Complete eTextbook Content:
Part I: First Steps
Chapter 1: Let’s Discuss Learning
Chapter 2: Some Technical Background
Chapter 3: Predicting Categories: Getting Started with Classification
Chapter 4: Predicting Numerical Values: Getting Started with Regression
Part II: Evaluation
Chapter 5: Evaluating and Comparing Learners
Chapter 6: Evaluating Classifiers
Chapter 7: Evaluating Regressors
Part III: More Methods and Fundamentals
Chapter 8: More Classification Methods
Chapter 9: More Regression Methods
Chapter 10: Manual Feature Engineering: Manipulating Data for Fun and Profit
Chapter 11: Tuning Hyperparameters and Pipelines
Part IV: Adding Complexity
Chapter 12: Combining Learners
Chapter 13: Models That Engineer Features for Us
Chapter 14: Feature Engineering for Domains: Domain-Specific Learning
Chapter 15: Connections, Extensions, and Further Directions
Appendix A: mlwpy.py Listing
Name: Machine Learning with Python for Everyone (Addison-Wesley Data & Analytics Series)
Author: Mark E. Fenner
This is a eBook for the actual textbook of Machine Learning with Python for Everyone (Addison-Wesley Data & Analytics Series), by Mark E. Fenner.
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It saves to your hard-drive and can be burned to CD-ROM. All pages are printable.
It is exactly same as the actual hardbook. And it is Color Version.
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