Building Features from Nominal Data

سرفصل های دوره

This course covers various techniques for encoding categorical data, starting with the familiar forms of one-hot and label encoding, before moving to contrast coding schemes such as simple coding, Helmert coding, and orthogonal polynomial coding.

1. Course Overview

1. Course Overview

2. Implementing Approaches to Working with Categorical Data

01. Version Check

02. Module Overview

03. Prerequisites and Course Outline

04. Continuous and Categorical Data

05. Numeric Data

06. Categorical Data

07. Label Encoding and One-hot Encoding

08. Choosing between Label Encoding and One-hot Encoding

09. Types of Classification Tasks

10. One-hot Encoding with Known and Unknown Categories

11. One-hot Encoding on a Pandas Data Frame Column

12. One-hot Encoding Using pd.get_dummies()

13. Label Encoding to Convert Categorical Data to Ordinal

14. Label Binarizer to Perform One vs. Rest Encoding of Targets

15. Multilabel Binarizer for Encoding Multilabel Targets

16. Module Summary

3. Understanding and Implementing Dummy Coding

1. Module Overview

2. The Dummy Trap

3. Avoiding the Dummy Trap

4. Dummy Coding to Overcome Limitations of One-hot Encoding

5. Regression Analysis with Dummy or Treatment Coding

6. Dummy Coding Using Patsy

7. Perform Regression Analysis Using Machine Learning on Dummy Coded Categories

8. Performing Linear Regression Using Machine Learning with One-hot Encoded Categories

9. Module Summary

4. Understanding and Implementing Contrast Coding

01. Module Overview

02. Dummy Coding vs. Contrast Coding

03. Exploring Contrast Coding Techniques

04. Regression Analysis Using Simple Effect Coding

05. Performing Linear Regression Using Machine Learning with Simple Effect Coding

06. Regression Using Backward Difference Encoding

07. Regression Using Helmert Encoding

08. Generating Equally Spaced Categories to Perform Orthogonal Polynomial Encoding

09. Performing Regression Analysis Using Orthogonal Polynomial Encoding

10. Module Summary

5. Implementing Bin Counting and Feature Hashing

1. Module Overview

2. Bucketing Continuous Data

3. Bucketing Continuous Data Using Pandas

4. Categorizing Continuous Data Using the KBinsDiscretizer

5. Hashing

6. Feature Hashing with Dictionaries, Tuples, and Text Data

7. Building a Simple Regression Model Using Hashed Categorical Values

8. Summary and Further Study

45,900 تومان

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تولید کننده: PluralSight