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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

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.
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  • Current AI technology can boost business productivity by up to 40%

AWS Machine Learning Certification Specialty Exam Prep for iOs Android Windows10/11

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

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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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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
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What does a Professional Machine Learning Engineer do?

A 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)


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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

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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?

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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


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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

 

 
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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.

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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

 

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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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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
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What does a Professional Machine Learning Engineer do?

A 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.

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A 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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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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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.

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A 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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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:

What is best cloud solution for deep learning?

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 data? BigQuery 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

What are list of machine learning classification techniques?

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?

 
https://jmlr.org/papers/v15/delgado14a.html
 
 

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!

What is the best way to know which machine learning algorithm has a better probability to accurately or more precisely classify a dataset, before applying it?

These basic questions should help:

1. Is the classification going to be supervised or unsupervised? Several well defined techniques likes SVM (Support Vector Machines), trained neural net,etc. are applicable for supervised classification. For unsupervised classification, GMMs (Gaussian Mixture Models), HMMs (Hidden Markov models) with Baye’s techniques could be used. (Several other techniques could of course be used as well)

2.How much training data do you have in case it is supervised ? A small number of training data may yield discouraging classification accuracy even if the chosen classifier is the most suitable one for the problem. In such a case, try to obtain more number of samples. There’s also generally a correlation (for practical purposes at least) between the feature dimensionality and the number of samples for given technique. For example, while using SVM, the linear kernel tends to yield better results when the number of training samples are less than or equal to or only slightly more than the number of feature dimensions as compared to RBF or any other kernel.

3. If the feature vector dimensionality is small enough (1/2/3 -D) then it makes sense to plot and visually inspect if techniques like clustering could be more useful. With very high number of feature dimensions, methods like clustering are generally not advisable(Refer : “The Curse Of Dimensionality”).

4. Are you doing classification in real time ? Some techniques ,e.g. “Template Match” in image classification may lead to a higher number of errors but is generally faster than most other techniques if the number of templates to be evaluated are not excessively high.

5. Depending upon the problem domain, you can decide if you can choose the underlying model in such a way that it can use certain temporal/spatial correlations that may be inherent in the data. For example, HMMs use the temporal continuity of speech samples for enhancing classification results in speech recognition problems.

Another point, slightly off the topic perhaps, but the classification performance is as much a function of choosing the correct feature vectors, the pre-processing of the feature vectors as much as the classifier itself. It’s generally a good idea to give reserve some initial part of the project to try out various classifiers on the same data-set. It may at least help you reject the ones which are highly inaccurate.

 
 
 
 
 
 
 
 
 
 

What skills does a data scientist need to learn in order to put machine learning models in production?

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 …..

What are some mistakes data scientists make when building machine learning models?

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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What is the difference between data analytics and data mining?

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 ….

What is the life cycle of a data science project?

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 Q&A -Part II:

 
 
 
 
 
 
 
 
 
 
What skills does a data scientist need to learn in order to put machine learning models in production?

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 …..
What are some mistakes data scientists make when building machine learning models?

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…

What is the difference between data analytics and data mining?

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 ….

What is the life cycle of a data science project?

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. 

 

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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:

What is best cloud solution for deep learning?

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 data? BigQuery 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

What are list of machine learning classification techniques?

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?

 
https://jmlr.org/papers/v15/delgado14a.html
 
 

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!

What is the best way to know which machine learning algorithm has a better probability to accurately or more precisely classify a dataset, before applying it?

These basic questions should help:

1. Is the classification going to be supervised or unsupervised? Several well defined techniques likes SVM (Support Vector Machines), trained neural net,etc. are applicable for supervised classification. For unsupervised classification, GMMs (Gaussian Mixture Models), HMMs (Hidden Markov models) with Baye’s techniques could be used. (Several other techniques could of course be used as well)

2.How much training data do you have in case it is supervised ? A small number of training data may yield discouraging classification accuracy even if the chosen classifier is the most suitable one for the problem. In such a case, try to obtain more number of samples. There’s also generally a correlation (for practical purposes at least) between the feature dimensionality and the number of samples for given technique. For example, while using SVM, the linear kernel tends to yield better results when the number of training samples are less than or equal to or only slightly more than the number of feature dimensions as compared to RBF or any other kernel.

3. If the feature vector dimensionality is small enough (1/2/3 -D) then it makes sense to plot and visually inspect if techniques like clustering could be more useful. With very high number of feature dimensions, methods like clustering are generally not advisable(Refer : “The Curse Of Dimensionality”).

4. Are you doing classification in real time ? Some techniques ,e.g. “Template Match” in image classification may lead to a higher number of errors but is generally faster than most other techniques if the number of templates to be evaluated are not excessively high.

5. Depending upon the problem domain, you can decide if you can choose the underlying model in such a way that it can use certain temporal/spatial correlations that may be inherent in the data. For example, HMMs use the temporal continuity of speech samples for enhancing classification results in speech recognition problems.

Another point, slightly off the topic perhaps, but the classification performance is as much a function of choosing the correct feature vectors, the pre-processing of the feature vectors as much as the classifier itself. It’s generally a good idea to give reserve some initial part of the project to try out various classifiers on the same data-set. It may at least help you reject the ones which are highly inaccurate.

 
 
 
 
 
 
 
 
 
 

What skills does a data scientist need to learn in order to put machine learning models in production?

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 …..

What are some mistakes data scientists make when building machine learning models?

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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What is the difference between data analytics and data mining?

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 ….

What is the life cycle of a data science project?

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 Q&A -Part II:

 
 
 
 
 
 
 
 
 
 
What skills does a data scientist need to learn in order to put machine learning models in production?

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 …..
What are some mistakes data scientists make when building machine learning models?

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…

What is the difference between data analytics and data mining?

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 ….

What is the life cycle of a data science project?

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. 

 

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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:

What is best cloud solution for deep learning?

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 data? BigQuery 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

What are list of machine learning classification techniques?

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?

 
https://jmlr.org/papers/v15/delgado14a.html
 
 

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!

What is the best way to know which machine learning algorithm has a better probability to accurately or more precisely classify a dataset, before applying it?

These basic questions should help:

1. Is the classification going to be supervised or unsupervised? Several well defined techniques likes SVM (Support Vector Machines), trained neural net,etc. are applicable for supervised classification. For unsupervised classification, GMMs (Gaussian Mixture Models), HMMs (Hidden Markov models) with Baye’s techniques could be used. (Several other techniques could of course be used as well)

2.How much training data do you have in case it is supervised ? A small number of training data may yield discouraging classification accuracy even if the chosen classifier is the most suitable one for the problem. In such a case, try to obtain more number of samples. There’s also generally a correlation (for practical purposes at least) between the feature dimensionality and the number of samples for given technique. For example, while using SVM, the linear kernel tends to yield better results when the number of training samples are less than or equal to or only slightly more than the number of feature dimensions as compared to RBF or any other kernel.

3. If the feature vector dimensionality is small enough (1/2/3 -D) then it makes sense to plot and visually inspect if techniques like clustering could be more useful. With very high number of feature dimensions, methods like clustering are generally not advisable(Refer : “The Curse Of Dimensionality”).

4. Are you doing classification in real time ? Some techniques ,e.g. “Template Match” in image classification may lead to a higher number of errors but is generally faster than most other techniques if the number of templates to be evaluated are not excessively high.

5. Depending upon the problem domain, you can decide if you can choose the underlying model in such a way that it can use certain temporal/spatial correlations that may be inherent in the data. For example, HMMs use the temporal continuity of speech samples for enhancing classification results in speech recognition problems.

Another point, slightly off the topic perhaps, but the classification performance is as much a function of choosing the correct feature vectors, the pre-processing of the feature vectors as much as the classifier itself. It’s generally a good idea to give reserve some initial part of the project to try out various classifiers on the same data-set. It may at least help you reject the ones which are highly inaccurate.

 
 
 
 
 
 
 
 
 
 

What skills does a data scientist need to learn in order to put machine learning models in production?

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 …..

What are some mistakes data scientists make when building machine learning models?

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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What is the difference between data analytics and data mining?

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 ….

What is the life cycle of a data science project?

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 Q&A -Part II:

 
 
 
 
 
 
 
 
 
 
What skills does a data scientist need to learn in order to put machine learning models in production?

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 …..
What are some mistakes data scientists make when building machine learning models?

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…

What is the difference between data analytics and data mining?

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 ….

What is the life cycle of a data science project?

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. 

 

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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:

What is best cloud solution for deep learning?

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 data? BigQuery 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

What are list of machine learning classification techniques?

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?

 
https://jmlr.org/papers/v15/delgado14a.html
 
 

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!

What is the best way to know which machine learning algorithm has a better probability to accurately or more precisely classify a dataset, before applying it?

These basic questions should help:

1. Is the classification going to be supervised or unsupervised? Several well defined techniques likes SVM (Support Vector Machines), trained neural net,etc. are applicable for supervised classification. For unsupervised classification, GMMs (Gaussian Mixture Models), HMMs (Hidden Markov models) with Baye’s techniques could be used. (Several other techniques could of course be used as well)

2.How much training data do you have in case it is supervised ? A small number of training data may yield discouraging classification accuracy even if the chosen classifier is the most suitable one for the problem. In such a case, try to obtain more number of samples. There’s also generally a correlation (for practical purposes at least) between the feature dimensionality and the number of samples for given technique. For example, while using SVM, the linear kernel tends to yield better results when the number of training samples are less than or equal to or only slightly more than the number of feature dimensions as compared to RBF or any other kernel.

3. If the feature vector dimensionality is small enough (1/2/3 -D) then it makes sense to plot and visually inspect if techniques like clustering could be more useful. With very high number of feature dimensions, methods like clustering are generally not advisable(Refer : “The Curse Of Dimensionality”).

4. Are you doing classification in real time ? Some techniques ,e.g. “Template Match” in image classification may lead to a higher number of errors but is generally faster than most other techniques if the number of templates to be evaluated are not excessively high.

5. Depending upon the problem domain, you can decide if you can choose the underlying model in such a way that it can use certain temporal/spatial correlations that may be inherent in the data. For example, HMMs use the temporal continuity of speech samples for enhancing classification results in speech recognition problems.

Another point, slightly off the topic perhaps, but the classification performance is as much a function of choosing the correct feature vectors, the pre-processing of the feature vectors as much as the classifier itself. It’s generally a good idea to give reserve some initial part of the project to try out various classifiers on the same data-set. It may at least help you reject the ones which are highly inaccurate.

 
 
 
 
 
 
 
 
 
 

What skills does a data scientist need to learn in order to put machine learning models in production?

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 …..

What are some mistakes data scientists make when building machine learning models?

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]
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What is the difference between data analytics and data mining?

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 ….

What is the life cycle of a data science project?

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 Q&A -Part II:

 
 
 
 
 
 
 
 
 
 
What skills does a data scientist need to learn in order to put machine learning models in production?

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 …..
What are some mistakes data scientists make when building machine learning models?

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…

What is the difference between data analytics and data mining?

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 ….

What is the life cycle of a data science project?

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

What are the simplest examples of machine learning algorithms?

Originally Answered: What are the top algorithms for machine learning?

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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What are the basic elements of a machine learning algorithm?

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.

What is your favorite machine learning algorithm?

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

  • [D] Are neural networks outdated?
    by /u/MonkeyD-Lucy (Machine Learning) on March 26, 2025 at 3:18 pm

    I’m a junior data science student studying ml. For a semester project, we were told to use surface level models like Trees and such. Well for my dataset, it had a lot of underlying relationships that I thought a Neural Network would be better suited for. I learned through YouTube, Stack Overflow, etc. I told my prof about it only for her to say they’re outdated and I thought “a core foundation of deep learning is outdated?” Granted I did use KerasTuner cause my dataset only has about 5000 observations. She recommended that I switch to PyTorch, but went on to say how the industry is focusing on MLPs, LLMs, and vision models. So I wanted to get takes from professionals in the field to understand if this true. A discussion on the topic and, in hindsight, key takeaways you’d tell your college self if you could. submitted by /u/MonkeyD-Lucy [link] [comments]

  • [D] Using Pytorch GradScaler results in NaN weights
    by /u/ripototo (Machine Learning) on March 26, 2025 at 3:02 pm

    I created a pro-gan Implementation, following this repo. I trained on my data and sometimes I get NANValues. I used a random seed and got to the training step just before the nan values appear for the first time. Here is the code gen,critic,opt_gen,opt_critic= load_checkpoint(gen,critic,opt_gen,opt_critic) # load the weights just before the nan values fake = gen(noise, alpha, step) # get the fake image critic_real = critic(real, alpha, step) # loss of the critic on the real images critic_fake = critic(fake.detach(), alpha, step) # loss of the critic on the fake gp = gradient_penalty (critic, real, fake, alpha, step) # gradient penalty loss_critic = ( -(torch.mean(critic_real) - torch.mean(critic_fake)) + LAMBDA_GP * gp + (0.001 * torch.mean(critic_real ** 2)) ) # the loss is the sumation of the above plus a regularisation print(loss_critic) # the loss in NOT NAN(around 28 cause gp has random in it) print(critic_real.mean().item(),critic_fake.mean().item(),gp.item(),torch.mean(critic_real ** 2).item()) # print all the loss calues seperately, non of them are NAN # standard opt_critic.zero_grad() scaler_critic.scale(loss_critic).backward() scaler_critic.step(opt_critic) scaler_critic.update() # do the same, but this time all the components of the loss are NAN fake = gen(noise, alpha, step) critic_real = critic(real, alpha, step) critic_fake = critic(fake.detach(), alpha, step) gp = gradient_penalty (critic, real, fake, alpha, step) loss_critic = ( -(torch.mean(critic_real) - torch.mean(critic_fake)) + LAMBDA_GP * gp + (0.001 * torch.mean(critic_real ** 2)) ) print(loss_critic) print(critic_real.mean().item(),critic_fake.mean().item(),gp.item(),torch.mean(critic_real ** 2).item()) I tried it with the standard backward and step and i get fine values. loss_critic.backward() opt_critic.step() I also tried to modify the loss function, keep only one of the components, but I still get nan weights. submitted by /u/ripototo [link] [comments]

  • [D] Figuring out how to run simulations using Bayesian Belief Networks
    by /u/Global-State-4271 (Machine Learning) on March 26, 2025 at 2:28 pm

    Hey all, I want to run simulations using Bayesian Belief Networks for some decision making, i am new to BBN , do you all have any suggestions or resources that might be helpful Also to add , i want to kind of recreate Bayesian Lab, a paid software submitted by /u/Global-State-4271 [link] [comments]

  • [P] Volga - Real-Time Data Processing Engine for AI/ML
    by /u/saws_baws_228 (Machine Learning) on March 26, 2025 at 11:57 am

    Hi all, wanted to share the project I've been working on: Volga - real-time data processing/feature calculation engine tailored for modern AI/ML systems. GitHub - https://github.com/volga-project/volga Blog - https://volgaai.substack.com/ Roadmap - https://github.com/volga-project/volga/issues/69 What My Project Does Volga allows you to create scalable real-time data processing/ML feature calculation pipelines (which can also be executed in offline mode with the same code) without setting up/maintaining complex infra (Flink/Spark with custom data models/data services) or relying on 3rd party systems (data/feature platforms like Tecton.ai, Fennel.ai, Chalk.ai - if you are in ML space you may have heard about those). Volga, at it's core, consists of two main parts: Streaming Engine which is a (soon to be fully functional) alternative to Flink/Spark Streaming with Python-native runtime and Rust for performance-critical parts (called the Push Part). On-Demand Compute Layer (the Pull Part): a pool of workers to execute arbitrary user-defined logic (which can be chained in a Directed Acyclic Graphs) at request time in sync with streaming engine (which is a common use case for AI/ML systems, e.g. feature calculation/serving for model inference) Volga also provides unified data models with compile-time schema-validation and an API stitching both systems together to build modular real-time/offline general data pipelines or AI/ML features. Features Python-native streaming engine backed by Rust that scales to millions of messages per-second with milliseconds-scale latency (benchmark running Volga on EKS). On-Demand Compute Layer to perform arbitrary DAGs of request time/inference time calculations in sync with streaming engine (brief high-level architecture overview). Entity API to build standardized data models with compile-time schema validation, Pandas-like operators like transform, filter, join, groupby/aggregate, drop, etc. to build modular data pipelines or AI/ML features with consistent online/offline semantics. Built on top of Ray - Easily integrates with Ray ecosystem, runs on Kubernetes and local machines, provides a homogeneous platform with no heavy dependencies on multiple JVM-based systems. If you already have Ray set up you get the streaming infrastructure for free - no need to spin up Flink/Spark. Configurable data connectors to read/write data from/to any third party system. Quick Example Define data models via @entity decorator ``` from volga.api.entity import Entity, entity, field @entity class User: user_id: str = field(key=True) registered_at: datetime.datetime = field(timestamp=True) name: str @entity class Order: buyer_id: str = field(key=True) product_id: str = field(key=True) product_type: str purchased_at: datetime.datetime = field(timestamp=True) product_price: float @entity class OnSaleUserSpentInfo: user_id: str = field(key=True) timestamp: datetime.datetime = field(timestamp=True) avg_spent_7d: float num_purchases_1h: int - Define streaming/batch pipelines via@sourceand@pipeline. from volga.api.pipeline import pipeline from volga.api.source import Connector, MockOnlineConnector, source, MockOfflineConnector users = [...] # sample User entities orders = [...] # sample Order entities @source(User) def usersource() -> Connector: return MockOfflineConnector.with_items([user.dict_ for user in users]) @source(Order) def ordersource(online: bool = True) -> Connector: # this will generate appropriate connector based on param we pass during job graph compilation if online: return MockOnlineConnector.with_periodic_items([order.dict_ for order in orders], periods=purchase_event_delays_s) else: return MockOfflineConnector.with_items([order.dict_ for order in orders]) @pipeline(dependencies=['user_source', 'order_source'], output=OnSaleUserSpentInfo) def user_spent_pipeline(users: Entity, orders: Entity) -> Entity: on_sale_purchases = orders.filter(lambda x: x['product_type'] == 'ON_SALE') per_user = on_sale_purchases.join( users, left_on=['buyer_id'], right_on=['user_id'], how='left' ) return per_user.group_by(keys=['buyer_id']).aggregate([ Avg(on='product_price', window='7d', into='avg_spent_7d'), Count(window='1h', into='num_purchases_1h'), ]).rename(columns={ 'purchased_at': 'timestamp', 'buyer_id': 'user_id' }) - Run offline (batch) materialization from volga.client.client import Client from volga.api.feature import FeatureRepository client = Client() pipeline_connector = InMemoryActorPipelineDataConnector(batch=False) # store data in-memory, can be any other user-defined connector, e.g. Redis/Cassandra/S3 Note that offline materialization only works for pipeline features at the moment, so offline data points you get will match event time, not request time client.materialize( features=[FeatureRepository.get_feature('user_spent_pipeline')], pipeline_data_connector=InMemoryActorPipelineDataConnector(batch=False), _async=False, params={'global': {'online': False}} ) Get results from storage. This will be specific to what db you use keys = [{'user_id': user.user_id} for user in users] we user in-memory Ray actor offline_res_raw = ray.get(cache_actor.get_range.remote(feature_name='user_spent_pipeline', keys=keys, start=None, end=None, with_timestamps=False)) offline_res_flattened = [item for items in offline_res_raw for item in items] offline_res_flattened.sort(key=lambda x: x['timestamp']) offline_df = pd.DataFrame(offline_res_flattened) pprint(offline_df) ... user_id timestamp avg_spent_7d num_purchases_1h 0 0 2025-03-22 13:54:43.335568 100.0 1 1 1 2025-03-22 13:54:44.335568 100.0 1 2 2 2025-03-22 13:54:45.335568 100.0 1 3 3 2025-03-22 13:54:46.335568 100.0 1 4 4 2025-03-22 13:54:47.335568 100.0 1 .. ... ... ... ... 796 96 2025-03-22 14:07:59.335568 100.0 8 797 97 2025-03-22 14:08:00.335568 100.0 8 798 98 2025-03-22 14:08:01.335568 100.0 8 799 99 2025-03-22 14:08:02.335568 100.0 8 800 0 2025-03-22 14:08:03.335568 100.0 9 - For real-time feature serving/calculation, define result entity and on-demand feature from volga.api.on_demand import on_demand @entity class UserStats: user_id: str = field(key=True) timestamp: datetime.datetime = field(timestamp=True) total_spent: float purchase_count: int @on_demand(dependencies=[( 'user_spent_pipeline', # name of dependency, matches positional argument in function 'latest' # name of the query defined in OnDemandDataConnector - how we access dependant data (e.g. latest, last_n, average, etc.). )]) def user_stats(spent_info: OnSaleUserSpentInfo) -> UserStats: # logic to execute at request time return UserStats( user_id=spent_info.user_id, timestamp=spent_info.timestamp, total_spent=spent_info.avg_spent_7d * spent_info.num_purchases_1h, purchase_count=spent_info.num_purchases_1h ) - Run online/streaming materialization job and query results run online materialization client.materialize( features=[FeatureRepository.get_feature('user_spent_pipeline')], pipeline_data_connector=pipeline_connector, job_config=DEFAULT_STREAMING_JOB_CONFIG, scaling_config={}, _async=True, params={'global': {'online': True}} ) query features client = OnDemandClient(DEFAULT_ON_DEMAND_CLIENT_URL) user_ids = [...] # user ids you want to query while True: request = OnDemandRequest( target_features=['user_stats'], feature_keys={ 'user_stats': [ {'user_id': user_id} for user_id in user_ids ] }, query_args={ 'user_stats': {}, # empty for 'latest', can be time range if we have 'last_n' query or any other query/params configuration defined in data connector } ) response = await self.client.request(request) for user_id, user_stats_raw in zip(user_ids, response.results['user_stats']): user_stats = UserStats(**user_stats_raw[0]) pprint(f'New feature: {user_stats.__dict__}') ... ("New feature: {'user_id': '98', 'timestamp': '2025-03-22T10:04:54.685096', " "'total_spent': 400.0, 'purchase_count': 4}") ("New feature: {'user_id': '99', 'timestamp': '2025-03-22T10:04:55.685096', " "'total_spent': 400.0, 'purchase_count': 4}") ("New feature: {'user_id': '0', 'timestamp': '2025-03-22T10:04:56.685096', " "'total_spent': 500.0, 'purchase_count': 5}") ("New feature: {'user_id': '1', 'timestamp': '2025-03-22T10:04:57.685096', " "'total_spent': 500.0, 'purchase_count': 5}") ("New feature: {'user_id': '2', 'timestamp': '2025-03-22T10:04:58.685096', " "'total_spent': 500.0, 'purchase_count': 5}") ``` Target Audience The project is meant for data engineers, AI/ML engineers, MLOps/AIOps engineers who want to have general Python-based streaming pipelines or introduce real-time ML capabilities to their project (specifically in feature engineering domain) and want to avoid setting up/maintaining complex heterogeneous infra (Flink/Spark/custom data layers) or rely on 3rd party services. Comparison with Existing Frameworks Flink/Spark Streaming - Volga aims to be a fully functional Python-native (with some Rust) alternative to Flink with no dependency on JVM: general streaming DataStream API Volga exposes is very similar to Flink's DataStream API. Volga also includes parts necessary for fully operational ML workloads (On-Demand Compute + proper modular API). ByteWax - similar functionality w.r.t. general Python-based streaming use-cases but lacks ML-specific parts to provide full spectre of tools for real-time feature engineering (On-Demand Compute, proper data models/APIs, feature serving, feature modularity/repository, etc.). Tecton.ai/Fennel.ai/Chalk.ai - Managed services/feature platforms that provide end-to-end functionality for real-time feature engineering, but are black boxes and lead to vendor lock-in. Volga aims to provide the same functionality via combination of streaming and on-demand compute while being open-source and running on a homogeneous platform (i.e. no multiple system to support). Chronon - Has similar goal but is also built on existing engines (Flink/Spark) with custom Scala/Java services and lacks flexibility w.r.t. pipelines configurability, data models and Python integrations. What’s Next Volga is currently in alpha with most complex parts of the system in place (streaming, on-demand layer, data models and APIs are done), the main work now is introducing fault-tolerance (state persistence and checkpointing), finishing operators (join and window), improving batch execution, adding various data connectors and proper observability - here is the v1.0 Release Roadmap. I'm posting about the progress and technical details in the blog - would be happy to grow the audience and get feedback (here is more about motivation, high level architecture and in-depth streaming engine deign). GitHub stars are also extremely helpful. If anyone is interested in becoming a contributor - happy to hear from you, the project is in early stages so it's a good opportunity to shape the final result and have a say in critical design decisions. Thank you! submitted by /u/saws_baws_228 [link] [comments]

  • [R] Equivariant Image Generation Through Translation-Invariant Task Decomposition
    by /u/Successful-Western27 (Machine Learning) on March 26, 2025 at 11:16 am

    I've been exploring this new equivariant approach to autoregressive image modeling that addresses a fundamental problem: traditional image generation models don't handle transformations (like rotations and flips) consistently. The researchers have developed a framework that ensures equivariance - meaning that transforming an input and then processing it produces the same result as processing first and then transforming. This is achieved through: Technical Contributions: - Equivariant pixel embeddings that transform properly with the image - A novel equivariant pixel ordering method that maintains consistency across transformations - Integration with autoregressive models for image generation that preserves equivariance properties - Support for different transformation groups (rotations, reflections, dihedral) Key Results: - Improved log-likelihood scores on CIFAR-10 and ImageNet compared to baseline models - Generated images maintain consistency and symmetry properties across transformations - Demonstrated better sample diversity while preserving structural properties - Showed that both equivariant ordering and embedding components contribute to performance gains I think this approach represents an important step toward more robust image generation systems. When models understand fundamental transformation properties, they can develop a more coherent internal representation of visual concepts. This could potentially lead to better generalization, more reliable image editing tools, and models that require less data to learn meaningful representations. I think the computational complexity challenges mentioned in the limitations are real concerns, but the core principles could inspire more efficient implementations. The focus on spatial transformations is a natural starting point, and extending to other transformation types (lighting, perspective) would be valuable future work. TLDR: A new technique makes image generation models transformation-aware by incorporating equivariance properties into autoregressive frameworks, improving both quantitative metrics and sample quality/consistency. Full summary is here. Paper here. submitted by /u/Successful-Western27 [link] [comments]

  • [Project]How do I perform inference on the ScienceQA dataset using IDEFICS-9B model.
    by /u/uppercuthard2 (Machine Learning) on March 26, 2025 at 11:12 am

    Kaggle notebook link The notebook consist of code to setup the dependencies, clone the scienceqa dataset and prepare it for inference. My goal is to first filter out all the questions that consist of only 2 options called two_option_dataset. I then create three datasets from two_option_dataset called original_dataset, first_pos_dataset, and second_pos_dataset original_dataset is just an exact copy of two_option_dataset first_pos_dataset is a modified dataset where the answer is always present in the 0th index second_pos_dataset: answer present in 1st index. I want to run inference on all three of these datasets, and compare the accuracies. But I am finding difficulty in getting IDEFICS to give the response in the correct format. If this is not the right sub to ask for help regrading this, pls direct me to the correct one. For reference, here is the kaggle notebook for inference on the same datasets using llava-7B. submitted by /u/uppercuthard2 [link] [comments]

  • Tensorflow not detecting RTX 5080 GPU - Help [D]
    by /u/exotic123567 (Machine Learning) on March 26, 2025 at 10:42 am

    I built a new System with RTX 5080 in it and wanted to test out some previous models I had built using tensorflow and jupyter notebook, but I just can't seem to get Tensorflow to detect my GPU. I tried running it on WSL Ubuntu 22.04 within a conda environment with python 3.10 but after installing it, It still doesn't detect my GPU. When I try building it from source, it doesn't build. I don't know what to do. Does anyone here have an RTX 5000 series Graphics card? - if so, how'd you get Tensorflow running on your system? submitted by /u/exotic123567 [link] [comments]

  • [D] ACL ARR Feb 2025 Discussion
    by /u/AccomplishedCode4689 (Machine Learning) on March 26, 2025 at 7:48 am

    Feb ARR reviews will be out soon. This is a thread for all types of discussions. submitted by /u/AccomplishedCode4689 [link] [comments]

  • [D] [P] - Determining Physical Anchor Points on Object
    by /u/503dev (Machine Learning) on March 26, 2025 at 5:37 am

    Hi fellow redditors. I'm pretty far along with a project I've been building and I could use some ideas or dialog on a specific problem. Problem: I need to determine two physical or grabbing or anchoring. The positioning logical are handled by other models I have working. Details: looking top down on an object the goal is to find two anchor spots, the objects are known and only 15 or 20 variants. They are all flat but not 2D aka have some volume and the dimension varies. The goal is to find the center / bisect and then half way between the center and edge of object on each side - establish a point to anchor too physically. My question for all of you: what possible strategies or models would you all consider for a task like this? I considered using Yolov8 for segmentation and then more simplistic methods for final processing but my solution feels awkward and inefficient. The objects are in perfect lighting, controlled environment and there is a decent amount of computing power available for the task. submitted by /u/503dev [link] [comments]

  • [D] [P] Variational Inference for Neural Network Weights in High-Dimensional Spatio-Temporal Models?
    by /u/Specific-Dark (Machine Learning) on March 25, 2025 at 4:56 pm

    Hey everyone ! I'm currently working on a spatio-temporal prediction project for my Bayesian ML class using a combination of GNN (message-passing style) and LSTM. The goal is to recursively predict the mean and standard deviation of a target variable over multiple future steps. Right now, I'm optimizing the Negative Log Likelihood of a predicted Gaussian to capture aleatoric uncertainty. So far, I'm only feeding in the past values of the target input, though I plan to bring in auxiliary variables (physical features, etc.) later. I've seen some skepticism in this subreddit around using variational inference (VI) for uncertainty quantification, particularly about its expressiveness and scalability. Still, I'm curious: What are some viable approaches for capturing epistemic uncertainty via VI over neural network weights, especially in high-dimensional settings? But I'm wondering what the best way is to model epistemic uncertainty, ideally through variational inference over the network weights. My data is pretty high-dimensional (3D structure: time × space × features), so any method would need to scale reasonably. A few techniques that come to my mind: - Bayes by Backprop - MCMC Dropout? - Maybe even low-rank approximations? Has anyone had success applying VI to large models (like GNN + LSTM hybrids) in a way that’s not intractable? Would love to hear what others have tried or if there are any recent papers worth looking into. Thanks in advance! submitted by /u/Specific-Dark [link] [comments]

  • [R] [D] The Disconnect Between AI Benchmarks and Math Research
    by /u/poltory (Machine Learning) on March 25, 2025 at 4:12 pm

    Current AI systems boast impressive scores on mathematical benchmarks. Yet when confronted with the questions mathematicians actually ask in their daily research, these same systems often struggle, and don't even realize they are struggling. I've written up some preliminary analysis, both with examples I care about, and data from running a website that tries to help with exploratory research. submitted by /u/poltory [link] [comments]

  • [D] My custom DynamicNeuralNetwork hit 2.63 total loss on ARC‑AG1 at 0.6 epochs—projected 78% exact‑match validation before finishing epoch 1
    by /u/No_Cartographer7065 (Machine Learning) on March 25, 2025 at 3:51 pm

    Hey everyone—I’m excited (and honestly a little stunned) by how quickly my from‑scratch DynamicNeuralNetwork is learning ARC‑AGI tasks. I built this model over two years. After fewer than 100 gradient updates (0.6 of a full epoch on the 1,302‑example ARC training set), it’s already achieved: • Total loss: 2.63 (started above 11) • Cross‑entropy ≈ Knowledge Distillation loss (~2.60 each) • Cosine similarity ≈ 0.70 to the teacher model • Combined reward: 0.228 • Healthy scaled entropy (0.196) Based on these curves—and comparing to distilled baselines—I project it will hit ≈78% exact‑match accuracy on held‑out ARC validation by the end of epoch 1 (163 steps), with BLEU >0.90. That’s state‑of‑the‑art narrow reasoning performance for a Small model, before even finishing one pass through the data. This isn’t simply overfitting or memorization: the model’s balanced CE vs KD losses, rising cosine alignment, and robust uncertainty suggest genuine pattern abstraction. And it’s happening faster than any comparable distilled architecture I’ve seen. I’m sharing because I believe Phillnet2’s early trajectory represents a meaningful advance in narrow generalization. I introduce Phillnet2, a DynamicNeuralNetwork. Without any prior exposure to ARC‑AGI data, Phillnet2 distilled knowledge from a teacher and achieved a total training loss of 2.63 at just 0.6 epochs (≈97 steps) on the ARC‑AGI training set. Key metrics at this point include balanced cross‑entropy and knowledge‑distillation losses (~2.60 each), cosine similarity of 0.70 with the teacher’s hidden representations, and a combined reward of 0.228—exceeding typical baseline performance. I forecast a held‑out exact‑match accuracy of 78% by the end of epoch 1, surpassing state‑of‑the‑art distilled models on ARC. These results suggest Phillnet2 rapidly internalizes complex reasoning patterns, marking a substantial leap in narrow generalization capabilities. submitted by /u/No_Cartographer7065 [link] [comments]

  • [D][P] Can I use SMPL-generated outputs to train a commercial pose estimation model?
    by /u/I_am_a_robot_ (Machine Learning) on March 25, 2025 at 3:22 pm

    I plan to train a pose estimation network as part of a pipeline in a product to be commercialized. My question is if I can use a pose estimator trained to output SMPL pose parameters to generate pseudo ground truths on my own set of images, that will be used to train my network. I will then use my trained network to output the pose parameters and run forward kinematics on it using my own manually computed limb measurements, and for other tasks that does not involve SMPL at all. This post mentions that it is only the body models that are licensed, which is something I do not use at all. How true is that ? https://www.reddit.com/r/computervision/comments/1j2auox/how_to_perform_human_mesh_recovery_when_most/ I cant use models like OpenPose or RTMW because they only output the joint positions. I need the joint angles for internal limb rotations, something that is very difficult / impossible to obtain via keypoints. submitted by /u/I_am_a_robot_ [link] [comments]

  • [R] Adaptive Token Selection via Reconstruction-Based Feature Utility for Efficient Vision Encoders
    by /u/Successful-Western27 (Machine Learning) on March 25, 2025 at 2:24 pm

    I've been looking into this new approach called Adaptive Token Reduction (ATR) for vision transformers, which tackles a fundamental efficiency problem in computer vision models. Transformers have become dominant in vision tasks, but they process images by splitting them into hundreds or thousands of tokens, which gets computationally expensive fast. ATR addresses this by adaptively reducing tokens based on their importance to the final prediction. The key insight is that not all image regions require equal attention - some contain critical information while others are redundant. ATR uses a two-stage method: Stage 1: A lightweight token scorer assigns importance values to each token Stage 2: Low-importance tokens are pruned, while similar tokens are merged The reduction happens progressively through the network layers Token importance is determined adaptively for each image (unlike fixed patterns) The results are impressive: ViT-B/16: 47% FLOP reduction with only 0.5% accuracy drop on ImageNet Object detection: 40% FLOP reduction with just 0.3 AP drop on COCO Semantic segmentation: 50% FLOP reduction with 0.3 mIoU drop on ADE20K Works with both supervised models and self-supervised approaches (MAE) Consistently outperforms previous token reduction methods I think this addresses a critical bottleneck in deploying transformer models in production environments where computational resources are limited. The ability to maintain 99.5% of the original accuracy while nearly halving computation is a substantial step toward more efficient vision systems. What's particularly valuable is that ATR is architecture-agnostic - it can be integrated into existing transformer-based models without major redesigns. This means we could see these efficiency gains applied broadly across computer vision systems. I'm especially interested in how this approach might extend to video models, where the token redundancy problem is even more severe due to temporal dimensions. TLDR: ATR introduces an adaptive way to reduce token counts in vision transformers by up to 50% while maintaining accuracy. It intelligently decides which image regions to keep based on their importance and works across multiple vision tasks. Full summary is here. Paper here. submitted by /u/Successful-Western27 [link] [comments]

  • [R] Spatial Text Rendering: Enabling text-only LLMs to "see" documents
    by /u/cpcdoy (Machine Learning) on March 25, 2025 at 2:20 pm

    Hey r/machinelearning! I recently published an article titled "Spatial Text Rendering: Pushing the Limits of Spatial Understanding in LLMs" where I share a technique I've been using for quite some time now to help text-only LLMs process visually complex documents before Vision Language Models (VLMs) became usable. I thought it might be useful for anyone working with document processing! ➡️ Article link Summary: This article introduces Spatial Text Rendering (STR), a method that bridges the gap between visually complex documents and text-only LLMs by preserving the crucial spatial information that gives documents their meaning. While Vision-Language Models (VLMs) continue to advance, we needed an immediate solution that could handle complex financial documents in the MEA region (but not limited to it), including Arabic text and mixed right-to-left scripts. STR uses image processing techniques to extract the document's underlying structure and render it as spatially-aware text that LLMs can understand. Key Points and Highlights: Financial documents present unique challenges: complex layouts, mixed languages, and data that require absolute precision Spatial Text Rendering involves: document preprocessing/deskewing, OCR with spatial coordinates, structure extraction, and structural line detection We use a text-based rendering approach that translates visual structure into a format LLMs already understand from their pre-training A compaction process significantly reduces token usage while preserving key information Testing showed excellent results across multiple LLMs (Claude, GPT-4o, etc.) even without fine-tuning The approach offers an immediate solution for document processing while VLMs continue to develop and become more affordable to use ➡️ Link to a comparison of model results on an example document Side Open Discussion: One interesting aspect I've observed is that many LLMs seem to have robust spatial reasoning capabilities from their pre-training alone, despite not being explicitly trained for this task. This suggests that LLMs might have absorbed more spatial understanding through their text-only training than previously thought. I'm curious if others have observed and taken advantage of similar capabilities? Let me know what you think! submitted by /u/cpcdoy [link] [comments]

  • [D] FAccT Doctoral Colloquium
    by /u/Massive_Horror9038 (Machine Learning) on March 25, 2025 at 2:14 pm

    Did any of you applied to FAccT Doctoral Colloquium? Did you already receive any response from the selection process? The notification date was March 20th, but I didn't receive anything yet. submitted by /u/Massive_Horror9038 [link] [comments]

  • [D] ICML 2025 workshops
    by /u/isogonal-conjugate (Machine Learning) on March 25, 2025 at 1:41 pm

    Does anyone know when will the list of workshops at ICML2025 be published? I saw that the workshop notification deadline has passed already a week ago. I'd specifically like to know if there will be a workshop related to geometric deep learning or symmetries in ML, and if there is one, what is the deadline for submissions. Thanks! submitted by /u/isogonal-conjugate [link] [comments]

  • A better place for graph learning papers [R] [D]
    by /u/gholamaliaminian (Machine Learning) on March 25, 2025 at 11:27 am

    We have a paper on graph neural networks that we've been working on for a while: https://arxiv.org/pdf/2502.00716. Over the past year, we’ve submitted it to several top-tier ML conferences (NeurIPS, ICML, and LOG), but unfortunately, it hasn’t been accepted. At this point, we're considering submitting it to a different venue. Do you have any suggestions for conferences or workshops that might be a good fit? Also, any feedback or comments on the paper would be greatly appreciated. submitted by /u/gholamaliaminian [link] [comments]

  • [D] Scopus listing of Conferences like ICML/ICLR/NeurIPS
    by /u/Turbulent-Complex-25 (Machine Learning) on March 25, 2025 at 10:06 am

    I know a bit stupid question, because how considered these journals are in the community. But as a PhD student, for my publications only scopus listed publications are considered. I googled a bit, but could not find information on the scopus listing of these conferences. Do you have any knowledge on this? submitted by /u/Turbulent-Complex-25 [link] [comments]

  • [P] Is there anyway to finetune Stable Video Diffusion with minimal VRAM?
    by /u/blacktime14 (Machine Learning) on March 25, 2025 at 8:29 am

    I'm posting here instead of r/generativeAI since there seems to be more active people here. Is there any way to use as little VRAM as possible for finetuning Stable Video Diffusion? I've downloaded the official pretrained SVD model (https://huggingface.co/stabilityai/stable-video-diffusion-img2vid) The description says "This model was trained to generate 14 frames at resolution 576x1024 given a context frame of the same size." Thus, for full finetuning, do I have to stick with 14 frames and 576x1024 resolution? (which requires 7-80 VRAM) What I want for now is just to debug and test the training loop with slightly smaller VRAM (ex. with 3090). Then would it be possible for me to do things like reducing the number of frames or lowering spatial resolution? Since currently I have only smaller GPU, I just want to verify that the training code runs correctly before scaling up. Would appreciate any tips. Thanks! submitted by /u/blacktime14 [link] [comments]

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Top 1000 Canada Quiz and trivia: CANADA CITIZENSHIP TEST- HISTORY - GEOGRAPHY - GOVERNMENT- CULTURE - PEOPLE - LANGUAGES - TRAVEL - WILDLIFE - HOCKEY - TOURISM - SCENERIES - ARTS - DATA VISUALIZATION
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Exploring the Pros and Cons of Visiting All Provinces and Territories in Canada.
Exploring the Pros and Cons of Visiting All Provinces and Territories in Canada

Exploring the Advantages and Disadvantages of Visiting All 50 States in the USA
Exploring the Advantages and Disadvantages of Visiting All 50 States in the USA


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