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Machine Learning with Python for Everyone


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Table of Contents

  • 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

About the Author

Dr. Mark Fenner, owner of Fenner Training and Consulting, LLC, has taught computing and mathematics to diverse adult audiences since 1999, and holds a PhD in computer science. His research has included design, implementation, and performance of machine learning and numerical algorithms; developing learning systems to detect user anomalies; and probabilistic modeling of protein function.

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