AWS Certified Machine Learning: Feature Engineering Overview
Amazon Web Services 2021
| 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 course
describe the basic concepts behind feature engineering
describe how dimensions and features are linked to each other, specifying their impacts on building accurate ML models
describe the capabilities of Amazon SageMaker regarding feature engineering
describe how to use Amazon SageMaker Feature Store to fully manage repositories for ML features
work with Amazon SageMaker Feature Store to achieve feature consistency and standardization -
describe how Amazon SageMaker Ground Truth works and name its major benefits
work with Amazon SageMaker Ground Truth to identify its major workflows
describe how missing data impacts ML models and name ways to deal with missing data
specify how skewed data can affect ML classification and ways to address it
describe how data outliers impact data analysis and name common ways to deal with outliers
summarize 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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3. Features and the Dimensionality Dilemma2m 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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4. Amazon SageMaker for Feature Engineering1m 36sIn this video, you will learn how to describe the capabilities of Amazon SageMaker regarding feature engineering. FREE ACCESS
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5. How Amazon SageMaker Feature Store Works1m 51sDiscover how to describe how to use Amazon SageMaker Feature Store to manage repositories for ML features. FREE ACCESS
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6. Working with Amazon SageMaker Feature Store9mIn this video, you will work with Amazon SageMaker Feature Store to ensure features are consistent and standardized. FREE ACCESS
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7. How Amazon SageMaker Ground Truth Works2m 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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8. Working with Amazon SageMaker Ground Truth7m 34sIn this video, find out how to work with Amazon SageMaker Ground Truth to identify its major workflows. FREE ACCESS
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9. Missing Data Imputation in ML Models3m 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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10. Imbalanced Data in ML Classification1m 45sDuring this video, you will learn how skewed data can affect ML classification and ways to address it. FREE ACCESS
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11. How Data Outliers Impact Data Analysis1m 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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12. Course Summary39sIn 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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