- Cover
- About This E-Book
- Half Title
- Series Page
- Title Page
- Copyright Page
- Dedication
- Contents
- Foreword
- Preface
- About the Author
- Part I: First Steps
- 1. Let’s Discuss Learning
- 2. Some Technical Background
- 2.1 About Our Setup
- 2.2 The Need for Mathematical Language
- 2.3 Our Software for Tackling Machine Learning
- 2.4 Probability
- 2.5 Linear Combinations, Weighted Sums, and Dot Products
- 2.6 A Geometric View: Points in Space
- 2.7 Notation and the Plus-One Trick
- 2.8 Getting Groovy, Breaking the Straight-Jacket, and Nonlinearity
- 2.9 NumPy versus “All the Maths”
- 2.10 Floating-Point Issues
- 2.11 EOC
- 3. Predicting Categories: Getting Started with Classification
- 3.1 Classification Tasks
- 3.2 A Simple Classification Dataset
- 3.3 Training and Testing: Don’t Teach to the Test
- 3.4 Evaluation: Grading the Exam
- 3.5 Simple Classifier #1: Nearest Neighbors, Long Distance Relationships, and Assumptions
- 3.6 Simple Classifier #2: Naive Bayes, Probability, and Broken Promises
- 3.7 Simplistic Evaluation of Classifiers
- 3.8 EOC
- 4. Predicting Numerical Values: Getting Started with Regression
- Part II: Evaluation
- 5. Evaluating and Comparing Learners
- 5.1 Evaluation and Why Less Is More
- 5.2 Terminology for Learning Phases
- 5.3 Major Tom, There’s Something Wrong: Overfitting and Underfitting
- 5.4 From Errors to Costs
- 5.5 (Re)Sampling: Making More from Less
- 5.6 Break-It-Down: Deconstructing Error into Bias and Variance
- 5.7 Graphical Evaluation and Comparison
- 5.8 Comparing Learners with Cross-Validation
- 5.9 EOC
- 6. Evaluating Classifiers
- 7. Evaluating Regressors
- 5. Evaluating and Comparing Learners
- Part III: More Methods and Fundamentals
- Part IV: Adding Complexity
- A. mlwpy.py Listing
- Index
- Code Snippets
Machine Learning with Python for Everyone
Mark E. Fenner
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