contacting customer service. About 70% of customer inquiries are simple requests that are solved within 10 intents. The remaining 30% of inquiries require much longer and more complicated requests. Which intents should you automate first?

A. Automate a blend of the shortest and longest intents to be representative of all intents.
B. Automate the more complicated requests first because those require more of the agents’ time.
C. Automate the 10 intents that cover 70% of the requests so that live agents can handle the more complicated requests.
 
D. Automate intents in places where common words such as “payment” only appear once to avoid confusing the software.
Answer 10)
C
 
Notes 10)

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Machine Learning Q&A Part I:

Google.

Azure and AWS are second class citizens in this area.

Sure, AWS has 70% of the market.

Sure, Azure is the easiest turn key and super user friendly.

But, the king of machine learning in the cloud is GCP.

GCP = Google Cloud Platform

Google has the largest data science team in the world, not mention they have Hinton.

Let’s forgot for a minute they created TensorFlow and give it away.

Let’s just talk about building a real world model with data that doesn’t fit into a excel spreadsheet.

The vast majority of applied machine learning is supervised and that means we need data.

Not just normal data, we need very clean highly structured data.

Where’s the easiest place in the world to upload and model a Petabyte of structured dataBigQuery of course.

Why BigQuery? I don’t have to do anything but upload my data. No spinning up RedShit clusters or whatever I have to do in Azure, just upload and massage data with my familiar SQL. If I do have to wrangle my data it won’t take my six months to update 5 rows here, minutes usually.

Then, you’ll need a front end. Cloud datalab is a Jupyter notebook, which is good because I don’t want nor do I need anything else.

Then, with a single line of code I connect by datalab (Jupyter) notebook to my data in BigQuery and build away.

I’ve worked in all three and the only thing I care about is getting to my job the fastest and right now that means I build my models in GCP.

If you’re new to machine learning don’t start in GCP or any cloud vendor for that matter. Start learning Python from the comfort of your laptop.

The course below is free to the first 20.

The Complete Python Course for Machine Learning Engineers

Here, I want to share the best research paper on Machine Learning classification methods, titled ‘Do we Need Hundreds of Classifiers to Solve Real World Classification Problems?’, published in the ‘Journal of Machine Learning Research’.

This paper nicely explained 179 classification techniques and applied them on 121 data sets thus sharing small summary of the paper:

Do we Need Hundreds of Classifiers to Solve Real World Classification Problems?

 
 
 

The paper evaluated 179 classifiers arising from 17 ML families (discriminant analysis, Bayesian, neural networks, support vector machines, decision trees, rule-based classifiers, boosting, bagging, stacking, random forests and other ensembles, generalized linear models, nearest neighbours, partial least squares and principal component regression, logistic and multinomial regression, multiple adaptive regression splines and other methods), implemented in Weka, R ( with and without the caret package), C and Matlab, including all the relevant classifiers available today.

Experiments used total 121 data sets , which represent the whole UCI data base (excluding the large-scale problems) and other own real problems, in order to achieve significant conclusions about the classifier behaviour, not dependent on the data set collection.

The whole data set and partitions are available from: http://persoal.citius.usc.es/manuel.fernandez.delgado/papers/jmlr/data.tar.gz

The classifiers most likely to be the bests are the random forest (RF) versions, the best of which (implemented in R and accessed via caret) achieves 94.1% of the maximum accuracy overcoming 90% in the 84.3% of the data sets. However, the difference is not statistically significant with the second best, the SVM with Gaussian kernel implemented in C using LibSVM, which achieves 92.3% of the maximum accuracy. A few models are clearly better than the remaining ones: random forest, SVM with Gaussian and polynomial kernels, extreme learning machine with Gaussian kernel, C5.0 and avNNet (a committee of multi-layer perceptrons implemented in R with the caret package).

The random forest is clearly the best family of classifiers (3 out of 5 bests classifiers are RF), followed by SVM (4 classifiers in the top-10), neural networks and boosting ensembles (5 and 3 members in the top-20, respectively).

You can see the table with the complete results: http://persoal.citius.usc.es/manuel.fernandez.delgado/papers/jmlr/results.txt

I hope it will be helpful for Statistic and Machine Leaning aspirants!

Thank you!

 
 
 

At a high level, these skills are a combination of software and data engineering.

The persons that are more appropriate to do this job are a data engineer and/or a machine learning engineer.

That being said, if you work at a startup or happen to be in a small company and need to put the models into production yourself, here are the top skills you need to get:

  • Well structured code: it doesn’t need to be perfect but at least can be understood and updated by other team members. Avoid spaghetti code[1] as the plague.
  • Add logs: if you are a Python user, the logging[2] module is your friend. Avoid print statements at any cost.
  • Model versioning: add a hash key to your different models. You will thank me later.
  • Metadata everywhere: save as much data about your models and ML experiments as you can (running time, hyperparameters, used features, CV scores, and so on). You will thank me later, again.
  • Monitor performances: execution time and statistical scores of your models.
  • Data and models management: store the necessary data and models somewhere that is available to everyone (S3[3] for example). Avoid uploading these to your VCS[4] system. Don’t share them using Slack or Drive. I won’t judge you though, I do it sometimes (read often). Read more here …..

Some of the mistakes that might involve during building a machine learning model (I can think of) are listed here:

  1. Not understanding the structure of the dataset
  2. Not giving proper care during features selection
  3. Leaving out categorical features and considering just numerical variables
  4. Falling into dummy variable trap
  5. Selection of inefficient machine learning algorithm
  6. Not trying out various ML algorithms for building the model based on structure of data.
  7. Improper tuning of model parameters
  8. Most importantly: Building an idiotstic imperfect model i.e. suppose we have a classification problem with 99% chances of falling into class1 and remaining to class2. The built model may develop a mapping function which all the time for all data inputs, may predict the result to be class1. Well, one might say his/her model has 99% accuracy. But in reality the 1% class2 case hasn’t been included in the model. So this must be taken into consideration.
  9. Read more here…

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Basically, data mining is a key aspect of data analytics. Some even consider the former as essential to execute before the latter. While data analytics is the complete package and involves most components needed to examine a data set and extract valuable information, data mining focuses specifically on identifying hidden patterns.

That’s just the surface-level comparison though. The image above gives an overview of how the two differ.

One such difference is the presence of a hypothesis. Data analytics usually requires coming up with one, as it aims to find specific answers. Data mining, on the other hand, generally doesn’t need one to test or prove. The expected output are patterns or trends, which doesn’t require coming up with a statement or fact to test.

However, that doesn’t mean you mine data blindly. You still have a goal, whether it’s to come up with a recommender system or identify predictors of a certain dimension. Ultimately though, you strive to come up with data patterns or trends. For data analysis on the other hand, you’re expected to come up with valuable and actionable insights, usually in relation to a predetermined hypothesis. Read more here ….

The data science life cycle is not something well-defined like the software development life-cycle, and there is no ‘one-size-fits-all’ solution for data science projects. Every step in the life-cycle of a data science project depends on various data scientist skills and data science tools. The typical life-cycle of a data science project involves jumping back and forth among various interdependent science tasks using a variety of tools, techniques, programming, etc.

Thus, the data science life-cycle can include the following steps:

  1. Business requirement understanding.
  2. Data collection.
  3. Data cleaning.
  4. Data analysis.
  5. Modeling.
  6. Performance evaluation.
  7. Communicating with stakeholders.
  8. Deployment.
  9. Real-world testing.
  10. Business buy-in.
  11. Support and maintenance.

Looks neat, but here is the scheme to visualize how it is happening in reality:

Agile development processes, especially continuous delivery lends itself well to the data science project life-cycle. The early comparison helps the data science team to change approaches, refine hypotheses and even discard the project if the business case is nonviable or the benefits from the predictive models are not worth the effort to build it.

Read more here….

 

Top

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Machine Learning Q&A -Part II:

 
 
 

At a high level, these skills are a combination of software and data engineering.

The persons that are more appropriate to do this job are a data engineer and/or a machine learning engineer.

That being said, if you work at a startup or happen to be in a small company and need to put the models into production yourself, here are the top skills you need to get:

  • Well structured code: it doesn’t need to be perfect but at least can be understood and updated by other team members. Avoid spaghetti code[1] as the plague.
  • Add logs: if you are a Python user, the logging[2] module is your friend. Avoid print statements at any cost.
  • Model versioning: add a hash key to your different models. You will thank me later.
  • Metadata everywhere: save as much data about your models and ML experiments as you can (running time, hyperparameters, used features, CV scores, and so on). You will thank me later, again.
  • Monitor performances: execution time and statistical scores of your models.
  • Data and models management: store the necessary data and models somewhere that is available to everyone (S3[3] for example). Avoid uploading these to your VCS[4] system. Don’t share them using Slack or Drive. I won’t judge you though, I do it sometimes (read often). Read more here …..

Some of the mistakes that might involve during building a machine learning model (I can think of) are listed here:

  1. Not understanding the structure of the dataset
  2. Not giving proper care during features selection
  3. Leaving out categorical features and considering just numerical variables
  4. Falling into dummy variable trap
  5. Selection of inefficient machine learning algorithm
  6. Not trying out various ML algorithms for building the model based on structure of data.
  7. Improper tuning of model parameters
  8. Most importantly: Building an idiotstic imperfect model i.e. suppose we have a classification problem with 99% chances of falling into class1 and remaining to class2. The built model may develop a mapping function which all the time for all data inputs, may predict the result to be class1. Well, one might say his/her model has 99% accuracy. But in reality the 1% class2 case hasn’t been included in the model. So this must be taken into consideration.
  9. Read more here…

Basically, data mining is a key aspect of data analytics. Some even consider the former as essential to execute before the latter. While data analytics is the complete package and involves most components needed to examine a data set and extract valuable information, data mining focuses specifically on identifying hidden patterns.

That’s just the surface-level comparison though. The image above gives an overview of how the two differ.

One such difference is the presence of a hypothesis. Data analytics usually requires coming up with one, as it aims to find specific answers. Data mining, on the other hand, generally doesn’t need one to test or prove. The expected output are patterns or trends, which doesn’t require coming up with a statement or fact to test.

However, that doesn’t mean you mine data blindly. You still have a goal, whether it’s to come up with a recommender system or identify predictors of a certain dimension. Ultimately though, you strive to come up with data patterns or trends. For data analysis on the other hand, you’re expected to come up with valuable and actionable insights, usually in relation to a predetermined hypothesis. Read more here ….

The data science life cycle is not something well-defined like the software development life-cycle, and there is no ‘one-size-fits-all’ solution for data science projects. Every step in the life-cycle of a data science project depends on various data scientist skills and data science tools. The typical life-cycle of a data science project involves jumping back and forth among various interdependent science tasks using a variety of tools, techniques, programming, etc.

Thus, the data science life-cycle can include the following steps:

  1. Business requirement understanding.
  2. Data collection.
  3. Data cleaning.
  4. Data analysis.
  5. Modeling.
  6. Performance evaluation.
  7. Communicating with stakeholders.
  8. Deployment.
  9. Real-world testing.
  10. Business buy-in.
  11. Support and maintenance.

Looks neat, but here is the scheme to visualize how it is happening in reality:

Agile development processes, especially continuous delivery lends itself well to the data science project life-cycle. The early comparison helps the data science team to change approaches, refine hypotheses and even discard the project if the business case is nonviable or the benefits from the predictive models are not worth the effort to build it.

Read more here….

 

Top

AWS machine Learning Specialty Exam Prep MLS-C01

iOs: https://apps.apple.com/ca/app/aws-machine-learning-prep-pro/id1611045854

Windows: https://www.microsoft.com/en-ca/p/aws-machine-learning-mls-c01-specialty-certification-exam-prep/9n8rl80hvm4t

Android/Amazon: https://www.amazon.com/gp/product/B09TZ4H8V6

AWS MLS-C01 Machine Learning Exam Prep

Quizzes, Practice Exams: Modeling, Data Engineering, Vision, Exploratory Data Analysis, ML Ops, Cheat Sheets, ML Jobs Interview Q&A

Use this App to learn about Machine Learning on AWS and prepare for the AWS Machine Learning Specialty Certification MLS-C01.

Earning AWS Certified Machine Learning Specialty validates expertise in building, training, tuning, and deploying machine learning (ML) models on AWS.

The App provides hundreds of quizzes and practice exam about:

– Machine Learning Operation on AWS

– Modelling

– Data Engineering

– Computer Vision,

– Exploratory Data Analysis,

– ML implementation & Operations

– Machine Learning Basics Questions and Answers

– Machine Learning Advanced Questions and Answers

– Scorecard

– Countdown timer

– Machine Learning Cheat Sheets

– Machine Learning Interview Questions and Answers

– Machine Learning Latest News

The App covers Machine Learning Basics and Advanced topics including: NLP, Computer Vision, Python, linear regression, logistic regression, Sampling, dataset, statistical interaction, selection bias, non-Gaussian distribution, bias-variance trade-off, Normal Distribution, correlation and covariance, Point Estimates and Confidence Interval, A/B Testing, p-value, statistical power of sensitivity, over-fitting and under-fitting, regularization, Law of Large Numbers, Confounding Variables, Survivorship Bias, univariate, bivariate and multivariate, Resampling, ROC curve, TF/IDF vectorization, Cluster Sampling, etc.

Domain 1: Data Engineering

Create data repositories for machine learning.

Identify data sources (e.g., content and location, primary sources such as user data)

Determine storage mediums (e.g., DB, Data Lake, S3, EFS, EBS)

Identify and implement a data ingestion solution.

Data job styles/types (batch load, streaming)

Data ingestion pipelines (Batch-based ML workloads and streaming-based ML workloads), etc.

Domain 2: Exploratory Data Analysis

Sanitize and prepare data for modeling.

Perform feature engineering.

Analyze and visualize data for machine learning.

Domain 3: Modeling

Frame business problems as machine learning problems.

Select the appropriate model(s) for a given machine learning problem.

Train machine learning models.

Perform hyperparameter optimization.

Evaluate machine learning models.

Domain 4: Machine Learning Implementation and Operations

Build machine learning solutions for performance, availability, scalability, resiliency, and fault tolerance.

Recommend and implement the appropriate machine learning services and features for a given problem.

Apply basic AWS security practices to machine learning solutions.

Deploy and operationalize machine learning solutions.

Machine Learning Services covered:

Amazon Comprehend

AWS Deep Learning AMIs (DLAMI)

AWS DeepLens

Amazon Forecast

Amazon Fraud Detector

Amazon Lex

Amazon Polly

Amazon Rekognition

Amazon SageMaker

Amazon Textract

Amazon Transcribe

Amazon Translate

Other Services and topics covered are:

Ingestion/Collection

Processing/ETL

Data analysis/visualization

Model training

Model deployment/inference

Operational

AWS ML application services

Language relevant to ML (for example, Python, Java, Scala, R, SQL)

Notebooks and integrated development environments (IDEs),

S3, SageMaker, Kinesis, Lake Formation, Athena, Kibana, Redshift, Textract, EMR, Glue, SageMaker, CSV, JSON, IMG, parquet or databases, Amazon Athena

Amazon EC2, Amazon Elastic Container Registry (Amazon ECR), Amazon Elastic Container Service, Amazon Elastic Kubernetes Service , Amazon Redshift

Sagemaker API Explained:

SageMaker API

AWS Certified Machine Learning Engineer Specialty Questions and Answers:

Question1: An advertising and analytics company uses machine learning to predict user response to online advertisements using a custom XGBoost model. The company wants to improve its ML pipeline by porting its training and inference code, written in R, to Amazon SageMaker, and do so with minimal changes to the existing code.

Answer1: Use the Build Your Own Container (BYOC) Amazon Sagemaker option.
Create a new docker container with the existing code. Register the container in Amazon Elastic Container registry. with the existing code. Register the container in Amazon Elastic Container Registry. Finally run the training and inference jobs using this container.

Question2: Which feature of Amazon SageMaker can you use for preprocessing the data?

 

Answer2: Amazon Sagemaker Notebook instances

Amazon SageMaker enables developers and data scientists to build, train, tune, and deploy machine learning (ML) models at scale. You can deploy trained ML models for real-time or batch predictions on unseen data, a process known as inference. However, in most cases, the raw input data must be preprocessed and can’t be used directly for making predictions. This is because most ML models expect the data in a predefined format, so the raw data needs to be first cleaned and formatted in order for the ML model to process the data.  You can use the Amazon SageMaker built-in Scikit-learn library for preprocessing input data and then use the Amazon SageMaker built-in Linear Learner algorithm for predictions.

Question3: What setting, when creating an Amazon SageMaker notebook instance, can you use to install libraries and import data?

Answer3: LifeCycle Configuration

Question4: How to Choose the right Sagemaker built-in algorithm?

How to chose the right built in algorithm in SageMaker?
How to chose the right built in algorithm in SageMaker?
Guide to choosing the right unsupervised learning algorithm
Guide to choosing the right unsupervised learning algorithm

 

Choosing the right  ML algorithm based on Data Type
Choosing the right ML algorithm based on Data Type

 

Choosing the right ML algo based on data type
Choosing the right ML algo based on data type

This is a general guide for choosing which algorithm to use depending on what business problem you have and what data you have. 

 

Top

Top 10 Google Professional Machine Learning Engineer Sample Questions

Question 1: You work for a textile manufacturer and have been asked to build a model to detect and classify fabric defects. You trained a machine learning model with high recall based on high resolution images taken at the end of the production line. You want quality control inspectors to gain trust in your model. Which technique should you use to understand the rationale of your classifier?

A. Use K-fold cross validation to understand how the model performs on different test datasets.

B. Use the Integrated Gradients method to efficiently compute feature attributions for each predicted image.

C. Use PCA (Principal Component Analysis) to reduce the original feature set to a smaller set of easily understood features.

D. Use k-means clustering to group similar images together, and calculate the Davies-Bouldin index to evaluate the separation between clusters.

Answer 1)

B

Notes 1)

B is correct because it identifies the pixel of the input image that leads to the classification of the image itself.

Question 2: You need to write a generic test to verify whether Dense Neural Network (DNN) models automatically released by your team have a sufficient number of parameters to learn the task for which they were built. What should you do?

A. Train the model for a few iterations, and check for NaN values.
B. Train the model for a few iterations, and verify that the loss is constant.
C. Train a simple linear model, and determine if the DNN model outperforms it.
D. Train the model with no regularization, and verify that the loss function is close to zero.
 

Answer 2)

D

Notes 2)

D is correct because the test can check that the model has enough parameters to memorize the task.

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Question 3: Your team is using a TensorFlow Inception-v3 CNN model pretrained on ImageNet for an image classification prediction challenge on 10,000 images. You will use AI Platform to perform the model training. What TensorFlow distribution strategy and AI Platform training job configuration should you use to train the model and optimize for wall-clock time?

 

A. Default Strategy; Custom tier with a single master node and four v100 GPUs.
B. One Device Strategy; Custom tier with a single master node and four v100 GPUs.
C. One Device Strategy; Custom tier with a single master node and eight v100 GPUs.
D. Central Storage Strategy; Custom tier with a single master node and four v100 GPUs.
 

Answer 3)

D

Notes 3)

D is correct because this is the only strategy that can perform distributed training; albeit there is only a single copy of the variables on the CPU host.

Question 4: You work on a team where the process for deploying a model into production starts with data scientists training different versions of models in a Kubeflow pipeline. The workflow then stores the new model artifact into the corresponding Cloud Storage bucket. You need to build the next steps of the pipeline after the submitted model is ready to be tested and deployed in production on AI Platform. How should you configure the architecture before deploying the model to production?

 
A. Deploy model in test environment -> Validate model -> Create a new AI Platform model version
 
B. Validate model -> Deploy model in test environment -> Create a new AI Platform model version
 
C. Create a new AI Platform model version -> Validate model -> Deploy model in test environment
D. Create a new AI Platform model version – > Deploy model in test environment -> Validate model
 
Answer 4)
A
 
Notes 4)
A is correct because the model can be validated after it is deployed to the test environment, and the release version is established before the model is deployed in production.
 
Question 5: You work for a maintenance company and have built and trained a deep learning model that identifies defects based on thermal images of underground electric cables. Your dataset contains 10,000 images, 100 of which contain visible defects. How should you evaluate the performance of the model on a test dataset?
 
A. Calculate the Area Under the Curve (AUC) value.
 
B. Calculate the number of true positive results predicted by the model.
C. Calculate the fraction of images predicted by the model to have a visible defect.
D. Calculate the Cosine Similarity to compare the model’s performance on the test dataset to the model’s performance on the training dataset.
 
Answer 5)
A
 
Notes 5)
A is correct because it is scale-invariant. AUC measures how well predictions are ranked, rather than their absolute values. AUC is also classification-threshold invariant. It measures the quality of the model’s predictions irrespective of what classification threshold is chosen.
 
Question 6: You work for a manufacturing company that owns a high-value machine which has several machine settings and multiple sensors. A history of the machine’s hourly sensor readings and known failure event data are stored in BigQuery. You need to predict if the machine will fail within the next 3 days in order to schedule maintenance before the machine fails. Which data preparation and model training steps should you take?

 

A. Data preparation: Daily max value feature engineering with DataPrep; Model training: AutoML classification with BQML
 
B. Data preparation: Daily min value feature engineering with DataPrep; Model training: Logistic regression with BQML and AUTO_CLASS_WEIGHTS set to True
C. Data preparation: Rolling average feature engineering with DataPrep; Model training: Logistic regression with BQML and AUTO_CLASS_WEIGHTS set to False
D. Data preparation: Rolling average feature engineering with DataPrep; Model training: Logistic regression with BQML and AUTO_CLASS_WEIGHTS set to True
Answer 6)
D
 
Notes 6)
D is correct because it uses the rolling average of the sensor data and balances the weights using the BQML auto class weight balance parameter.
 
 
Question 7: You are an ML engineer at a media company. You need to build an ML model to analyze video content frame-by-frame, identify objects, and alert users if there is inappropriate content. Which Google Cloud products should you use to build this project?

 

A. Pub/Sub, Cloud Function, Cloud Vision API
 
B. Pub/Sub, Cloud IoT, Dataflow, Cloud Vision API, Cloud Logging
C. Pub/Sub, Cloud Function, Video Intelligence API, Cloud Logging
D. Pub/Sub, Cloud Function, AutoML Video Intelligence, Cloud Logging
 
Answer 7)
C
 
Notes 7)
C is correct as Video Intelligence API can find inappropriate components and other components satisfy the requirements of real-time processing and notification.
 
Question 8: You work for a large retailer. You want to use ML to forecast future sales leveraging 10 years of historical sales data. The historical data is stored in Cloud Storage in Avro format. You want to rapidly experiment with all the available data. How should you build and train your model for the sales forecast?
 
A. Load data into BigQuery and use the ARIMA model type on BigQuery ML.
B. Convert the data into CSV format and create a regression model on AutoML Tables.
C. Convert the data into TFRecords and create an RNN model on TensorFlow on AI Platform Notebooks.
D. Convert and refactor the data into CSV format and use the built-in XGBoost algorithm on AI Platform Training.
 
Answer 8)
A
 
Notes 8)
A is correct because BigQuery ML is designed for fast and rapid experimentation and it is possible to use federated queries to read data directly from Cloud Storage. Moreover, ARIMA is considered one of the best in class for time series forecasting.
 
Question 9) You need to build an object detection model for a small startup company to identify if and where the company’s logo appears in an image. You were given a large repository of images, some with logos and some without. These images are not yet labelled. You need to label these pictures, and then train and deploy the model. What should you do?

 

A. Use Google Cloud’s Data Labelling Service to label your data. Use AutoML Object Detection to train and deploy the model.
B. Use Vision API to detect and identify logos in pictures and use it as a label. Use AI Platform to build and train a convolutional neural network.
 
C. Create two folders: one where the logo appears and one where it doesn’t. Manually place images in each folder. Use AI Platform to build and train a convolutional neural network.
D. Create two folders: one where the logo appears and one where it doesn’t. Manually place images in each folder. Use AI Platform to build and train a real time object detection model.
 
Answer 9)
A
 
Notes 9)
A is correct as this will allow you to easily create a request for a labelling task and deploy a high-performance model.
 

Question 10) You work for a large financial institution that is planning to use Dialogflow to create a chatbot for the company’s mobile app. You have reviewed old chat logs and tagged each conversation for intent based on each customer’s stated intention for contacting customer service. About 70% of customer inquiries are simple requests that are solved within 10 intents. The remaining 30% of inquiries require much longer and more complicated requests. Which intents should you automate first?

A. Automate a blend of the shortest and longest intents to be representative of all intents.
B. Automate the more complicated requests first because those require more of the agents’ time.
C. Automate the 10 intents that cover 70% of the requests so that live agents can handle the more complicated requests.
 
D. Automate intents in places where common words such as “payment” only appear once to avoid confusing the software.
Answer 10)
C
 
Notes 10)

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Machine Learning Q&A Part I:

Google.

Azure and AWS are second class citizens in this area.

Sure, AWS has 70% of the market.

Sure, Azure is the easiest turn key and super user friendly.

But, the king of machine learning in the cloud is GCP.

GCP = Google Cloud Platform

Google has the largest data science team in the world, not mention they have Hinton.

Let’s forgot for a minute they created TensorFlow and give it away.

Let’s just talk about building a real world model with data that doesn’t fit into a excel spreadsheet.

The vast majority of applied machine learning is supervised and that means we need data.

Not just normal data, we need very clean highly structured data.

Where’s the easiest place in the world to upload and model a Petabyte of structured dataBigQuery of course.

Why BigQuery? I don’t have to do anything but upload my data. No spinning up RedShit clusters or whatever I have to do in Azure, just upload and massage data with my familiar SQL. If I do have to wrangle my data it won’t take my six months to update 5 rows here, minutes usually.

Then, you’ll need a front end. Cloud datalab is a Jupyter notebook, which is good because I don’t want nor do I need anything else.

Then, with a single line of code I connect by datalab (Jupyter) notebook to my data in BigQuery and build away.

I’ve worked in all three and the only thing I care about is getting to my job the fastest and right now that means I build my models in GCP.

If you’re new to machine learning don’t start in GCP or any cloud vendor for that matter. Start learning Python from the comfort of your laptop.

The course below is free to the first 20.

The Complete Python Course for Machine Learning Engineers

Here, I want to share the best research paper on Machine Learning classification methods, titled ‘Do we Need Hundreds of Classifiers to Solve Real World Classification Problems?’, published in the ‘Journal of Machine Learning Research’.

This paper nicely explained 179 classification techniques and applied them on 121 data sets thus sharing small summary of the paper:

Do we Need Hundreds of Classifiers to Solve Real World Classification Problems?

 
 
 

The paper evaluated 179 classifiers arising from 17 ML families (discriminant analysis, Bayesian, neural networks, support vector machines, decision trees, rule-based classifiers, boosting, bagging, stacking, random forests and other ensembles, generalized linear models, nearest neighbours, partial least squares and principal component regression, logistic and multinomial regression, multiple adaptive regression splines and other methods), implemented in Weka, R ( with and without the caret package), C and Matlab, including all the relevant classifiers available today.

Experiments used total 121 data sets , which represent the whole UCI data base (excluding the large-scale problems) and other own real problems, in order to achieve significant conclusions about the classifier behaviour, not dependent on the data set collection.

The whole data set and partitions are available from: http://persoal.citius.usc.es/manuel.fernandez.delgado/papers/jmlr/data.tar.gz

The classifiers most likely to be the bests are the random forest (RF) versions, the best of which (implemented in R and accessed via caret) achieves 94.1% of the maximum accuracy overcoming 90% in the 84.3% of the data sets. However, the difference is not statistically significant with the second best, the SVM with Gaussian kernel implemented in C using LibSVM, which achieves 92.3% of the maximum accuracy. A few models are clearly better than the remaining ones: random forest, SVM with Gaussian and polynomial kernels, extreme learning machine with Gaussian kernel, C5.0 and avNNet (a committee of multi-layer perceptrons implemented in R with the caret package).

The random forest is clearly the best family of classifiers (3 out of 5 bests classifiers are RF), followed by SVM (4 classifiers in the top-10), neural networks and boosting ensembles (5 and 3 members in the top-20, respectively).

You can see the table with the complete results: http://persoal.citius.usc.es/manuel.fernandez.delgado/papers/jmlr/results.txt

I hope it will be helpful for Statistic and Machine Leaning aspirants!

Thank you!

 
 
 

At a high level, these skills are a combination of software and data engineering.

The persons that are more appropriate to do this job are a data engineer and/or a machine learning engineer.

That being said, if you work at a startup or happen to be in a small company and need to put the models into production yourself, here are the top skills you need to get:

  • Well structured code: it doesn’t need to be perfect but at least can be understood and updated by other team members. Avoid spaghetti code[1] as the plague.
  • Add logs: if you are a Python user, the logging[2] module is your friend. Avoid print statements at any cost.
  • Model versioning: add a hash key to your different models. You will thank me later.
  • Metadata everywhere: save as much data about your models and ML experiments as you can (running time, hyperparameters, used features, CV scores, and so on). You will thank me later, again.
  • Monitor performances: execution time and statistical scores of your models.
  • Data and models management: store the necessary data and models somewhere that is available to everyone (S3[3] for example). Avoid uploading these to your VCS[4] system. Don’t share them using Slack or Drive. I won’t judge you though, I do it sometimes (read often). Read more here …..

Some of the mistakes that might involve during building a machine learning model (I can think of) are listed here:

  1. Not understanding the structure of the dataset
  2. Not giving proper care during features selection
  3. Leaving out categorical features and considering just numerical variables
  4. Falling into dummy variable trap
  5. Selection of inefficient machine learning algorithm
  6. Not trying out various ML algorithms for building the model based on structure of data.
  7. Improper tuning of model parameters
  8. Most importantly: Building an idiotstic imperfect model i.e. suppose we have a classification problem with 99% chances of falling into class1 and remaining to class2. The built model may develop a mapping function which all the time for all data inputs, may predict the result to be class1. Well, one might say his/her model has 99% accuracy. But in reality the 1% class2 case hasn’t been included in the model. So this must be taken into consideration.
  9. Read more here…

[appbox appstore 1560083470-iphone screenshots]
[appbox googleplay com.awssolutionarchitectassociateexampreppro.app]

Basically, data mining is a key aspect of data analytics. Some even consider the former as essential to execute before the latter. While data analytics is the complete package and involves most components needed to examine a data set and extract valuable information, data mining focuses specifically on identifying hidden patterns.

That’s just the surface-level comparison though. The image above gives an overview of how the two differ.

One such difference is the presence of a hypothesis. Data analytics usually requires coming up with one, as it aims to find specific answers. Data mining, on the other hand, generally doesn’t need one to test or prove. The expected output are patterns or trends, which doesn’t require coming up with a statement or fact to test.

However, that doesn’t mean you mine data blindly. You still have a goal, whether it’s to come up with a recommender system or identify predictors of a certain dimension. Ultimately though, you strive to come up with data patterns or trends. For data analysis on the other hand, you’re expected to come up with valuable and actionable insights, usually in relation to a predetermined hypothesis. Read more here ….

The data science life cycle is not something well-defined like the software development life-cycle, and there is no ‘one-size-fits-all’ solution for data science projects. Every step in the life-cycle of a data science project depends on various data scientist skills and data science tools. The typical life-cycle of a data science project involves jumping back and forth among various interdependent science tasks using a variety of tools, techniques, programming, etc.

Thus, the data science life-cycle can include the following steps:

  1. Business requirement understanding.
  2. Data collection.
  3. Data cleaning.
  4. Data analysis.
  5. Modeling.
  6. Performance evaluation.
  7. Communicating with stakeholders.
  8. Deployment.
  9. Real-world testing.
  10. Business buy-in.
  11. Support and maintenance.

Looks neat, but here is the scheme to visualize how it is happening in reality:

Agile development processes, especially continuous delivery lends itself well to the data science project life-cycle. The early comparison helps the data science team to change approaches, refine hypotheses and even discard the project if the business case is nonviable or the benefits from the predictive models are not worth the effort to build it.

Read more here….

 

Top

[appbox appstore 1611045854-iphone screenshots]

[appbox microsoftstore  9n8rl80hvm4t-mobile screenshots]

Machine Learning Q&A -Part II:

 
 
 

At a high level, these skills are a combination of software and data engineering.

The persons that are more appropriate to do this job are a data engineer and/or a machine learning engineer.

That being said, if you work at a startup or happen to be in a small company and need to put the models into production yourself, here are the top skills you need to get:

  • Well structured code: it doesn’t need to be perfect but at least can be understood and updated by other team members. Avoid spaghetti code[1] as the plague.
  • Add logs: if you are a Python user, the logging[2] module is your friend. Avoid print statements at any cost.
  • Model versioning: add a hash key to your different models. You will thank me later.
  • Metadata everywhere: save as much data about your models and ML experiments as you can (running time, hyperparameters, used features, CV scores, and so on). You will thank me later, again.
  • Monitor performances: execution time and statistical scores of your models.
  • Data and models management: store the necessary data and models somewhere that is available to everyone (S3[3] for example). Avoid uploading these to your VCS[4] system. Don’t share them using Slack or Drive. I won’t judge you though, I do it sometimes (read often). Read more here …..

Some of the mistakes that might involve during building a machine learning model (I can think of) are listed here:

  1. Not understanding the structure of the dataset
  2. Not giving proper care during features selection
  3. Leaving out categorical features and considering just numerical variables
  4. Falling into dummy variable trap
  5. Selection of inefficient machine learning algorithm
  6. Not trying out various ML algorithms for building the model based on structure of data.
  7. Improper tuning of model parameters
  8. Most importantly: Building an idiotstic imperfect model i.e. suppose we have a classification problem with 99% chances of falling into class1 and remaining to class2. The built model may develop a mapping function which all the time for all data inputs, may predict the result to be class1. Well, one might say his/her model has 99% accuracy. But in reality the 1% class2 case hasn’t been included in the model. So this must be taken into consideration.
  9. Read more here…

Basically, data mining is a key aspect of data analytics. Some even consider the former as essential to execute before the latter. While data analytics is the complete package and involves most components needed to examine a data set and extract valuable information, data mining focuses specifically on identifying hidden patterns.

That’s just the surface-level comparison though. The image above gives an overview of how the two differ.

One such difference is the presence of a hypothesis. Data analytics usually requires coming up with one, as it aims to find specific answers. Data mining, on the other hand, generally doesn’t need one to test or prove. The expected output are patterns or trends, which doesn’t require coming up with a statement or fact to test.

However, that doesn’t mean you mine data blindly. You still have a goal, whether it’s to come up with a recommender system or identify predictors of a certain dimension. Ultimately though, you strive to come up with data patterns or trends. For data analysis on the other hand, you’re expected to come up with valuable and actionable insights, usually in relation to a predetermined hypothesis. Read more here ….

The data science life cycle is not something well-defined like the software development life-cycle, and there is no ‘one-size-fits-all’ solution for data science projects. Every step in the life-cycle of a data science project depends on various data scientist skills and data science tools. The typical life-cycle of a data science project involves jumping back and forth among various interdependent science tasks using a variety of tools, techniques, programming, etc.

Thus, the data science life-cycle can include the following steps:

  1. Business requirement understanding.
  2. Data collection.
  3. Data cleaning.
  4. Data analysis.
  5. Modeling.
  6. Performance evaluation.
  7. Communicating with stakeholders.
  8. Deployment.
  9. Real-world testing.
  10. Business buy-in.
  11. Support and maintenance.

Looks neat, but here is the scheme to visualize how it is happening in reality:

Agile development processes, especially continuous delivery lends itself well to the data science project life-cycle. The early comparison helps the data science team to change approaches, refine hypotheses and even discard the project if the business case is nonviable or the benefits from the predictive models are not worth the effort to build it.

Read more here….

 

Top

 

AWS machine Learning Specialty Exam Prep MLS-C01

iOs: https://apps.apple.com/ca/app/aws-machine-learning-prep-pro/id1611045854

Windows: https://www.microsoft.com/en-ca/p/aws-machine-learning-mls-c01-specialty-certification-exam-prep/9n8rl80hvm4t

Android/Amazon: https://www.amazon.com/gp/product/B09TZ4H8V6

AWS MLS-C01 Machine Learning Exam Prep

Quizzes, Practice Exams: Modeling, Data Engineering, Vision, Exploratory Data Analysis, ML Ops, Cheat Sheets, ML Jobs Interview Q&A

Use this App to learn about Machine Learning on AWS and prepare for the AWS Machine Learning Specialty Certification MLS-C01.

Earning AWS Certified Machine Learning Specialty validates expertise in building, training, tuning, and deploying machine learning (ML) models on AWS.

The App provides hundreds of quizzes and practice exam about:

– Machine Learning Operation on AWS

– Modelling

– Data Engineering

– Computer Vision,

– Exploratory Data Analysis,

– ML implementation & Operations

– Machine Learning Basics Questions and Answers

– Machine Learning Advanced Questions and Answers

– Scorecard

– Countdown timer

– Machine Learning Cheat Sheets

– Machine Learning Interview Questions and Answers

– Machine Learning Latest News

The App covers Machine Learning Basics and Advanced topics including: NLP, Computer Vision, Python, linear regression, logistic regression, Sampling, dataset, statistical interaction, selection bias, non-Gaussian distribution, bias-variance trade-off, Normal Distribution, correlation and covariance, Point Estimates and Confidence Interval, A/B Testing, p-value, statistical power of sensitivity, over-fitting and under-fitting, regularization, Law of Large Numbers, Confounding Variables, Survivorship Bias, univariate, bivariate and multivariate, Resampling, ROC curve, TF/IDF vectorization, Cluster Sampling, etc.

Domain 1: Data Engineering

Create data repositories for machine learning.

Identify data sources (e.g., content and location, primary sources such as user data)

Determine storage mediums (e.g., DB, Data Lake, S3, EFS, EBS)

Identify and implement a data ingestion solution.

Data job styles/types (batch load, streaming)

Data ingestion pipelines (Batch-based ML workloads and streaming-based ML workloads), etc.

Domain 2: Exploratory Data Analysis

Sanitize and prepare data for modeling.

Perform feature engineering.

Analyze and visualize data for machine learning.

Domain 3: Modeling

Frame business problems as machine learning problems.

Select the appropriate model(s) for a given machine learning problem.

Train machine learning models.

Perform hyperparameter optimization.

Evaluate machine learning models.

Domain 4: Machine Learning Implementation and Operations

Build machine learning solutions for performance, availability, scalability, resiliency, and fault tolerance.

Recommend and implement the appropriate machine learning services and features for a given problem.

Apply basic AWS security practices to machine learning solutions.

Deploy and operationalize machine learning solutions.

Machine Learning Services covered:

Amazon Comprehend

AWS Deep Learning AMIs (DLAMI)

AWS DeepLens

Amazon Forecast

Amazon Fraud Detector

Amazon Lex

Amazon Polly

Amazon Rekognition

Amazon SageMaker

Amazon Textract

Amazon Transcribe

Amazon Translate

Other Services and topics covered are:

Ingestion/Collection

Processing/ETL

Data analysis/visualization

Model training

Model deployment/inference

Operational

AWS ML application services

Language relevant to ML (for example, Python, Java, Scala, R, SQL)

Notebooks and integrated development environments (IDEs),

S3, SageMaker, Kinesis, Lake Formation, Athena, Kibana, Redshift, Textract, EMR, Glue, SageMaker, CSV, JSON, IMG, parquet or databases, Amazon Athena

Amazon EC2, Amazon Elastic Container Registry (Amazon ECR), Amazon Elastic Container Service, Amazon Elastic Kubernetes Service , Amazon Redshift

Sagemaker API Explained:

SageMaker API

AWS Certified Machine Learning Engineer Specialty Questions and Answers:

Question1: An advertising and analytics company uses machine learning to predict user response to online advertisements using a custom XGBoost model. The company wants to improve its ML pipeline by porting its training and inference code, written in R, to Amazon SageMaker, and do so with minimal changes to the existing code.

Answer1: Use the Build Your Own Container (BYOC) Amazon Sagemaker option.
Create a new docker container with the existing code. Register the container in Amazon Elastic Container registry. with the existing code. Register the container in Amazon Elastic Container Registry. Finally run the training and inference jobs using this container.

Question2: Which feature of Amazon SageMaker can you use for preprocessing the data?

 

Answer2: Amazon Sagemaker Notebook instances

Amazon SageMaker enables developers and data scientists to build, train, tune, and deploy machine learning (ML) models at scale. You can deploy trained ML models for real-time or batch predictions on unseen data, a process known as inference. However, in most cases, the raw input data must be preprocessed and can’t be used directly for making predictions. This is because most ML models expect the data in a predefined format, so the raw data needs to be first cleaned and formatted in order for the ML model to process the data.  You can use the Amazon SageMaker built-in Scikit-learn library for preprocessing input data and then use the Amazon SageMaker built-in Linear Learner algorithm for predictions.

Question3: What setting, when creating an Amazon SageMaker notebook instance, can you use to install libraries and import data?

Answer3: LifeCycle Configuration

Question4: How to Choose the right Sagemaker built-in algorithm?

How to chose the right built in algorithm in SageMaker?
How to chose the right built in algorithm in SageMaker?
Guide to choosing the right unsupervised learning algorithm
Guide to choosing the right unsupervised learning algorithm

 

Choosing the right  ML algorithm based on Data Type
Choosing the right ML algorithm based on Data Type

 

Choosing the right ML algo based on data type
Choosing the right ML algo based on data type

This is a general guide for choosing which algorithm to use depending on what business problem you have and what data you have. 

 

Top

Top 10 Google Professional Machine Learning Engineer Sample Questions

Question 1: You work for a textile manufacturer and have been asked to build a model to detect and classify fabric defects. You trained a machine learning model with high recall based on high resolution images taken at the end of the production line. You want quality control inspectors to gain trust in your model. Which technique should you use to understand the rationale of your classifier?

A. Use K-fold cross validation to understand how the model performs on different test datasets.

B. Use the Integrated Gradients method to efficiently compute feature attributions for each predicted image.

C. Use PCA (Principal Component Analysis) to reduce the original feature set to a smaller set of easily understood features.

D. Use k-means clustering to group similar images together, and calculate the Davies-Bouldin index to evaluate the separation between clusters.

Answer 1)

B

Notes 1)

B is correct because it identifies the pixel of the input image that leads to the classification of the image itself.

Question 2: You need to write a generic test to verify whether Dense Neural Network (DNN) models automatically released by your team have a sufficient number of parameters to learn the task for which they were built. What should you do?

A. Train the model for a few iterations, and check for NaN values.
B. Train the model for a few iterations, and verify that the loss is constant.
C. Train a simple linear model, and determine if the DNN model outperforms it.
D. Train the model with no regularization, and verify that the loss function is close to zero.
 

Answer 2)

D

Notes 2)

D is correct because the test can check that the model has enough parameters to memorize the task.

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Question 3: Your team is using a TensorFlow Inception-v3 CNN model pretrained on ImageNet for an image classification prediction challenge on 10,000 images. You will use AI Platform to perform the model training. What TensorFlow distribution strategy and AI Platform training job configuration should you use to train the model and optimize for wall-clock time?

 

A. Default Strategy; Custom tier with a single master node and four v100 GPUs.
B. One Device Strategy; Custom tier with a single master node and four v100 GPUs.
C. One Device Strategy; Custom tier with a single master node and eight v100 GPUs.
D. Central Storage Strategy; Custom tier with a single master node and four v100 GPUs.
 

Answer 3)

D

Notes 3)

D is correct because this is the only strategy that can perform distributed training; albeit there is only a single copy of the variables on the CPU host.

Question 4: You work on a team where the process for deploying a model into production starts with data scientists training different versions of models in a Kubeflow pipeline. The workflow then stores the new model artifact into the corresponding Cloud Storage bucket. You need to build the next steps of the pipeline after the submitted model is ready to be tested and deployed in production on AI Platform. How should you configure the architecture before deploying the model to production?

 
A. Deploy model in test environment -> Validate model -> Create a new AI Platform model version
 
B. Validate model -> Deploy model in test environment -> Create a new AI Platform model version
 
C. Create a new AI Platform model version -> Validate model -> Deploy model in test environment
D. Create a new AI Platform model version – > Deploy model in test environment -> Validate model
 
Answer 4)
A
 
Notes 4)
A is correct because the model can be validated after it is deployed to the test environment, and the release version is established before the model is deployed in production.
 
Question 5: You work for a maintenance company and have built and trained a deep learning model that identifies defects based on thermal images of underground electric cables. Your dataset contains 10,000 images, 100 of which contain visible defects. How should you evaluate the performance of the model on a test dataset?
 
A. Calculate the Area Under the Curve (AUC) value.
 
B. Calculate the number of true positive results predicted by the model.
C. Calculate the fraction of images predicted by the model to have a visible defect.
D. Calculate the Cosine Similarity to compare the model’s performance on the test dataset to the model’s performance on the training dataset.
 
Answer 5)
A
 
Notes 5)
A is correct because it is scale-invariant. AUC measures how well predictions are ranked, rather than their absolute values. AUC is also classification-threshold invariant. It measures the quality of the model’s predictions irrespective of what classification threshold is chosen.
 
Question 6: You work for a manufacturing company that owns a high-value machine which has several machine settings and multiple sensors. A history of the machine’s hourly sensor readings and known failure event data are stored in BigQuery. You need to predict if the machine will fail within the next 3 days in order to schedule maintenance before the machine fails. Which data preparation and model training steps should you take?

 

A. Data preparation: Daily max value feature engineering with DataPrep; Model training: AutoML classification with BQML
 
B. Data preparation: Daily min value feature engineering with DataPrep; Model training: Logistic regression with BQML and AUTO_CLASS_WEIGHTS set to True
C. Data preparation: Rolling average feature engineering with DataPrep; Model training: Logistic regression with BQML and AUTO_CLASS_WEIGHTS set to False
D. Data preparation: Rolling average feature engineering with DataPrep; Model training: Logistic regression with BQML and AUTO_CLASS_WEIGHTS set to True
Answer 6)
D
 
Notes 6)
D is correct because it uses the rolling average of the sensor data and balances the weights using the BQML auto class weight balance parameter.
 
 
Question 7: You are an ML engineer at a media company. You need to build an ML model to analyze video content frame-by-frame, identify objects, and alert users if there is inappropriate content. Which Google Cloud products should you use to build this project?

 

A. Pub/Sub, Cloud Function, Cloud Vision API
 
B. Pub/Sub, Cloud IoT, Dataflow, Cloud Vision API, Cloud Logging
C. Pub/Sub, Cloud Function, Video Intelligence API, Cloud Logging
D. Pub/Sub, Cloud Function, AutoML Video Intelligence, Cloud Logging
 
Answer 7)
C
 
Notes 7)
C is correct as Video Intelligence API can find inappropriate components and other components satisfy the requirements of real-time processing and notification.
 
Question 8: You work for a large retailer. You want to use ML to forecast future sales leveraging 10 years of historical sales data. The historical data is stored in Cloud Storage in Avro format. You want to rapidly experiment with all the available data. How should you build and train your model for the sales forecast?
 
A. Load data into BigQuery and use the ARIMA model type on BigQuery ML.
B. Convert the data into CSV format and create a regression model on AutoML Tables.
C. Convert the data into TFRecords and create an RNN model on TensorFlow on AI Platform Notebooks.
D. Convert and refactor the data into CSV format and use the built-in XGBoost algorithm on AI Platform Training.
 
Answer 8)
A
 
Notes 8)
A is correct because BigQuery ML is designed for fast and rapid experimentation and it is possible to use federated queries to read data directly from Cloud Storage. Moreover, ARIMA is considered one of the best in class for time series forecasting.
 
Question 9) You need to build an object detection model for a small startup company to identify if and where the company’s logo appears in an image. You were given a large repository of images, some with logos and some without. These images are not yet labelled. You need to label these pictures, and then train and deploy the model. What should you do?

 

A. Use Google Cloud’s Data Labelling Service to label your data. Use AutoML Object Detection to train and deploy the model.
B. Use Vision API to detect and identify logos in pictures and use it as a label. Use AI Platform to build and train a convolutional neural network.
 
C. Create two folders: one where the logo appears and one where it doesn’t. Manually place images in each folder. Use AI Platform to build and train a convolutional neural network.
D. Create two folders: one where the logo appears and one where it doesn’t. Manually place images in each folder. Use AI Platform to build and train a real time object detection model.
 
Answer 9)
A
 
Notes 9)
A is correct as this will allow you to easily create a request for a labelling task and deploy a high-performance model.
 

Question 10) You work for a large financial institution that is planning to use Dialogflow to create a chatbot for the company’s mobile app. You have reviewed old chat logs and tagged each conversation for intent based on each customer’s stated intention for contacting customer service. About 70% of customer inquiries are simple requests that are solved within 10 intents. The remaining 30% of inquiries require much longer and more complicated requests. Which intents should you automate first?

A. Automate a blend of the shortest and longest intents to be representative of all intents.
B. Automate the more complicated requests first because those require more of the agents’ time.
C. Automate the 10 intents that cover 70% of the requests so that live agents can handle the more complicated requests.
 
D. Automate intents in places where common words such as “payment” only appear once to avoid confusing the software.
Answer 10)
C
 
Notes 10)

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[appbox microsoftstore  9n8rl80hvm4t-mobile screenshots]

Machine Learning Q&A Part I:

Google.

Azure and AWS are second class citizens in this area.

Sure, AWS has 70% of the market.

Sure, Azure is the easiest turn key and super user friendly.

But, the king of machine learning in the cloud is GCP.

GCP = Google Cloud Platform

Google has the largest data science team in the world, not mention they have Hinton.

Let’s forgot for a minute they created TensorFlow and give it away.

Let’s just talk about building a real world model with data that doesn’t fit into a excel spreadsheet.

The vast majority of applied machine learning is supervised and that means we need data.

Not just normal data, we need very clean highly structured data.

Where’s the easiest place in the world to upload and model a Petabyte of structured dataBigQuery of course.

Why BigQuery? I don’t have to do anything but upload my data. No spinning up RedShit clusters or whatever I have to do in Azure, just upload and massage data with my familiar SQL. If I do have to wrangle my data it won’t take my six months to update 5 rows here, minutes usually.

Then, you’ll need a front end. Cloud datalab is a Jupyter notebook, which is good because I don’t want nor do I need anything else.

Then, with a single line of code I connect by datalab (Jupyter) notebook to my data in BigQuery and build away.

I’ve worked in all three and the only thing I care about is getting to my job the fastest and right now that means I build my models in GCP.

If you’re new to machine learning don’t start in GCP or any cloud vendor for that matter. Start learning Python from the comfort of your laptop.

The course below is free to the first 20.

The Complete Python Course for Machine Learning Engineers

Here, I want to share the best research paper on Machine Learning classification methods, titled ‘Do we Need Hundreds of Classifiers to Solve Real World Classification Problems?’, published in the ‘Journal of Machine Learning Research’.

This paper nicely explained 179 classification techniques and applied them on 121 data sets thus sharing small summary of the paper:

Do we Need Hundreds of Classifiers to Solve Real World Classification Problems?

 
 
 

The paper evaluated 179 classifiers arising from 17 ML families (discriminant analysis, Bayesian, neural networks, support vector machines, decision trees, rule-based classifiers, boosting, bagging, stacking, random forests and other ensembles, generalized linear models, nearest neighbours, partial least squares and principal component regression, logistic and multinomial regression, multiple adaptive regression splines and other methods), implemented in Weka, R ( with and without the caret package), C and Matlab, including all the relevant classifiers available today.

Experiments used total 121 data sets , which represent the whole UCI data base (excluding the large-scale problems) and other own real problems, in order to achieve significant conclusions about the classifier behaviour, not dependent on the data set collection.

The whole data set and partitions are available from: http://persoal.citius.usc.es/manuel.fernandez.delgado/papers/jmlr/data.tar.gz

The classifiers most likely to be the bests are the random forest (RF) versions, the best of which (implemented in R and accessed via caret) achieves 94.1% of the maximum accuracy overcoming 90% in the 84.3% of the data sets. However, the difference is not statistically significant with the second best, the SVM with Gaussian kernel implemented in C using LibSVM, which achieves 92.3% of the maximum accuracy. A few models are clearly better than the remaining ones: random forest, SVM with Gaussian and polynomial kernels, extreme learning machine with Gaussian kernel, C5.0 and avNNet (a committee of multi-layer perceptrons implemented in R with the caret package).

The random forest is clearly the best family of classifiers (3 out of 5 bests classifiers are RF), followed by SVM (4 classifiers in the top-10), neural networks and boosting ensembles (5 and 3 members in the top-20, respectively).

You can see the table with the complete results: http://persoal.citius.usc.es/manuel.fernandez.delgado/papers/jmlr/results.txt

I hope it will be helpful for Statistic and Machine Leaning aspirants!

Thank you!

 
 
 

At a high level, these skills are a combination of software and data engineering.

The persons that are more appropriate to do this job are a data engineer and/or a machine learning engineer.

That being said, if you work at a startup or happen to be in a small company and need to put the models into production yourself, here are the top skills you need to get:

  • Well structured code: it doesn’t need to be perfect but at least can be understood and updated by other team members. Avoid spaghetti code[1] as the plague.
  • Add logs: if you are a Python user, the logging[2] module is your friend. Avoid print statements at any cost.
  • Model versioning: add a hash key to your different models. You will thank me later.
  • Metadata everywhere: save as much data about your models and ML experiments as you can (running time, hyperparameters, used features, CV scores, and so on). You will thank me later, again.
  • Monitor performances: execution time and statistical scores of your models.
  • Data and models management: store the necessary data and models somewhere that is available to everyone (S3[3] for example). Avoid uploading these to your VCS[4] system. Don’t share them using Slack or Drive. I won’t judge you though, I do it sometimes (read often). Read more here …..

Some of the mistakes that might involve during building a machine learning model (I can think of) are listed here:

  1. Not understanding the structure of the dataset
  2. Not giving proper care during features selection
  3. Leaving out categorical features and considering just numerical variables
  4. Falling into dummy variable trap
  5. Selection of inefficient machine learning algorithm
  6. Not trying out various ML algorithms for building the model based on structure of data.
  7. Improper tuning of model parameters
  8. Most importantly: Building an idiotstic imperfect model i.e. suppose we have a classification problem with 99% chances of falling into class1 and remaining to class2. The built model may develop a mapping function which all the time for all data inputs, may predict the result to be class1. Well, one might say his/her model has 99% accuracy. But in reality the 1% class2 case hasn’t been included in the model. So this must be taken into consideration.
  9. Read more here…

[appbox appstore 1560083470-iphone screenshots]
[appbox googleplay com.awssolutionarchitectassociateexampreppro.app]

Basically, data mining is a key aspect of data analytics. Some even consider the former as essential to execute before the latter. While data analytics is the complete package and involves most components needed to examine a data set and extract valuable information, data mining focuses specifically on identifying hidden patterns.

That’s just the surface-level comparison though. The image above gives an overview of how the two differ.

One such difference is the presence of a hypothesis. Data analytics usually requires coming up with one, as it aims to find specific answers. Data mining, on the other hand, generally doesn’t need one to test or prove. The expected output are patterns or trends, which doesn’t require coming up with a statement or fact to test.

However, that doesn’t mean you mine data blindly. You still have a goal, whether it’s to come up with a recommender system or identify predictors of a certain dimension. Ultimately though, you strive to come up with data patterns or trends. For data analysis on the other hand, you’re expected to come up with valuable and actionable insights, usually in relation to a predetermined hypothesis. Read more here ….

The data science life cycle is not something well-defined like the software development life-cycle, and there is no ‘one-size-fits-all’ solution for data science projects. Every step in the life-cycle of a data science project depends on various data scientist skills and data science tools. The typical life-cycle of a data science project involves jumping back and forth among various interdependent science tasks using a variety of tools, techniques, programming, etc.

Thus, the data science life-cycle can include the following steps:

  1. Business requirement understanding.
  2. Data collection.
  3. Data cleaning.
  4. Data analysis.
  5. Modeling.
  6. Performance evaluation.
  7. Communicating with stakeholders.
  8. Deployment.
  9. Real-world testing.
  10. Business buy-in.
  11. Support and maintenance.

Looks neat, but here is the scheme to visualize how it is happening in reality:

Agile development processes, especially continuous delivery lends itself well to the data science project life-cycle. The early comparison helps the data science team to change approaches, refine hypotheses and even discard the project if the business case is nonviable or the benefits from the predictive models are not worth the effort to build it.

Read more here….

 

Top

[appbox appstore 1611045854-iphone screenshots]

[appbox microsoftstore  9n8rl80hvm4t-mobile screenshots]

Machine Learning Q&A -Part II:

 
 
 

At a high level, these skills are a combination of software and data engineering.

The persons that are more appropriate to do this job are a data engineer and/or a machine learning engineer.

That being said, if you work at a startup or happen to be in a small company and need to put the models into production yourself, here are the top skills you need to get:

  • Well structured code: it doesn’t need to be perfect but at least can be understood and updated by other team members. Avoid spaghetti code[1] as the plague.
  • Add logs: if you are a Python user, the logging[2] module is your friend. Avoid print statements at any cost.
  • Model versioning: add a hash key to your different models. You will thank me later.
  • Metadata everywhere: save as much data about your models and ML experiments as you can (running time, hyperparameters, used features, CV scores, and so on). You will thank me later, again.
  • Monitor performances: execution time and statistical scores of your models.
  • Data and models management: store the necessary data and models somewhere that is available to everyone (S3[3] for example). Avoid uploading these to your VCS[4] system. Don’t share them using Slack or Drive. I won’t judge you though, I do it sometimes (read often). Read more here …..

Some of the mistakes that might involve during building a machine learning model (I can think of) are listed here:

  1. Not understanding the structure of the dataset
  2. Not giving proper care during features selection
  3. Leaving out categorical features and considering just numerical variables
  4. Falling into dummy variable trap
  5. Selection of inefficient machine learning algorithm
  6. Not trying out various ML algorithms for building the model based on structure of data.
  7. Improper tuning of model parameters
  8. Most importantly: Building an idiotstic imperfect model i.e. suppose we have a classification problem with 99% chances of falling into class1 and remaining to class2. The built model may develop a mapping function which all the time for all data inputs, may predict the result to be class1. Well, one might say his/her model has 99% accuracy. But in reality the 1% class2 case hasn’t been included in the model. So this must be taken into consideration.
  9. Read more here…

Basically, data mining is a key aspect of data analytics. Some even consider the former as essential to execute before the latter. While data analytics is the complete package and involves most components needed to examine a data set and extract valuable information, data mining focuses specifically on identifying hidden patterns.

That’s just the surface-level comparison though. The image above gives an overview of how the two differ.

One such difference is the presence of a hypothesis. Data analytics usually requires coming up with one, as it aims to find specific answers. Data mining, on the other hand, generally doesn’t need one to test or prove. The expected output are patterns or trends, which doesn’t require coming up with a statement or fact to test.

However, that doesn’t mean you mine data blindly. You still have a goal, whether it’s to come up with a recommender system or identify predictors of a certain dimension. Ultimately though, you strive to come up with data patterns or trends. For data analysis on the other hand, you’re expected to come up with valuable and actionable insights, usually in relation to a predetermined hypothesis. Read more here ….

The data science life cycle is not something well-defined like the software development life-cycle, and there is no ‘one-size-fits-all’ solution for data science projects. Every step in the life-cycle of a data science project depends on various data scientist skills and data science tools. The typical life-cycle of a data science project involves jumping back and forth among various interdependent science tasks using a variety of tools, techniques, programming, etc.

Thus, the data science life-cycle can include the following steps:

  1. Business requirement understanding.
  2. Data collection.
  3. Data cleaning.
  4. Data analysis.
  5. Modeling.
  6. Performance evaluation.
  7. Communicating with stakeholders.
  8. Deployment.
  9. Real-world testing.
  10. Business buy-in.
  11. Support and maintenance.

Looks neat, but here is the scheme to visualize how it is happening in reality:

Agile development processes, especially continuous delivery lends itself well to the data science project life-cycle. The early comparison helps the data science team to change approaches, refine hypotheses and even discard the project if the business case is nonviable or the benefits from the predictive models are not worth the effort to build it.

Read more here….

 

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Machine Learning Latest News

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Top 10 Machine Learning Algorithms

Source: Top 10 Machine Learning Algorithms for Data Scientist

In machine learning, there’s something called the “No Free Lunch” theorem. In a nutshell, it states that no one algorithm works best for every problem. It’s especially relevant for supervised learning. For example, you can’t say that neural networks are always better than decision trees or vice-versa. Furthermore, there are many factors at play, such as the size and structure of your dataset. As a result, you should try many different algorithms for your problem!

Top ML Algorithms

1. Linear Regression

Regression is a technique for numerical prediction. Additionally, regression is a statistical measure that attempts to determine the strength of the relationship between two variables. One is a dependent variable. Other is from a series of other changing variables which are our independent variables. Moreover, just like Classification is for predicting categorical labels, Regression is for predicting a continuous value. For example, we may wish to predict the salary of university graduates with 5 years of work experience. We use regression to determine how much specific factors or sectors influence the dependent variable.

Linear regression attempts to model the relationship between a scalar variable and explanatory variables by fitting a linear equation. For example, one might want to relate the weights of individuals to their heights using a linear regression model.

Additionally, this operator calculates a linear regression model. It uses the Akaike criterion for model selection. Furthermore, the Akaike information criterion is a measure of the relative goodness of a fit of a statistical model.

2. Logistic Regression

Logistic regression is a classification model. It uses input variables to predict a categorical outcome variable. The variable can take on one of a limited set of class values. A binomial logistic regression relates to two binary output categories. A multinomial logistic regression allows for more than two classes. Examples of logistic regression include classifying a binary condition as “healthy” / “not healthy”. Logistic regression applies the logistic sigmoid function to weighted input values to generate a prediction of the data class.

A logistic regression model estimates the probability of a dependent variable as a function of independent variables. The dependent variable is the output that we are trying to predict. The independent variables or explanatory variables are the factors that we feel could influence the output. Multiple regression refers to regression analysis with two or more independent variables. Multivariate regression, on the other hand, refers to regression analysis with two or more dependent variables.

3. Linear Discriminant Analysis

Logistic Regression is a classification algorithm traditionally for two-class classification problems. If you have more than two classes then the Linear Discriminant Analysis algorithm is the preferred linear classification technique.

The representation of LDA is pretty straight forward. It consists of statistical properties of your data, calculated for each class. For a single input variable this includes:

  1. The mean value for each class.
  2. The variance calculated across all classes.

We make predictions by calculating a discriminate value for each class. After that we make a prediction for the class with the largest value. The technique assumes that the data has a Gaussian distribution. Hence, it is a good idea to remove outliers from your data beforehand. It’s a simple and powerful method for classification predictive modelling problems.

4. Classification and Regression Trees

Prediction Trees are for predicting response or class YY from input X1, X2,…,XnX1,X2,…,Xn. If it is a continuous response it is a regression tree, if it is categorical, it is a classification tree. At each node of the tree, we check the value of one the input XiXi. Depending on the (binary) answer we continue to the left or to the right subbranch. When we reach a leaf we will find the prediction.

Contrary to linear or polynomial regression which are global models, trees try to partition the data space into small enough parts where we can apply a simple different model on each part. The non-leaf part of the tree is just the procedure to determine for each data xx what is the model we will use to classify it.

5. Naive Bayes

A Naive Bayes Classifier is a supervised machine-learning algorithm that uses the Bayes’ Theorem, which assumes that features are statistically independent. The theorem relies on the naive assumption that input variables are independent of each other, i.e. there is no way to know anything about other variables when given an additional variable. Regardless of this assumption, it has proven itself to be a classifier with good results.

Naive Bayes Classifiers rely on the Bayes’ Theorem, which is based on conditional probability or in simple terms, the likelihood that an event (A) will happen given that another event (B) has already happened. Essentially, the theorem allows a hypothesis to be updated each time new evidence is introduced. The equation below expresses Bayes’ Theorem in the language of probability:

Let’s explain what each of these terms means.

  • “P” is the symbol to denote probability.
  • P(A | B) = The probability of event A (hypothesis) occurring given that B (evidence) has occurred.
  • P(B | A) = The probability of the event B (evidence) occurring given that A (hypothesis) has occurred.
  • P(A) = The probability of event B (hypothesis) occurring.
  • P(B) = The probability of event A (evidence) occurring.

6. K-Nearest Neighbors

k-nearest neighbours (or k-NN for short) is a simple machine learning algorithm that categorizes an input by using its k nearest neighbours.

For example, suppose a k-NN algorithm has an input of data points of specific men and women’s weight and height, as plotted below. To determine the gender of an unknown input (green point), k-NN can look at the nearest k neighbours (suppose ) and will determine that the input’s gender is male. This method is a very simple and logical way of marking unknown inputs, with a high rate of success.

Also, we can k-NN in a variety of machine learning tasks; for example, in computer vision, k-NN can help identify handwritten letters and in gene expression analysis, the algorithm can determine which genes contribute to a certain characteristic. Overall, k-nearest neighbours provide a combination of simplicity and effectiveness that makes it an attractive algorithm to use for many machine learning tasks.

7. Learning Vector Quantization

A downside of K-Nearest Neighbors is that you need to hang on to your entire training dataset. The Learning Vector Quantization algorithm (or LVQ for short) is an artificial neural network algorithm that allows you to choose how many training instances to hang onto and learns exactly what those instances should look like.

Additionally, the representation for LVQ is a collection of codebook vectors. We select them randomly in the beginning and adapted to best summarize the training dataset over a number of iterations of the learning algorithm. After learned, the codebook vectors can make predictions just like K-Nearest Neighbors. Also, we find the most similar neighbour (best matching codebook vector) by calculating the distance between each codebook vector and the new data instance. The class value or (real value in the case of regression) for the best matching unit is then returned as the prediction. Moreover, you can get the best results if you rescale your data to have the same range, such as between 0 and 1.

If you discover that KNN gives good results on your dataset try using LVQ to reduce the memory requirements of storing the entire training dataset.

8. Bagging and Random Forest

A Random Forest consists of a collection or ensemble of simple tree predictors, each capable of producing a response when presented with a set of predictor values. For classification problems, this response takes the form of a class membership, which associates, or classifies, a set of independent predictor values with one of the categories present in the dependent variable. Alternatively, for regression problems, the tree response is an estimate of the dependent variable given the predictors.e

A Random Forest consists of an arbitrary number of simple trees, which determine the final outcome. For classification problems, the ensemble of simple trees votes for the most popular class. In the regression problem, we average responses to obtain an estimate of the dependent variable. Using tree ensembles can lead to significant improvement in prediction accuracy (i.e., better ability to predict new data cases).

9. SVM

A Support Vector Machine (SVM) is a supervised machine learning algorithm that can be employed for both classification and regression purposes. Also, SVMs have more common usage in classification problems and as such, this is what we will focus on in this post.

SVMs are based on the idea of finding a hyperplane that best divides a dataset into two classes, as shown in the image below.

Also, you can think of a hyperplane as a line that linearly separates and classifies a set of data.

Intuitively, the further from the hyperplane our data points lie, the more confident we are that they have been correctly classified. We, therefore, want our data points to be as far away from the hyperplane as possible, while still being on the correct side of it.

So when we add a new testing data , whatever side of the hyperplane it lands will decide the class that we assign to it.

The distance between the hyperplane and the nearest data point from either set is the margin. Furthermore, the goal is to choose a hyperplane with the greatest possible margin between the hyperplane and any point within the training set, giving a greater chance of correct classification of data.

But the data is rarely ever as clean as our simple example above. A dataset will often look more like the jumbled balls below which represent a linearly non-separable dataset.

10. Boosting and AdaBoost

Boosting is an ensemble technique that attempts to create a strong classifier from a number of weak classifiers. We do this by building a model from the training data, then creating a second model that attempts to correct the errors from the first model. We can add models until the training set is predicted perfectly or a maximum number of models are added.

AdaBoost was the first really successful boosting algorithm developed for binary classification. It is the best starting point for understanding boosting. Modern boosting methods build on AdaBoost, most notably stochastic gradient boosting machines.

AdaBoost is used with short decision trees. After the first tree is created, the performance of the tree on each training instance is used to weight how much attention the next tree that is created should pay attention to each training instance. Training data that is hard to predict is given more weight, whereas easy to predict instances are given less weight. Models are created sequentially one after the other, each updating the weights on the training instances that affect the learning performed by the next tree in the sequence. After all the trees are built, predictions are made for new data, and the performance of each tree is weighted by how accurate it was on training data.

Because so much attention is put on correcting mistakes by the algorithm it is important that you have clean data with outliers removed.

Summary

A typical question asked by a beginner, when facing a wide variety of machine learning algorithms, is “which algorithm should I use?” The answer to the question varies depending on many factors, including: (1) The size, quality, and nature of data; (2) The available computational time; (3) The urgency of the task; and (4) What you want to do with the data.

Even an experienced data scientist cannot tell which algorithm will perform the best before trying different algorithms. Although there are many other Machine Learning algorithms, these are the most popular ones. If you’re a newbie to Machine Learning, these would be a good starting point to learn.

Follow this link, if you are looking to learn Data Science Course Online!

Additionally, if you are having an interest in learning Data Science, Learn online Data Science Course to boost your career in Data Science.

Also, learn AWS Big Data Course click here, AWS Online Course

Furthermore, if you want to read more about data science, read this Data Science blogs

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The foundations of most algorithms lie in linear algebra, multivariable calculus, and optimization methods. Most algorithms use a sequence of combinations to estimate an objective function given a set of data, and the sequence order and included methods distinguish one algorithm from another. It’s helpful to learn enough math to read the development papers associated with key algorithms in the field, as many other methods (or one’s own innovations) include pieces of those algorithms. It’s like learning the language of machine learning. Once you are fluent in it, it’s pretty easy to modify algorithms as needed and create new ones likely to improve on a problem in a short period of time.

Matrix factorization: a simple, beautiful way to do dimensionality reduction —and dimensionality reduction is the essence of cognition. Recommender systems would be a big application of matrix factorization. Another application I’ve been using over the years (starting in 2010 with video data) is factorizing a matrix of pairwise mutual information (or pointwise mutual information, which is more common) between features, which can be used for feature extraction, computing word embeddings, computing label embeddings (that was the topic of a recent paper of mine [1]), etc.

Used in a convolutional settings, this acts as an excellent unsupervised feature extractor for images and videos. There’s one big issue though: it is fundamentally a shallow algorithm. Deep neural networks will quickly outperform it if any kind of supervision labels are available.

[1] [1607.05691] Information-theoretical label embeddings for large-scale image classification

Machine Learning Demos:

1- TensorFlow Demos

LipSync by YouTube

See how well you synchronize to the lyrics of the popular hit “Dance Monkey.” This in-browser experience uses the Facemesh model for estimating key points around the lips to score lip-syncing accuracy.Explore demo  View code  

Emoji Scavenger Hunt

Use your phone’s camera to identify emojis in the real world. Can you find all the emojis before time expires?Explore demo  View code  

Webcam Controller

Play Pac-Man using images trained in your browser.Explore demo  View code  

Teachable Machine

No coding required! Teach a machine to recognize images and play sounds.Explore demo  View code  

Move Mirror

Explore pictures in a fun new way, just by moving around.Explore demo  View code  

Performance RNN

Enjoy a real-time piano performance by a neural network.Explore demo  View code  

Node.js Pitch Prediction

Train a server-side model to classify baseball pitch types using Node.js.View code  

Visualize Model Training

See how to visualize in-browser training and model behaviour and training using tfjs-vis.Explore demo  View code  

Community demos

Get started with official templates and explore top picks from the community for inspiration.Glitch 

Check out community Glitches and make your own TensorFlow.js-powered projects.Explore Glitch  Codepen 

Fork boilerplate templates and check out working examples from the community.Explore CodePen  GitHub Community Projects 

See what the community has created and submitted to the TensorFlow.js gallery page.Explore GitHub  

https://cdpn.io/jasonmayes/fullcpgrid/QWbNeJdOpen in Editor

Real time body segmentation using TensorFlow.js

Load in a pre-trained Body-Pix model from the TensorFlow.js team so that you can locate all pixels in an image that are part of a body, and what part of the body they belong to. Clone this to make your own TensorFlow.js powered projects to recognize body parts in images from your webcam and more!

New Pen from Templatehttps://cdpn.io/jasonmayes/fullcpgrid/qBEJxggOpen in Editor

Multiple object detection using pre trained model in TensorFlow.js

This demo shows how we can use a pre made machine learning solution to recognize objects (yes, more than one at a time!) on any image you wish to present to it. Even better, not only do we know that the image contains an object, but we can also get the co-ordinates of the bounding box for each object it finds, which allows you to highlight the found object in the image.

For this demo we are loading a model using the ImageNet-SSD architecture, to recognize 90 common objects it has already been taught to find from the COCO dataset.

If what you want to recognize is in that list of things it knows about (for example a cat, dog, etc), this may be useful to you as is in your own projects, or just to experiment with Machine Learning in the browser and get familiar with the possibilities of machine learning.

If you are feeling particularly confident you can check out our GitHub documentation (https://github.com/tensorflow/tfjs-models/tree/master/coco-ssd) which goes into much more detail for customizing various parameters to tailor performance to your needs.

New Pen from Templatehttps://cdpn.io/jasonmayes/fullcpgrid/JjompwwOpen in Editor

Classifying images using a pre trained model in TensorFlow.js

This demo shows how we can use a pre made machine learning solution to classify images (aka a binary image classifier). It should be noted that this model works best when a single item is in the image at a time. Busy images may not work so well. You may want to try our demo for Multiple Object Detection (https://codepen.io/jasonmayes/pen/qBEJxgg) for that.

For this demo we are loading a model using the MobileNet architecture, to recognize 1000 common objects it has already been taught to find from the ImageNet data set (http://image-net.org/).

If what you want to recognize is in that list of things it knows about (for example a cat, dog, etc), this may be useful to you as is in your own projects, or just to experiment with Machine Learning in the browser and get familiar with the possibilities of machine learning.

Please note: This demo loads an easy to use JavaScript class made by the TensorFlow.js team to do the hardwork for you so no machine learning knowledge is needed to use it.

If you were looking to learn how to load in a TensorFlow.js saved model directly yourself then please see our tutorial on loading TensorFlow.js models directly.

If you want to train a system to recognize your own objects, using your own data, then check out our tutorials on “transfer learning”.

New Pen from TemplateOpen in Editor

Tensorflow.js Boilerplate

The hello world for TensorFlow.js 🙂 Absolute minimum needed to import into your website and simply prints the loaded TensorFlow.js version. From here we can do great things. Clone this to make your own TensorFlow.js powered projects or if you are following a tutorial that needs TensorFlow.js to work.

New Pen from Template

Examples

tfjs-examples provides small code examples that implement various ML tasks using TensorFlow.js.MNIST Digit Recognizer

Train a model to recognize handwritten digits from the MNIST database.Explore example  View code  Addition RNN

Train a model to learn addition from text examples.Explore example  View code  

TensorFlow.js Layers: Iris Demo

More TensorFlow examples

Top-paying Cloud certifications:

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  1. Google Certified Professional Cloud Architect — $175,761/year
  2. AWS Certified Solutions Architect – Associate — $149,446/year
  3. Azure/Microsoft Cloud Solution Architect – $141,748/yr
  4. Google Cloud Associate Engineer – $145,769/yr
  5. AWS Certified Cloud Practitioner — $131,465/year
  6. Microsoft Certified: Azure Fundamentals — $126,653/year
  7. Microsoft Certified: Azure Administrator Associate — $125,993/year

Complete overview of machine learning concepts seen in 27 data science and machine learning interviews:

Supervised Learning

Linear Regression

Logistic Regression

Naive Bayes

Support Vector Machines

Decision Trees

K-Nearest Neighbors

Test your knowledge

Machine Learning in Practice

Bias-Variance Tradeoff

How to Select a Model

How to Select Features

Regularizing Your Model

Ensembling: How to Combine Your Models

Evaluation Metrics

Unsupervised Learning

Market Basket Analysis

K-Means Clustering

Principal Components Analysis

Deep Learning

Feedforward Neural Networks

Grab Bag of Neural Network Practices

Convolutional Neural Networks

Recurrent Neural Networks

Test Your Knowledge

Feature Extraction

Best Subset Features Feature

Selection Examples

Adding Features Example
Activation Practice I
Activation Practice II
Activation Practice III
Weight Initialization
Batch vs. Stochastic

Recurrent Network Advantages

Alternatives Recurrent Units


Convolutional Application
Convolutional Layer Advantages

Are you interested in becoming an AWS Certified Machine Learning Specialist? If so, then this exam preparation blog is for you! The blog contains over 100 quiz and practice exam questions, as well as detailed answers. The questions are very similar to those you will encounter on the actual exam, so this is a great way to prepare. In addition, the blog also includes cheat sheets and illustrations to help you understand the concepts better.

Bring your own algorithm to an MLOps Pipeline: Architecture

AWS Certified machine Learning Specialty Exam Prep MLS-C01: AWS architecture diagram showing all services used and how they are connected
AWS Certified machine Learning Specialty Exam Prep MLS-C01
Bring your own algorithm to an MLOps Pipeline: Architecture
Bring your own algorithm to an MLOps Pipeline: Architecture
Bring your own algorithm to an MLOps Pipeline: Architecture

Code and Serve Your ML Model with AWS CodeBuild

What are some ways we can use machine learning and artificial intelligence for algorithmic trading in the stock market?

How do we know that the Top 3 Voice Recognition Devices like Siri Alexa and Ok Google are not spying on us?

What are some good datasets for Data Science and Machine Learning?

Machine Learning Engineer Interview Questions and Answers

  • Found a company asking for high school certificates for a Data Scientist role.
    by /u/xandie985 (Data Science) on March 27, 2024 at 10:20 pm

    ​ https://preview.redd.it/2qy68tawbyqc1.png?width=1322&format=png&auto=webp&s=2e9d875eb6fb7d11e14e9e1d7fa91180c6f67eb8 submitted by /u/xandie985 [link] [comments]

  • Causal inference question
    by /u/Amazing_Alarm6130 (Data Science) on March 27, 2024 at 9:47 pm

    I used DoWhy to create some synthetic data. The causal graph is shown below. Treatment is v0 and y is the outcome. True ATE is 10. I also used the DoWhy package to find ATE (propensity score matching) and I obtained ~10, which is great. For fun, I fitted a OLS model (y ~ W1 + W2 + v0 + Z1 + Z2) on the data and, surprisingly the beta for the treatment v0 is 10. I was expecting something different from 10, because of the confounders. What am I missing here? ​ https://preview.redd.it/ve6753p75yqc1.png?width=458&format=png&auto=webp&s=0935bbb15fba1dc63bdb3f8f445dca73fa2988e9 submitted by /u/Amazing_Alarm6130 [link] [comments]

  • Dumb question but do data scientists make an effort to automate there work?
    by /u/Marion_Shepard (Data Science) on March 27, 2024 at 8:37 pm

    Lowly BI person here -- just curious outside of maths, data modeling, and drinking scotch in the library, do data scientists make an effort to automate their work? Like are there tools or scripts you all are building to be more efficient or is it not really a part of the job? submitted by /u/Marion_Shepard [link] [comments]

  • Limited data, need help with analysis
    by /u/bernful (Data Science) on March 27, 2024 at 6:55 pm

    I work for a large chain grocer and I've been tasked with "Missed Opportunity." Missed Opportunity (MO) is defined as such: When a customer wants to buy an item, and the item IS stocked, but is not on the shelf. I.e. in most cases, this translates to the item is in the backrooms. But it could be the case that someone grabbed an item and did not return it to the right place. Now my goal is to look at what items (in the past couple of months) are experiencing the "most" MO, quantified by $ value or units. The limited amount of data I have is sales. I can tell you what time an item was sold, how many units, in what store it was sold, and the price. I do NOT have: anything related to inventory, even delivery dates. I also do NOT have a "true" dataset of actual MO being experienced. ​ Thus, how in the hell do I figure out my goal with this little data??? The only thing that I have been trying is to cluster stores (K-means) based off sales of a particular item, and if the store is underperforming in its cluster, then it could be somewhat assumed that it may be experiencing MO. However, this runs into its own problems and assumptions. So what other statistical methods, techniques, manipulations, etc. could possibly help me here? I feel like I need to get pretty creative submitted by /u/bernful [link] [comments]

  • [D] Thoughts on a blockchain based robot authorisation system
    by /u/d41_fpflabs (Machine Learning) on March 27, 2024 at 6:26 pm

    Robots intended to be used by the general public, with the ability to execute critical tasks must be governed by a trustless, transparent, auditable authorisation system. There are 3 main points of vulnerability for a robot deployed into the real world. Malicious intent from the robot Malicious intent from the robot manufacturer 3.Malicious intent from hackers A blockchain based authorisation system seems like the perfect solution. The blockchain authorisation control system will have 4 fundamental aspects: 1.Soul-bound NFTs Multi-Sig Roles Smart contract events Read the full proposed approach here: https://github.com/dev-diaries41/robo-auth What are you thoughts? submitted by /u/d41_fpflabs [link] [comments]

  • [D] Dataloading from external disk
    by /u/bkffadia (Machine Learning) on March 27, 2024 at 6:17 pm

    Hey there, I am training a deep lesrning model using a dataset of 400Go in an external SSD disk and I noticed that training is very slow, any tricks to make dataloading faster ? PS : I have to use the external disk submitted by /u/bkffadia [link] [comments]

  • [D] How do you measure performance of AI copilot/assistant?
    by /u/n2parko (Machine Learning) on March 27, 2024 at 5:38 pm

    Curious to hear from those that are building and deploying products with AI copilots. How are you tracking the interactions? And are you feeding the interaction back into the model for retraining? Put together a how-to to do this with an OS Copilot (Vercel AI SDK) and Segment and would love any feedback to improve the spec: https://segment.com/blog/instrumenting-user-insights-for-your-ai-copilot/ submitted by /u/n2parko [link] [comments]

  • Is it just me, or have there been a lot of data science job postings lately that require skills in data engineering?
    by /u/trafalgar28 (Data Science) on March 27, 2024 at 4:53 pm

    Not only with job postings, but I know a few individuals who work as data scientists at reputable companies, and often they are tasked with the responsibilities of a data engineer. I believe the issue stems from a lack of data literacy among companies and data managers. In terms of job postings, most of them require extensive experience in SQL, data cleaning, ETL, Pipelines and data quality-related tasks, which I believe fall within the realm of data engineering. I would like to hear your thoughts on this. Have any of you experienced something similar or perhaps dealt with it firsthand? submitted by /u/trafalgar28 [link] [comments]

  • [D] What is the state-of-the-art for 1D signal cleanup?
    by /u/XmintMusic (Machine Learning) on March 27, 2024 at 4:52 pm

    I have the following problem. Imagine I have a 'supervised' dataset of 1D curves with inputs and outputs, where the input is a modulated noisy signal and the output is the cleaned desired signal. Is there a consensus in the machine learning community on how to tackle this simple problem? Have you ever worked on anything similar? What algorithm did you end up using? Example: https://imgur.com/JYgkXEe submitted by /u/XmintMusic [link] [comments]

  • [D] State of the art TTS
    by /u/Zireaone (Machine Learning) on March 27, 2024 at 3:04 pm

    State of the art Tts question Hey! I'm currently working on a project and I'd like to implement speech using TTS, I tried many things and I can't seem to find something that fits my needs, I haven't worked on TTS for a while now so I was wondering if maybe they were newer technologies I could use. Here is what I'm looking for : I need to be be quite fast and without too many sound artifacts (I tried bark and while the possibility of manipulating emotion is quite remarkable the generated voice is full of artifacts and noise) It'd be a bonus if I could stream the audio and pipe it through other things, I'd like to apply an RVC Model on top of it (live) Another 'nice to have' is to have some controls over the emotions or tone of the voice. I tried these so far (either myself or through demos) : TORTOISETTS and EDGETTS seem to have a nice voice quality but are relatively monotone. Bark as I said is very good at emotions and controls but lots of artifacts in the voice, if I have time I'd try to apply postprocessing but idk to what extent it can help OpenAI models don't have much emotions IMO Same as eleven labs I used Uber duck in the past but it seems a lot of fun functionalities disappeared. If you have any advice, suggestion or if you think I should try somethings further feel free to reply! I also want to thanks everyone in advance! Have a nice day! submitted by /u/Zireaone [link] [comments]

  • [D] Data cleaning for classification model
    by /u/fardin__khan (Machine Learning) on March 27, 2024 at 2:42 pm

    Currently working on a classification model, which entails data cleaning. We've got 8000 images categorized into 3 classes. After removing duplicates and corrupted images, what else should we consider? submitted by /u/fardin__khan [link] [comments]

  • [D] Seeking guidance/advice
    by /u/qheeeee (Machine Learning) on March 27, 2024 at 2:14 pm

    Hi, I've finished Andrew Ng's course on Coursera. I think I've got the basics. I've started learning ML for my master's thesis. I want to develop a method to estimate scope 3 emissions. I studied business and I do not have any python background except for a 6-month data analytics bootcamp. I've got the data needed for my thesis, but when I try to work on it, I'm not sure what I'm doing, and ofc a sh*t ton of bugs and errors. Do I need to just keep trying to push through and learn through the experience by working on my thesis or do I need to study more? I've been considering to by a book <\Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow> by Aurelien Geron. Any guidance/recommendation would be much appreciated! submitted by /u/qheeeee [link] [comments]

  • [P] Insta Face Swap
    by /u/abdullahozmntr (Machine Learning) on March 27, 2024 at 2:03 pm

    ComfyUI node repo: https://github.com/abdozmantar/ComfyUI-InstaSwap Standalone repo: https://github.com/abdozmantar/Standalone-InstaSwap ​ ​ https://i.redd.it/9d4ti20fvvqc1.gif submitted by /u/abdullahozmntr [link] [comments]

  • [D] Seeking Advice
    by /u/MD24IB (Machine Learning) on March 27, 2024 at 1:45 pm

    I'm currently pursuing my undergraduate degree in robotics engineering and have been immersing myself in concepts related to machine learning, deep learning, and computer vision, both modern and traditional. With strong programming skills and a habit of regularly reading research papers, I'm eager to understand the job landscape in my field and pursue a Phd. Are there ample opportunities available? What can I expect in terms of salaries and future prospects? Additionally, I'm curious about the comparative job market between natural language processing (NLP) and computer vision. Given my background and interests, what areas or skills should I focus on learning to enhance my career prospects? Thanks in advance for your time and advice. submitted by /u/MD24IB [link] [comments]

  • UPDATE #3: I built an app to make my job search a little more sane, and I thought others might like it too! No ads, no recruiter spam, etc.
    by /u/eipi-10 (Data Science) on March 27, 2024 at 1:41 pm

    Hey again everyone! ​ Checking back in with more updates on Zen because of how enthusiastic the community has been about it! We've done a lot of work the past two months or so since I last posted, but first I'll drop a couple of the most important things / highlights about the app here: ​ Zen is still a candidate / seeker-first job board. This means we have no ads, we have no promoted jobs from companies who are paying us, we have no recruiters, etc. The whole point of Zen is to help you find jobs quickly at companies you're interested in without any headaches. On that point, we'll send you emails notifying you when companies you care about post new jobs that match your preferences, so you don't need to continuously check their job boards. ​ In the past two months, we've made some major changes! Many of them are discussed in the changelog: We've continued adding postings and companies, so you can now explore over 170k open jobs at >6,200 companies We've continued to completely overhaul the UX of the app We've added some new preference filters to help you filter for relevant jobs better We've launched a premium tier. The reason for this was as we've grown (largely thanks to all of your support!) our costs have continued to go up significantly, and we want to be able to keep providing an ad-free, spam-free, promotion-free service to all of you without making any compromises. We're launching on ProductHunt today! You can check out our launch here ​ I started building Zen when I was on the job hunt and realized it was harder than it should've been to just get notifications when a company I was interested in posted a job that was relevant to me. And we hope that this goal -- to cut out all the noise and make it easier for you to find great matches -- is valuable for everyone here 🙂 Here are the original posts: https://www.reddit.com/r/datascience/comments/1ad5lxa/update_2_i_built_an_app_to_make_my_job_search_a/ https://www.reddit.com/r/datascience/comments/183562x/update_i_built_an_app_to_make_my_job_search_a/ https://www.reddit.com/r/datascience/comments/17s5fyq/i_built_an_app_to_make_my_job_search_a_little/ ​ And here's one more link to the app submitted by /u/eipi-10 [link] [comments]

  • [N] Introducing DBRX: A New Standard for Open LLM
    by /u/artificial_intelect (Machine Learning) on March 27, 2024 at 1:35 pm

    https://x.com/vitaliychiley/status/1772958872891752868?s=20 Shill disclaimer: I was the pretraining lead for the project DBRX deets: 16 Experts (12B params per single expert; top_k=4 routing) 36B active params (132B total params) trained for 12T tokens 32k sequence length training submitted by /u/artificial_intelect [link] [comments]

  • [D] Seeking Advice: Transitioning to Low-Level Implementations in AIoT Systems - Where to Start?
    by /u/MaTwickenham (Machine Learning) on March 27, 2024 at 1:20 pm

    Hello everyone, I'm a prospective graduate student who will be starting my studies in September this year, specializing in AIoT (Artificial Intelligence of Things) Systems. Recently, I've been reading papers from journals like INFOCOM and SIGCOMM, and I've noticed that they mostly focus on relatively low-level aspects of operating systems, including GPU/CPU scheduling, optimization of deep learning model inference, operator optimization, cross-platform migration, and deployment. I find it challenging to grasp the implementation details of these works at the code level. When I looked at the implementations of these works uploaded on GitHub, I found it relatively difficult to understand. My primary programming languages are Java and Python. During my undergraduate studies, I gained proficiency in implementing engineering projects and ideas using Python, especially in the fields of deep learning and machine learning. However, I lack experience and familiarity with C/C++ (many of the aforementioned works are based on C/C++). Therefore, I would like to ask for advice from senior professionals and friends on which areas of knowledge I should focus on. Do I need to learn CUDA programming, operating system programming, or other directions? Any recommended learning paths would be greatly appreciated. PS: Recently, I have started studying the MIT 6.S081 Operating System Engineering course. Thank you all sincerely for your advice. submitted by /u/MaTwickenham [link] [comments]

  • [P] Run AI & ML workflows locally from your Mac desktop
    by /u/creatorai (Machine Learning) on March 27, 2024 at 1:08 pm

    Hi all - I wanted to share an app I’ve been working on with a small team over the past year that I thought this community would be interested in. Odyssey is a completely native Mac app for creating remarkable art, getting work done, and automating repetitive tasks with the power of AI and machine learning models. We just made a major feature update and added the ability to create your own Widgets. Odyssey Widgets are fully interactive mini applications that live in their own windows or panels and are driven by a workflow. This means you can take a workflow you create with Odyssey and add it directly to your desktop. So, as an example, you could generate an image, chat with locally run chatbot, run bulk image processing, etc. straight from your desktop without even opening the Odyssey app. Widgets can be built with Odyssey and triggered from the Odyssey logo in your Mac’s menu. https://i.redd.it/8s9s6i0clvqc1.gif We're in public beta but here's a full list of everything Odyssey supports: Image generation and processing Run Stable Diffusion 1.5, SDXL, SDXL Lightning, and SDXL Turbo locally or connect your Stable Diffusion API key Add custom models & LoRAs ControlNet support including canny edges, pose detection, depth estimation, and QR Code Monster Inpainting and outpainting Super resolution models (Best Buddy GAN, Ultrasharp 4x, Remacri, and ESRGAN) Multiple image segmentation models Erase objects Dozens of image processing nodes including aspect ratio, resizing, and extracting dominant colors Custom image transitions for powerful slideshows Large language models and math equations Run Llama2 locally or connect your ChatGPT API key Supports both chatbot mode and instructions mode Solver node for word problems and math nodes for complex equations Lots of updates coming here in the next few weeks Automation and batch workflows Batch image and text nodes support hundreds of images and lines of text at once Remove backgrounds, upscale, change aspect ratios, and run dozens of image processors in bulk Private, customizable, and shareable No images, chats, or inputs are stored or accessible by the Odyssey team Completely private and secure. The only tracking is anonymized usage data to help us improve Odyssey Process your own data entirely locally No internet connection required to run local models Use your own API keys for ChatGPT and Stable Diffusion Easily save and share custom workflows What’s coming soon: Custom LLMs & more text processing nodes - we are adding support for bringing in custom LLMs, document uploads, and more Batch text and workflow automation - we are building in document upload, batch text support, and an integration with Apple shortcuts Plug-in support - we are opening up the Odyssey to 3P developers. If you’re interested, please reach out - would love to learn more from you as we work on building this out Feel free to reach out to [john@odysseyapp.io](mailto:john@odysseyapp.io) if you have any questions or feedback. submitted by /u/creatorai [link] [comments]

  • [P] Hybrid-Net: Real-time audio source separation, generate lyrics, chords, beat.
    by /u/CheekProfessional146 (Machine Learning) on March 27, 2024 at 12:11 pm

    Project: https://github.com/DoMusic/Hybrid-Net A transformer-based hybrid multimodal model, various transformer models address different problems in the field of music information retrieval, these models generate corresponding information dependencies that mutually influence each other. An AI-powered multimodal project focused on music, generate chords, beats, lyrics, melody, and tabs for any song. submitted by /u/CheekProfessional146 [link] [comments]

  • [P] Visualize RAG Data
    by /u/DocBrownMS (Machine Learning) on March 27, 2024 at 10:29 am

    Hey all, I've recently published a tutorial at Towards Data Science that explores a somewhat overlooked aspect of Retrieval-Augmented Generation (RAG) systems: the visualization of documents and questions in the embedding space: https://towardsdatascience.com/visualize-your-rag-data-evaluate-your-retrieval-augmented-generation-system-with-ragas-fc2486308557 While much of the focus in RAG discussions tends to be on the algorithms and data processing, I believe that visualization can help to explore the data and to gain insights into problematic subgroups within the data. This might be interesting for some of you, although I'm aware that not everyone is keen on this kind of visualization. I believe it can add a unique dimension to understanding RAG systems. submitted by /u/DocBrownMS [link] [comments]

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