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Python Machine Learning (Wiley)

You're reading from   Python Machine Learning (Wiley) Python makes machine learning easy for beginners and experienced developers

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Product type Paperback
Published in Apr 2019
Publisher Wiley
ISBN-13 9781119545637
Length 320 pages
Edition 1st Edition
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Author (1):
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Wei-Meng Lee Wei-Meng Lee
Author Profile Icon Wei-Meng Lee
Wei-Meng Lee
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Toc

Table of Contents (16) Chapters Close

1. Cover
2. Introduction FREE CHAPTER
3. CHAPTER 1: Introduction to Machine Learning 4. CHAPTER 2: Extending Python Using NumPy 5. CHAPTER 3: Manipulating Tabular Data Using Pandas 6. CHAPTER 4: Data Visualization Using matplotlib 7. CHAPTER 5: Getting Started with Scikit‐learn for Machine Learning 8. CHAPTER 6: Supervised Learning—Linear Regression 9. CHAPTER 7: Supervised Learning—Classification Using Logistic Regression 10. CHAPTER 8: Supervised Learning—Classification Using Support Vector Machines 11. CHAPTER 9: Supervised Learning—Classification Using K‐Nearest Neighbors (KNN) 12. CHAPTER 10: Unsupervised Learning—Clustering Using K‐Means 13. CHAPTER 11: Using Azure Machine Learning Studio 14. CHAPTER 12: Deploying Machine Learning Models 15. Index
16. End User License Agreement

Summary

In this chapter, you learned about the various types of linear regression. In particular, you learned about the following:

  • Multiple Regression Linear relationships between two or more independent variables and one dependent variable.
  • Polynomial Regression Modeling the relationship between one independent variable and one dependent variable using an nth degree polynomial function.
  • Polynomial Multiple Regression Modeling the relationship between two or more independent variables and one dependent variable using an nth degree polynomial function.

You also learned how to plot the hyperplane showing the relationships between two independent variables and the label.

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