Certification     Amazon     AWS Certified Machine Learning – Specialty     AWS Certified Machine Learning – Specialty

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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.

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Objectives

AWS Certified Machine Learning: Feature Engineering Overview 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