AWS Certified Machine Learning: Feature Engineering Techniques

Amazon Web Services    |    Intermediate
  • 13 videos | 28m 15s
  • Includes Assessment
  • Earns a Badge
Rating 3.9 of 19 users Rating 3.9 of 19 users (19)
Raw data is typically not perfect for developing effective machine learning (ML) models. Often, it needs to be processed using various feature engineering techniques to make it more suitable for building accurate and optimized ML models. Take this course to learn about techniques that help prepare the data to be compatible and improve the performance of machine learning models. Investigate techniques that are used to improve data usability, such as one-hot encoding, binning, transformations, scaling, and shuffling. You will also learn about the importance and usage of text feature engineering and major workflows in the AWS environment. After completing this course, you'll be able to implement feature engineering techniques using AWS workflows, 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 how to perform one-hot encoding and its main purpose
    Define binning and discretization as the process of transforming numerical variables into categorical counterparts
    Outline how data transformation can be used to make data more useful for data analysis
    Define data scaling and normalization and describe why it is important to standardize independent variables
    Outline data shuffling and define its role in removing biases and building more robust training models
    Work with commonly used feature engineering techniques on real data
  • Recognize the basic principles behind text feature engineering
    Describe the process of term frequency-inverse document frequency (tf-idf) and its uses in text mining
    Describe bag-of-words model and compare it to tf-idf
    Describe the concept of n-gram and why they are used for machine learning
    Use spark and emr workflows to prepare data for a tf-idf problem
    Summarize the key concepts covered in this course

IN THIS COURSE

  • 53s
  • 1m 13s
    In this video, you will learn how to describe how to perform one-hot encoding and its main purpose. FREE ACCESS
  • Locked
    3.  Feature Engineering: Binning
    1m 2s
    During this video, you will learn how to define binning and discretization as the process of transforming numerical variables into categorical counterparts. FREE ACCESS
  • Locked
    4.  Feature Engineering: Data Transformations
    1m 29s
    In this video, discover how to outline data transformation to make data more useful for data analysis. FREE ACCESS
  • Locked
    5.  Feature Engineering: Data Scaling and Normalization
    1m 28s
    Discover how to define data scaling and normalization and describe why it is important to standardize independent variables. FREE ACCESS
  • Locked
    6.  Feature Engineering: Data Shuffling
    1m 12s
    In this video, you will outline data shuffling and define its role in removing biases and building more robust training models. FREE ACCESS
  • Locked
    7.  Working with Feature Engineering Techniques
    7m 6s
    After completing this video, you will be able to work with commonly used feature engineering techniques on real data. FREE ACCESS
  • Locked
    8.  Text Feature Engineering
    1m 29s
    In this video, you will learn how to identify the basic principles behind text feature engineering. FREE ACCESS
  • Locked
    9.  Text Mining: TF-IDF
    1m 53s
    Upon completion of this video, you will be able to describe the process of term frequency-inverse document frequency (TF-IDF) and its uses in text mining. FREE ACCESS
  • Locked
    10.  Bag-of-Words Model vs. TF-IDF
    1m 27s
    During this video, you will learn how to describe the bag-of-words model and compare it to TF-IDF. FREE ACCESS
  • Locked
    11.  What are N-Grams?
    1m 55s
    Find out how to describe the concept of an n-gram and why they are used for machine learning. FREE ACCESS
  • Locked
    12.  Using Spark and EMR Workflows for Data Preparation
    6m 21s
    Learn how to use Spark and EMR workflows to prepare data for a TF-IDF problem. FREE ACCESS
  • Locked
    13.  Course Summary
    46s
    In this video, we will summarize the key concepts covered in this course. FREE ACCESS

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