You can translate the content of this page by selecting a language in the select box.
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.
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
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
Azure AI Fundamentals AI-900 Exam Prep App for iOS, Android, Windows10/11
Basics and Advanced Machine Learning Quizzes on Azure, Azure Machine Learning Job Interviews Questions and Answer, ML Cheat Sheets
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.
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.
![Amplify Your Brand's Exposure with the AI Unraveled Podcast - Elevate Your Sales Today! [150K downloads per Month]](https://djamgatech.com/wp-content/uploads/2023/06/amplify_your_brand_exposure_with_the_ai_unraveled_podcast_thumbnail.png)
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:
Notes/Hint1:
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)
Notes/Hint2)
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:
ANSWER3:
Notes/Hint3:
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?
ANSWER4:
Notes/Hint4:
Question 5) Which service in the Kinesis family allows you to easily load streaming data into data stores and analytics tools?
ANSWER5:
Notes/Hint5:
Notes 6)
Notes/Hint 8)
Answer 9)
Notes 9)
Answer 10)
Answer 11)
Notes 11)
Notes 12)
Answer 13)
Notes 13)
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?
Answer 14)
Notes 14)
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?
Answer 15)
Notes 15)
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?
Answer 16)
Notes 16)
[appbox appstore 1611045854-iphone screenshots]
[appbox microsoftstore 9n8rl80hvm4t-mobile screenshots]
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?
Answer 17)
Notes 17)
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?
Answer 18)
Notes 18)
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?
Answer 19)
Notes 19)
Question 20) Which service in the Kinesis family allows you to build custom applications that process or analyze streaming data for specialized needs?
Answer 20)
Notes 20)
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
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
Azure AI Fundamentals AI-900 Exam Prep App for iOS, Android, Windows10/11
Basics and Advanced Machine Learning Quizzes on Azure, Azure Machine Learning Job Interviews Questions and Answer, ML Cheat Sheets
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.
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)
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:
Notes/Hint1:
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)
Notes/Hint2)
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:
ANSWER3:
Notes/Hint3:
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?
ANSWER4:
Notes/Hint4:
Question 5) Which service in the Kinesis family allows you to easily load streaming data into data stores and analytics tools?
ANSWER5:
Notes/Hint5:
Notes 6)
Notes/Hint 8)
Answer 9)
Notes 9)
Answer 10)
Answer 11)
Notes 11)
Notes 12)
Answer 13)
Notes 13)
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?
Answer 14)
Notes 14)
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?
Answer 15)
Notes 15)
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?
Answer 16)
Notes 16)
[appbox appstore 1611045854-iphone screenshots]
[appbox microsoftstore 9n8rl80hvm4t-mobile screenshots]
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?
Answer 17)
Notes 17)
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?
Answer 18)
Notes 18)
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?
Answer 19)
Notes 19)
Question 20) Which service in the Kinesis family allows you to build custom applications that process or analyze streaming data for specialized needs?
Answer 20)
Notes 20)
Question21:
Answer21:
Notes 21:
Question22:
Answer22:
Notes 22:
Question23:
Answer23:
Notes 23:
Question24:
Answer24:
Notes 24:
What are the Top 100 AWS and Google Certified Machine Learning Specialty Questions and Answers Dumps?
This blog is the best way is the best way to prepare for your upcoming AWS Certified Machine Learning Specialty and Google Certified Professional Machine Learning Engineer exam. With over 100 questions and answers, this blog provides quizzes similar that are very similar to the real exam. It also includes the option to show and hide answers. Additionally, there are machine learning interview questions and detailed answers, as well as cheat sheets and illustrations. This blog is the best way to make sure you are well-prepared for your AWS Certified Machine Learning Specialty Exam.
The typical Google Machine Learning Engineer salary is $147,218. Machine Learning Engineer salaries at Google can range from $110,000 – $152,183.
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.
- By the end of 2020, 85% of customer interactions will be handled without a human (Call Center, Chatbot, etc…)
- 61% of marketers say artificial intelligence is the most important aspect of their data strategy.
- 80% of business and tech leaders say AI already boosts productivity (Robotic Process Automation, Power Automate, etc..)
- Current AI technology can boost business productivity by up to 40%
AWS Machine Learning Certification Specialty Exam Prep for iOs Android Windows10/11
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
Azure AI Fundamentals AI-900 Exam Prep App for iOS, Android, Windows10/11
Basics and Advanced Machine Learning Quizzes on Azure, Azure Machine Learning Job Interviews Questions and Answer, ML Cheat Sheets
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.
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)
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:
Notes/Hint1:
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)
Notes/Hint2)
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:
ANSWER3:
Notes/Hint3:
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?
ANSWER4:
Notes/Hint4:
Question 5) Which service in the Kinesis family allows you to easily load streaming data into data stores and analytics tools?
ANSWER5:
Notes/Hint5:
Notes 6)
Notes/Hint 8)
Answer 9)
Notes 9)
Answer 10)
Answer 11)
Notes 11)
Notes 12)
Answer 13)
Notes 13)
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?
Answer 14)
Notes 14)
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?
Answer 15)
Notes 15)
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?
Answer 16)
Notes 16)
[appbox appstore 1611045854-iphone screenshots]
[appbox microsoftstore 9n8rl80hvm4t-mobile screenshots]
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?
Answer 17)
Notes 17)
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?
Answer 18)
Notes 18)
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?
Answer 19)
Notes 19)
Question 20) Which service in the Kinesis family allows you to build custom applications that process or analyze streaming data for specialized needs?
Answer 20)
Notes 20)
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
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
Azure AI Fundamentals AI-900 Exam Prep App for iOS, Android, Windows10/11
Basics and Advanced Machine Learning Quizzes on Azure, Azure Machine Learning Job Interviews Questions and Answer, ML Cheat Sheets
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.
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)
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:
Notes/Hint1:
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)
Notes/Hint2)
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:
ANSWER3:
Notes/Hint3:
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?
ANSWER4:
Notes/Hint4:
Question 5) Which service in the Kinesis family allows you to easily load streaming data into data stores and analytics tools?
ANSWER5:
Notes/Hint5:
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?
Notes 6)
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?
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:
Notes/Hint 8)
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?
Answer 9)
Notes 9)
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?
Answer 10)
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?
Answer 11)
Notes 11)
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?
Notes 12)
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?
Answer 13)
Notes 13)
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?
Answer 14)
Notes 14)
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?
Answer 15)
Notes 15)
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?
Answer 16)
Notes 16)
[appbox appstore 1611045854-iphone screenshots]
[appbox microsoftstore 9n8rl80hvm4t-mobile screenshots]
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?
Answer 17)
Notes 17)
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?
Answer 18)
Notes 18)
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?
Answer 19)
Notes 19)
Question 20) Which service in the Kinesis family allows you to build custom applications that process or analyze streaming data for specialized needs?
Answer 20)
Notes 20)
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:
Notes 21:
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:
Notes 22:
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:
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)
Answer25:
Notes 25:
iOs: https://apps.apple.com/
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:
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?
This is a general guide for choosing which algorithm to use depending on what business problem you have and what data you have.
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)
Notes 1)
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?
Answer 2)
Notes 2)
[appbox appstore 1560083470-iphone screenshots]
[appbox googleplay com.awssolutionarchitectassociateexampreppro.app]
Answer 3)
Notes 3)
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?
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?
[appbox appstore 1611045854-iphone screenshots]
[appbox microsoftstore 9n8rl80hvm4t-mobile screenshots]
Machine Learning Q&A Part I:
Google.
Azure and AWS are second class citizens in this area.
Sure, AWS has 70% of the market.
Sure, Azure is the easiest turn key and super user friendly.
But, the king of machine learning in the cloud is GCP.
GCP = Google Cloud Platform
Google has the largest data science team in the world, not mention they have Hinton.
Let’s forgot for a minute they created TensorFlow and give it away.
Let’s just talk about building a real world model with data that doesn’t fit into a excel spreadsheet.
The vast majority of applied machine learning is supervised and that means we need data.
Not just normal data, we need very clean highly structured data.
Where’s the easiest place in the world to upload and model a Petabyte of structured 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
Here, I want to share the best research paper on Machine Learning classification methods, titled ‘Do we Need Hundreds of Classifiers to Solve Real World Classification Problems?’, published in the ‘Journal of Machine Learning Research’.
This paper nicely explained 179 classification techniques and applied them on 121 data sets thus sharing small summary of the paper:
Do we Need Hundreds of Classifiers to Solve Real World Classification Problems?
The paper evaluated 179 classifiers arising from 17 ML families (discriminant analysis, Bayesian, neural networks, support vector machines, decision trees, rule-based classifiers, boosting, bagging, stacking, random forests and other ensembles, generalized linear models, nearest neighbours, partial least squares and principal component regression, logistic and multinomial regression, multiple adaptive regression splines and other methods), implemented in Weka, R ( with and without the caret package), C and Matlab, including all the relevant classifiers available today.
Experiments used total 121 data sets , which represent the whole UCI data base (excluding the large-scale problems) and other own real problems, in order to achieve significant conclusions about the classifier behaviour, not dependent on the data set collection.
The whole data set and partitions are available from: http://persoal.citius.usc.es/manuel.fernandez.delgado/papers/jmlr/data.tar.gz
The classifiers most likely to be the bests are the random forest (RF) versions, the best of which (implemented in R and accessed via caret) achieves 94.1% of the maximum accuracy overcoming 90% in the 84.3% of the data sets. However, the difference is not statistically significant with the second best, the SVM with Gaussian kernel implemented in C using LibSVM, which achieves 92.3% of the maximum accuracy. A few models are clearly better than the remaining ones: random forest, SVM with Gaussian and polynomial kernels, extreme learning machine with Gaussian kernel, C5.0 and avNNet (a committee of multi-layer perceptrons implemented in R with the caret package).
The random forest is clearly the best family of classifiers (3 out of 5 bests classifiers are RF), followed by SVM (4 classifiers in the top-10), neural networks and boosting ensembles (5 and 3 members in the top-20, respectively).
You can see the table with the complete results: http://persoal.citius.usc.es/manuel.fernandez.delgado/papers/jmlr/results.txt
I hope it will be helpful for Statistic and Machine Leaning aspirants!
Thank you!
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.
At a high level, these skills are a combination of software and data engineering.
The persons that are more appropriate to do this job are a data engineer and/or a machine learning engineer.
That being said, if you work at a startup or happen to be in a small company and need to put the models into production yourself, here are the top skills you need to get:
- Well structured code: it doesn’t need to be perfect but at least can be understood and updated by other team members. Avoid spaghetti code[1] as the plague.
- Add logs: if you are a Python user, the logging[2] module is your friend. Avoid print statements at any cost.
- Model versioning: add a hash key to your different models. You will thank me later.
- Metadata everywhere: save as much data about your models and ML experiments as you can (running time, hyperparameters, used features, CV scores, and so on). You will thank me later, again.
- Monitor performances: execution time and statistical scores of your models.
- Data and models management: store the necessary data and models somewhere that is available to everyone (S3[3] for example). Avoid uploading these to your VCS[4] system. Don’t share them using Slack or Drive. I won’t judge you though, I do it sometimes (read often). Read more here …..
Some of the mistakes that might involve during building a machine learning model (I can think of) are listed here:
- Not understanding the structure of the dataset
- Not giving proper care during features selection
- Leaving out categorical features and considering just numerical variables
- Falling into dummy variable trap
- Selection of inefficient machine learning algorithm
- Not trying out various ML algorithms for building the model based on structure of data.
- Improper tuning of model parameters
- 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.
- Read more here…
[appbox appstore 1560083470-iphone screenshots]
[appbox googleplay com.awssolutionarchitectassociateexampreppro.app]
Basically, data mining is a key aspect of data analytics. Some even consider the former as essential to execute before the latter. While data analytics is the complete package and involves most components needed to examine a data set and extract valuable information, data mining focuses specifically on identifying hidden patterns.
That’s just the surface-level comparison though. The image above gives an overview of how the two differ.
One such difference is the presence of a hypothesis. Data analytics usually requires coming up with one, as it aims to find specific answers. Data mining, on the other hand, generally doesn’t need one to test or prove. The expected output are patterns or trends, which doesn’t require coming up with a statement or fact to test.
However, that doesn’t mean you mine data blindly. You still have a goal, whether it’s to come up with a recommender system or identify predictors of a certain dimension. Ultimately though, you strive to come up with data patterns or trends. For data analysis on the other hand, you’re expected to come up with valuable and actionable insights, usually in relation to a predetermined hypothesis. Read more here ….
The data science life cycle is not something well-defined like the software development life-cycle, and there is no ‘one-size-fits-all’ solution for data science projects. Every step in the life-cycle of a data science project depends on various data scientist skills and data science tools. The typical life-cycle of a data science project involves jumping back and forth among various interdependent science tasks using a variety of tools, techniques, programming, etc.
Thus, the data science life-cycle can include the following steps:
- Business requirement understanding.
- Data collection.
- Data cleaning.
- Data analysis.
- Modeling.
- Performance evaluation.
- Communicating with stakeholders.
- Deployment.
- Real-world testing.
- Business buy-in.
- 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.
[appbox appstore 1611045854-iphone screenshots]
[appbox microsoftstore 9n8rl80hvm4t-mobile screenshots]
Machine Learning Q&A -Part II:
At a high level, these skills are a combination of software and data engineering.
The persons that are more appropriate to do this job are a data engineer and/or a machine learning engineer.
That being said, if you work at a startup or happen to be in a small company and need to put the models into production yourself, here are the top skills you need to get:
- Well structured code: it doesn’t need to be perfect but at least can be understood and updated by other team members. Avoid spaghetti code[1] as the plague.
- Add logs: if you are a Python user, the logging[2] module is your friend. Avoid print statements at any cost.
- Model versioning: add a hash key to your different models. You will thank me later.
- Metadata everywhere: save as much data about your models and ML experiments as you can (running time, hyperparameters, used features, CV scores, and so on). You will thank me later, again.
- Monitor performances: execution time and statistical scores of your models.
- Data and models management: store the necessary data and models somewhere that is available to everyone (S3[3] for example). Avoid uploading these to your VCS[4] system. Don’t share them using Slack or Drive. I won’t judge you though, I do it sometimes (read often). Read more here …..
Some of the mistakes that might involve during building a machine learning model (I can think of) are listed here:
- Not understanding the structure of the dataset
- Not giving proper care during features selection
- Leaving out categorical features and considering just numerical variables
- Falling into dummy variable trap
- Selection of inefficient machine learning algorithm
- Not trying out various ML algorithms for building the model based on structure of data.
- Improper tuning of model parameters
- 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.
- Read more here…
Basically, data mining is a key aspect of data analytics. Some even consider the former as essential to execute before the latter. While data analytics is the complete package and involves most components needed to examine a data set and extract valuable information, data mining focuses specifically on identifying hidden patterns.
That’s just the surface-level comparison though. The image above gives an overview of how the two differ.
One such difference is the presence of a hypothesis. Data analytics usually requires coming up with one, as it aims to find specific answers. Data mining, on the other hand, generally doesn’t need one to test or prove. The expected output are patterns or trends, which doesn’t require coming up with a statement or fact to test.
However, that doesn’t mean you mine data blindly. You still have a goal, whether it’s to come up with a recommender system or identify predictors of a certain dimension. Ultimately though, you strive to come up with data patterns or trends. For data analysis on the other hand, you’re expected to come up with valuable and actionable insights, usually in relation to a predetermined hypothesis. Read more here ….
The data science life cycle is not something well-defined like the software development life-cycle, and there is no ‘one-size-fits-all’ solution for data science projects. Every step in the life-cycle of a data science project depends on various data scientist skills and data science tools. The typical life-cycle of a data science project involves jumping back and forth among various interdependent science tasks using a variety of tools, techniques, programming, etc.
Thus, the data science life-cycle can include the following steps:
- Business requirement understanding.
- Data collection.
- Data cleaning.
- Data analysis.
- Modeling.
- Performance evaluation.
- Communicating with stakeholders.
- Deployment.
- Real-world testing.
- Business buy-in.
- 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.
iOs: https://apps.apple.com/ca/app/aws-machine-learning-prep-pro/id1611045854
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:
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?
This is a general guide for choosing which algorithm to use depending on what business problem you have and what data you have.
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)
BNotes 1)
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?
Answer 2)
Notes 2)
[appbox appstore 1560083470-iphone screenshots]
[appbox googleplay com.awssolutionarchitectassociateexampreppro.app]
Answer 3)
Notes 3)
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?
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?
[appbox appstore 1611045854-iphone screenshots]
[appbox microsoftstore 9n8rl80hvm4t-mobile screenshots]
Machine Learning Q&A Part I:
Google.
Azure and AWS are second class citizens in this area.
Sure, AWS has 70% of the market.
Sure, Azure is the easiest turn key and super user friendly.
But, the king of machine learning in the cloud is GCP.
GCP = Google Cloud Platform
Google has the largest data science team in the world, not mention they have Hinton.
Let’s forgot for a minute they created TensorFlow and give it away.
Let’s just talk about building a real world model with data that doesn’t fit into a excel spreadsheet.
The vast majority of applied machine learning is supervised and that means we need data.
Not just normal data, we need very clean highly structured data.
Where’s the easiest place in the world to upload and model a Petabyte of structured 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
Here, I want to share the best research paper on Machine Learning classification methods, titled ‘Do we Need Hundreds of Classifiers to Solve Real World Classification Problems?’, published in the ‘Journal of Machine Learning Research’.
This paper nicely explained 179 classification techniques and applied them on 121 data sets thus sharing small summary of the paper:
Do we Need Hundreds of Classifiers to Solve Real World Classification Problems?
The paper evaluated 179 classifiers arising from 17 ML families (discriminant analysis, Bayesian, neural networks, support vector machines, decision trees, rule-based classifiers, boosting, bagging, stacking, random forests and other ensembles, generalized linear models, nearest neighbours, partial least squares and principal component regression, logistic and multinomial regression, multiple adaptive regression splines and other methods), implemented in Weka, R ( with and without the caret package), C and Matlab, including all the relevant classifiers available today.
Experiments used total 121 data sets , which represent the whole UCI data base (excluding the large-scale problems) and other own real problems, in order to achieve significant conclusions about the classifier behaviour, not dependent on the data set collection.
The whole data set and partitions are available from: http://persoal.citius.usc.es/manuel.fernandez.delgado/papers/jmlr/data.tar.gz
The classifiers most likely to be the bests are the random forest (RF) versions, the best of which (implemented in R and accessed via caret) achieves 94.1% of the maximum accuracy overcoming 90% in the 84.3% of the data sets. However, the difference is not statistically significant with the second best, the SVM with Gaussian kernel implemented in C using LibSVM, which achieves 92.3% of the maximum accuracy. A few models are clearly better than the remaining ones: random forest, SVM with Gaussian and polynomial kernels, extreme learning machine with Gaussian kernel, C5.0 and avNNet (a committee of multi-layer perceptrons implemented in R with the caret package).
The random forest is clearly the best family of classifiers (3 out of 5 bests classifiers are RF), followed by SVM (4 classifiers in the top-10), neural networks and boosting ensembles (5 and 3 members in the top-20, respectively).
You can see the table with the complete results: http://persoal.citius.usc.es/manuel.fernandez.delgado/papers/jmlr/results.txt
I hope it will be helpful for Statistic and Machine Leaning aspirants!
Thank you!
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.
At a high level, these skills are a combination of software and data engineering.
The persons that are more appropriate to do this job are a data engineer and/or a machine learning engineer.
That being said, if you work at a startup or happen to be in a small company and need to put the models into production yourself, here are the top skills you need to get:
- Well structured code: it doesn’t need to be perfect but at least can be understood and updated by other team members. Avoid spaghetti code[1] as the plague.
- Add logs: if you are a Python user, the logging[2] module is your friend. Avoid print statements at any cost.
- Model versioning: add a hash key to your different models. You will thank me later.
- Metadata everywhere: save as much data about your models and ML experiments as you can (running time, hyperparameters, used features, CV scores, and so on). You will thank me later, again.
- Monitor performances: execution time and statistical scores of your models.
- Data and models management: store the necessary data and models somewhere that is available to everyone (S3[3] for example). Avoid uploading these to your VCS[4] system. Don’t share them using Slack or Drive. I won’t judge you though, I do it sometimes (read often). Read more here …..
Some of the mistakes that might involve during building a machine learning model (I can think of) are listed here:
- Not understanding the structure of the dataset
- Not giving proper care during features selection
- Leaving out categorical features and considering just numerical variables
- Falling into dummy variable trap
- Selection of inefficient machine learning algorithm
- Not trying out various ML algorithms for building the model based on structure of data.
- Improper tuning of model parameters
- 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.
- Read more here…
[appbox appstore 1560083470-iphone screenshots]
[appbox googleplay com.awssolutionarchitectassociateexampreppro.app]
Basically, data mining is a key aspect of data analytics. Some even consider the former as essential to execute before the latter. While data analytics is the complete package and involves most components needed to examine a data set and extract valuable information, data mining focuses specifically on identifying hidden patterns.
That’s just the surface-level comparison though. The image above gives an overview of how the two differ.
One such difference is the presence of a hypothesis. Data analytics usually requires coming up with one, as it aims to find specific answers. Data mining, on the other hand, generally doesn’t need one to test or prove. The expected output are patterns or trends, which doesn’t require coming up with a statement or fact to test.
However, that doesn’t mean you mine data blindly. You still have a goal, whether it’s to come up with a recommender system or identify predictors of a certain dimension. Ultimately though, you strive to come up with data patterns or trends. For data analysis on the other hand, you’re expected to come up with valuable and actionable insights, usually in relation to a predetermined hypothesis. Read more here ….
The data science life cycle is not something well-defined like the software development life-cycle, and there is no ‘one-size-fits-all’ solution for data science projects. Every step in the life-cycle of a data science project depends on various data scientist skills and data science tools. The typical life-cycle of a data science project involves jumping back and forth among various interdependent science tasks using a variety of tools, techniques, programming, etc.
Thus, the data science life-cycle can include the following steps:
- Business requirement understanding.
- Data collection.
- Data cleaning.
- Data analysis.
- Modeling.
- Performance evaluation.
- Communicating with stakeholders.
- Deployment.
- Real-world testing.
- Business buy-in.
- 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.
[appbox appstore 1611045854-iphone screenshots]
[appbox microsoftstore 9n8rl80hvm4t-mobile screenshots]
Machine Learning Q&A -Part II:
At a high level, these skills are a combination of software and data engineering.
The persons that are more appropriate to do this job are a data engineer and/or a machine learning engineer.
That being said, if you work at a startup or happen to be in a small company and need to put the models into production yourself, here are the top skills you need to get:
- Well structured code: it doesn’t need to be perfect but at least can be understood and updated by other team members. Avoid spaghetti code[1] as the plague.
- Add logs: if you are a Python user, the logging[2] module is your friend. Avoid print statements at any cost.
- Model versioning: add a hash key to your different models. You will thank me later.
- Metadata everywhere: save as much data about your models and ML experiments as you can (running time, hyperparameters, used features, CV scores, and so on). You will thank me later, again.
- Monitor performances: execution time and statistical scores of your models.
- Data and models management: store the necessary data and models somewhere that is available to everyone (S3[3] for example). Avoid uploading these to your VCS[4] system. Don’t share them using Slack or Drive. I won’t judge you though, I do it sometimes (read often). Read more here …..
Some of the mistakes that might involve during building a machine learning model (I can think of) are listed here:
- Not understanding the structure of the dataset
- Not giving proper care during features selection
- Leaving out categorical features and considering just numerical variables
- Falling into dummy variable trap
- Selection of inefficient machine learning algorithm
- Not trying out various ML algorithms for building the model based on structure of data.
- Improper tuning of model parameters
- 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.
- Read more here…
Basically, data mining is a key aspect of data analytics. Some even consider the former as essential to execute before the latter. While data analytics is the complete package and involves most components needed to examine a data set and extract valuable information, data mining focuses specifically on identifying hidden patterns.
That’s just the surface-level comparison though. The image above gives an overview of how the two differ.
One such difference is the presence of a hypothesis. Data analytics usually requires coming up with one, as it aims to find specific answers. Data mining, on the other hand, generally doesn’t need one to test or prove. The expected output are patterns or trends, which doesn’t require coming up with a statement or fact to test.
However, that doesn’t mean you mine data blindly. You still have a goal, whether it’s to come up with a recommender system or identify predictors of a certain dimension. Ultimately though, you strive to come up with data patterns or trends. For data analysis on the other hand, you’re expected to come up with valuable and actionable insights, usually in relation to a predetermined hypothesis. Read more here ….
The data science life cycle is not something well-defined like the software development life-cycle, and there is no ‘one-size-fits-all’ solution for data science projects. Every step in the life-cycle of a data science project depends on various data scientist skills and data science tools. The typical life-cycle of a data science project involves jumping back and forth among various interdependent science tasks using a variety of tools, techniques, programming, etc.
Thus, the data science life-cycle can include the following steps:
- Business requirement understanding.
- Data collection.
- Data cleaning.
- Data analysis.
- Modeling.
- Performance evaluation.
- Communicating with stakeholders.
- Deployment.
- Real-world testing.
- Business buy-in.
- 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.
iOs: https://apps.apple.com/ca/app/aws-machine-learning-prep-pro/id1611045854
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:
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?
This is a general guide for choosing which algorithm to use depending on what business problem you have and what data you have.
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)
BNotes 1)
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?
Answer 2)
Notes 2)
[appbox appstore 1560083470-iphone screenshots]
[appbox googleplay com.awssolutionarchitectassociateexampreppro.app]
Answer 3)
Notes 3)
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?
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?
[appbox appstore 1611045854-iphone screenshots]
[appbox microsoftstore 9n8rl80hvm4t-mobile screenshots]
Machine Learning Q&A Part I:
Google.
Azure and AWS are second class citizens in this area.
Sure, AWS has 70% of the market.
Sure, Azure is the easiest turn key and super user friendly.
But, the king of machine learning in the cloud is GCP.
GCP = Google Cloud Platform
Google has the largest data science team in the world, not mention they have Hinton.
Let’s forgot for a minute they created TensorFlow and give it away.
Let’s just talk about building a real world model with data that doesn’t fit into a excel spreadsheet.
The vast majority of applied machine learning is supervised and that means we need data.
Not just normal data, we need very clean highly structured data.
Where’s the easiest place in the world to upload and model a Petabyte of structured 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
Here, I want to share the best research paper on Machine Learning classification methods, titled ‘Do we Need Hundreds of Classifiers to Solve Real World Classification Problems?’, published in the ‘Journal of Machine Learning Research’.
This paper nicely explained 179 classification techniques and applied them on 121 data sets thus sharing small summary of the paper:
Do we Need Hundreds of Classifiers to Solve Real World Classification Problems?
The paper evaluated 179 classifiers arising from 17 ML families (discriminant analysis, Bayesian, neural networks, support vector machines, decision trees, rule-based classifiers, boosting, bagging, stacking, random forests and other ensembles, generalized linear models, nearest neighbours, partial least squares and principal component regression, logistic and multinomial regression, multiple adaptive regression splines and other methods), implemented in Weka, R ( with and without the caret package), C and Matlab, including all the relevant classifiers available today.
Experiments used total 121 data sets , which represent the whole UCI data base (excluding the large-scale problems) and other own real problems, in order to achieve significant conclusions about the classifier behaviour, not dependent on the data set collection.
The whole data set and partitions are available from: http://persoal.citius.usc.es/manuel.fernandez.delgado/papers/jmlr/data.tar.gz
The classifiers most likely to be the bests are the random forest (RF) versions, the best of which (implemented in R and accessed via caret) achieves 94.1% of the maximum accuracy overcoming 90% in the 84.3% of the data sets. However, the difference is not statistically significant with the second best, the SVM with Gaussian kernel implemented in C using LibSVM, which achieves 92.3% of the maximum accuracy. A few models are clearly better than the remaining ones: random forest, SVM with Gaussian and polynomial kernels, extreme learning machine with Gaussian kernel, C5.0 and avNNet (a committee of multi-layer perceptrons implemented in R with the caret package).
The random forest is clearly the best family of classifiers (3 out of 5 bests classifiers are RF), followed by SVM (4 classifiers in the top-10), neural networks and boosting ensembles (5 and 3 members in the top-20, respectively).
You can see the table with the complete results: http://persoal.citius.usc.es/manuel.fernandez.delgado/papers/jmlr/results.txt
I hope it will be helpful for Statistic and Machine Leaning aspirants!
Thank you!
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.
At a high level, these skills are a combination of software and data engineering.
The persons that are more appropriate to do this job are a data engineer and/or a machine learning engineer.
That being said, if you work at a startup or happen to be in a small company and need to put the models into production yourself, here are the top skills you need to get:
- Well structured code: it doesn’t need to be perfect but at least can be understood and updated by other team members. Avoid spaghetti code[1] as the plague.
- Add logs: if you are a Python user, the logging[2] module is your friend. Avoid print statements at any cost.
- Model versioning: add a hash key to your different models. You will thank me later.
- Metadata everywhere: save as much data about your models and ML experiments as you can (running time, hyperparameters, used features, CV scores, and so on). You will thank me later, again.
- Monitor performances: execution time and statistical scores of your models.
- Data and models management: store the necessary data and models somewhere that is available to everyone (S3[3] for example). Avoid uploading these to your VCS[4] system. Don’t share them using Slack or Drive. I won’t judge you though, I do it sometimes (read often). Read more here …..
Some of the mistakes that might involve during building a machine learning model (I can think of) are listed here:
- Not understanding the structure of the dataset
- Not giving proper care during features selection
- Leaving out categorical features and considering just numerical variables
- Falling into dummy variable trap
- Selection of inefficient machine learning algorithm
- Not trying out various ML algorithms for building the model based on structure of data.
- Improper tuning of model parameters
- 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.
- Read more here…
[appbox appstore 1560083470-iphone screenshots]
[appbox googleplay com.awssolutionarchitectassociateexampreppro.app]
Basically, data mining is a key aspect of data analytics. Some even consider the former as essential to execute before the latter. While data analytics is the complete package and involves most components needed to examine a data set and extract valuable information, data mining focuses specifically on identifying hidden patterns.
That’s just the surface-level comparison though. The image above gives an overview of how the two differ.
One such difference is the presence of a hypothesis. Data analytics usually requires coming up with one, as it aims to find specific answers. Data mining, on the other hand, generally doesn’t need one to test or prove. The expected output are patterns or trends, which doesn’t require coming up with a statement or fact to test.
However, that doesn’t mean you mine data blindly. You still have a goal, whether it’s to come up with a recommender system or identify predictors of a certain dimension. Ultimately though, you strive to come up with data patterns or trends. For data analysis on the other hand, you’re expected to come up with valuable and actionable insights, usually in relation to a predetermined hypothesis. Read more here ….
The data science life cycle is not something well-defined like the software development life-cycle, and there is no ‘one-size-fits-all’ solution for data science projects. Every step in the life-cycle of a data science project depends on various data scientist skills and data science tools. The typical life-cycle of a data science project involves jumping back and forth among various interdependent science tasks using a variety of tools, techniques, programming, etc.
Thus, the data science life-cycle can include the following steps:
- Business requirement understanding.
- Data collection.
- Data cleaning.
- Data analysis.
- Modeling.
- Performance evaluation.
- Communicating with stakeholders.
- Deployment.
- Real-world testing.
- Business buy-in.
- 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.
[appbox appstore 1611045854-iphone screenshots]
[appbox microsoftstore 9n8rl80hvm4t-mobile screenshots]
Machine Learning Q&A -Part II:
At a high level, these skills are a combination of software and data engineering.
The persons that are more appropriate to do this job are a data engineer and/or a machine learning engineer.
That being said, if you work at a startup or happen to be in a small company and need to put the models into production yourself, here are the top skills you need to get:
- Well structured code: it doesn’t need to be perfect but at least can be understood and updated by other team members. Avoid spaghetti code[1] as the plague.
- Add logs: if you are a Python user, the logging[2] module is your friend. Avoid print statements at any cost.
- Model versioning: add a hash key to your different models. You will thank me later.
- Metadata everywhere: save as much data about your models and ML experiments as you can (running time, hyperparameters, used features, CV scores, and so on). You will thank me later, again.
- Monitor performances: execution time and statistical scores of your models.
- Data and models management: store the necessary data and models somewhere that is available to everyone (S3[3] for example). Avoid uploading these to your VCS[4] system. Don’t share them using Slack or Drive. I won’t judge you though, I do it sometimes (read often). Read more here …..
Some of the mistakes that might involve during building a machine learning model (I can think of) are listed here:
- Not understanding the structure of the dataset
- Not giving proper care during features selection
- Leaving out categorical features and considering just numerical variables
- Falling into dummy variable trap
- Selection of inefficient machine learning algorithm
- Not trying out various ML algorithms for building the model based on structure of data.
- Improper tuning of model parameters
- 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.
- Read more here…
Basically, data mining is a key aspect of data analytics. Some even consider the former as essential to execute before the latter. While data analytics is the complete package and involves most components needed to examine a data set and extract valuable information, data mining focuses specifically on identifying hidden patterns.
That’s just the surface-level comparison though. The image above gives an overview of how the two differ.
One such difference is the presence of a hypothesis. Data analytics usually requires coming up with one, as it aims to find specific answers. Data mining, on the other hand, generally doesn’t need one to test or prove. The expected output are patterns or trends, which doesn’t require coming up with a statement or fact to test.
However, that doesn’t mean you mine data blindly. You still have a goal, whether it’s to come up with a recommender system or identify predictors of a certain dimension. Ultimately though, you strive to come up with data patterns or trends. For data analysis on the other hand, you’re expected to come up with valuable and actionable insights, usually in relation to a predetermined hypothesis. Read more here ….
The data science life cycle is not something well-defined like the software development life-cycle, and there is no ‘one-size-fits-all’ solution for data science projects. Every step in the life-cycle of a data science project depends on various data scientist skills and data science tools. The typical life-cycle of a data science project involves jumping back and forth among various interdependent science tasks using a variety of tools, techniques, programming, etc.
Thus, the data science life-cycle can include the following steps:
- Business requirement understanding.
- Data collection.
- Data cleaning.
- Data analysis.
- Modeling.
- Performance evaluation.
- Communicating with stakeholders.
- Deployment.
- Real-world testing.
- Business buy-in.
- 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.
iOs: https://apps.apple.com/ca/app/aws-machine-learning-prep-pro/id1611045854
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:
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?
This is a general guide for choosing which algorithm to use depending on what business problem you have and what data you have.
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)
BNotes 1)
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?
Answer 2)
Notes 2)
[appbox appstore 1560083470-iphone screenshots]
[appbox googleplay com.awssolutionarchitectassociateexampreppro.app]
Answer 3)
Notes 3)
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?
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?