AWS Certified Machine Learning: Feature Engineering Overview

Amazon Web Services 2021
  • 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

  • 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

  • Playable
    1. 
    Course Overview
    57s
    UP NEXT
  • Playable
    2. 
    What Is Feature Engineering?
    1m 45s
  • Locked
    3. 
    Features and the Dimensionality Dilemma
    2m 15s
  • Locked
    4. 
    Amazon SageMaker for Feature Engineering
    1m 36s
  • Locked
    5. 
    How Amazon SageMaker Feature Store Works
    1m 51s
  • Locked
    6. 
    Working with Amazon SageMaker Feature Store
    9m
  • Locked
    7. 
    How Amazon SageMaker Ground Truth Works
    2m 23s
  • Locked
    8. 
    Working with Amazon SageMaker Ground Truth
    7m 34s
  • Locked
    9. 
    Missing Data Imputation in ML Models
    3m 36s
  • Locked
    10. 
    Imbalanced Data in ML Classification
    1m 45s
  • Locked
    11. 
    How Data Outliers Impact Data Analysis
    1m 26s
  • Locked
    12. 
    Course Summary
    39s

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

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