Encoding Categorical Data for Machine Learning

Machine Learning    |    Intermediate
  • 10 videos | 54m 19s
  • Earns a Badge
Data processing is essential for effectively managing the various data types encountered in machine learning (ML), setting the stage for advanced analytical tasks. Mastering encoding techniques for categorical variables allows you to convert qualitative data into quantitative formats, facilitating robust model training and insightful analysis. In this course, learn about numerical and categorical data types, how to handle different data types while training ML models, and the best approach for processing them. Next, explore categorical data encoding methods, perform one-hot encoding, and transform categories into numerical forms. Finally, discover how to implement label and ordinal encoding techniques, discretize variables, and train a random forest model. After completing this course, you will be able to encode categorical data for machine learning.

WHAT YOU WILL LEARN

  • Discover the key concepts covered in this course
    Differentiate between numerical and categorical data for modeling purposes
    Outline categorical data encoding
    Recognize when to use ordinal encoding and label encoding
    Describe encoding techniques for categorical variables in machine learning models
  • Employ encoding techniques to convert categorical data for machine learning
    Implement one-hot encoding for categorical x variables using a dense array
    Implement ordinal and nominal encoding for ranking and binary category transformation in data preprocessing
    Discretize numeric columns using kbinsdiscretizer
    Summarize the key concepts covered in this course

IN THIS COURSE

  • 1m 5s
    In this video, we will discover the key concepts covered in this course. FREE ACCESS
  • 5m 43s
    Upon completion of this video, you will be able to differentiate between numerical and categorical data for modeling purposes. FREE ACCESS
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    3.  One-Hot Encoding of Categorical Data
    5m 37s
    After completing this video, you will be able to outline categorical data encoding. FREE ACCESS
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    4.  Encoding Methods for Categorical Data
    7m 19s
    Through this video, you will be able to recognize when to use ordinal encoding and label encoding. FREE ACCESS
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    5.  Techniques for Encoding Categorical Variables
    3m 35s
    In this video, we will describe encoding techniques for categorical variables in machine learning models. FREE ACCESS
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    6.  Performing Label Encoding of Categorical Values
    7m
    Discover how to employ encoding techniques to convert categorical data for machine learning. FREE ACCESS
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    7.  Performing One-Hot Encoding of Nominal Categorical Variables
    8m 10s
    In this video, find out how to implement one-hot encoding for categorical X variables using a dense array. FREE ACCESS
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    8.  Encoding Categorical Variables with the Ordinal Encoder
    6m 52s
    Learn how to implement ordinal and nominal encoding for ranking and binary category transformation in data preprocessing. FREE ACCESS
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    9.  Discretizing Variables and Training a Random Forest Model
    7m 31s
    During this video, discover how to discretize numeric columns using KBinsDiscretizer. FREE ACCESS
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    10.  Course Summary
    1m 27s
    In this video, we will summarize the key concepts covered in this course. FREE ACCESS

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