AWS Certified Machine Learning: Feature Engineering Overview
Amazon Web Services
| Intermediate
- 12 videos | 34m 47s
- Includes Assessment
- Earns a Badge
Feature engineering is key in extracting the right attributes from raw incoming data, which is fundamental in building reliable ML algorithms. Amazon SageMaker, a fully managed machine learning studio on AWS, provides feature engineering functionality and many other machine-learning-related tasks. Use this course to explore fundamental feature engineering concepts and learn how to use Amazon SageMaker for feature engineering tasks. Work with the various tools available in SageMaker for preparing data for ML models, such as Ground Truth (for labeling data) and Feature Store (for storing, retrieving, and sharing features). Moving along, investigate various deficiencies, such as missing values, imbalance, and outliers, in real-world data and learn how to address these challenges. Upon completion, you'll be able to carry out feature engineering tasks efficiently using Amazon SageMaker, further preparing you for the AWS Certified Machine Learning - Specialty certification exam.
WHAT YOU WILL LEARN
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Discover the key concepts covered in this courseDescribe the basic concepts behind feature engineeringDescribe how dimensions and features are linked to each other, specifying their impacts on building accurate ml modelsDescribe the capabilities of amazon sagemaker regarding feature engineeringDescribe how to use amazon sagemaker feature store to fully manage repositories for ml featuresWork with amazon sagemaker feature store to achieve feature consistency and standardization
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Describe how amazon sagemaker ground truth works and name its major benefitsWork with amazon sagemaker ground truth to identify its major workflowsDescribe how missing data impacts ml models and name ways to deal with missing dataSpecify how skewed data can affect ml classification and ways to address itDescribe how data outliers impact data analysis and name common ways to deal with outliersSummarize the key concepts covered in this course
IN THIS COURSE
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57s
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1m 45sIn this video, you will learn how to describe the basic concepts behind feature engineering. FREE ACCESS
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2m 15sDuring this video, you will discover how dimensions and features are linked to each other, and how this impacts building accurate ML models. FREE ACCESS
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1m 36sIn this video, you will learn how to describe the capabilities of Amazon SageMaker regarding feature engineering. FREE ACCESS
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1m 51sDiscover how to describe how to use Amazon SageMaker Feature Store to manage repositories for ML features. FREE ACCESS
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9mIn this video, you will work with Amazon SageMaker Feature Store to ensure features are consistent and standardized. FREE ACCESS
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2m 23sAfter completing this video, you will be able to describe how Amazon SageMaker Ground Truth works and name its major benefits. FREE ACCESS
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7m 34sIn this video, find out how to work with Amazon SageMaker Ground Truth to identify its major workflows. FREE ACCESS
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3m 36sUpon completion of this video, you will be able to describe how missing data impacts ML models and name ways to deal with it. FREE ACCESS
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1m 45sDuring this video, you will learn how skewed data can affect ML classification and ways to address it. FREE ACCESS
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1m 26sFind out how to describe how data outliers impact data analysis and name common ways to deal with them. FREE ACCESS
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39sIn this video, we will summarize the key concepts covered in this course. FREE ACCESS
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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