## Machine Learning with Python for Everyone

### Machine Learning with Python for Everyone (Addison-Wesley Data & Analytics Series) 1st Edition:

**Additional ISBNs:**

**∗ eText ISBN: **0134845641, 978-0134845647, 9780134845647

##### Table of Contents

Cover

About This E-Book

Half Title

Series Page

Title Page

Copyright Page

Dedication

Contents

Foreword

Preface

Audience

Approach

Overview

Acknowledgments

Publisher’s Note

About the Author

Part I: First Steps

1. Let’s Discuss Learning

1.1 Welcome

1.2 Scope, Terminology, Prediction, and Data

1.3 Putting the Machine in Machine Learning

1.4 Examples of Learning Systems

1.5 Evaluating Learning Systems

1.6 A Process for Building Learning Systems

1.7 Assumptions and Reality of Learning

1.8 End-of-Chapter Material

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

4.1 A Simple Regression Dataset

4.2 Nearest-Neighbors Regression and Summary Statistics

4.3 Linear Regression and Errors

4.4 Optimization: Picking the Best Answer

4.5 Simple Evaluation and Comparison of Regressors

4.6 EOC

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

6.1 Baseline Classifiers

6.2 Beyond Accuracy: Metrics for Classification

6.3 ROC Curves

6.4 Another Take on Multiclass: One-versus-One

6.5 Precision-Recall Curves

6.6 Cumulative Response and Lift Curves

6.7 More Sophisticated Evaluation of Classifiers: Take Two

6.8 EOC

7. Evaluating Regressors

7.1 Baseline Regressors

7.2 Additional Measures for Regression

7.3 Residual Plots

7.4 A First Look at Standardization

7.5 Evaluating Regressors in a More Sophisticated Way: Take Two

7.6 EOC

Part III: More Methods and Fundamentals

8. More Classification Methods

8.1 Revisiting Classification

8.2 Decision Trees

8.3 Support Vector Classifiers

8.4 Logistic Regression

8.5 Discriminant Analysis

8.6 Assumptions, Biases, and Classifiers

8.7 Comparison of Classifiers: Take Three

8.8 EOC

9. More Regression Methods

9.1 Linear Regression in the Penalty Box: Regularization

9.2 Support Vector Regression

9.3 Piecewise Constant Regression

9.4 Regression Trees

9.5 Comparison of Regressors: Take Three

9.6 EOC

10. Manual Feature Engineering: Manipulating Data for Fun and Profit

10.1 Feature Engineering Terminology and Motivation

10.2 Feature Selection and Data Reduction: Taking out the Trash

10.3 Feature Scaling

10.4 Discretization

10.5 Categorical Coding

10.6 Relationships and Interactions

10.7 Target Manipulations

10.8 EOC

11. Tuning Hyperparameters and Pipelines

11.1 Models, Parameters, Hyperparameters

11.2 Tuning Hyperparameters

11.3 Down the Recursive Rabbit Hole: Nested Cross-Validation

11.4 Pipelines

11.5 Pipelines and Tuning Together

11.6 EOC

Part IV: Adding Complexity

12. Combining Learners

12.1 Ensembles

12.2 Voting Ensembles

12.3 Bagging and Random Forests

12.4 Boosting

12.5 Comparing the Tree-Ensemble Methods

12.6 EOC

13. Models That Engineer Features for Us

13.1 Feature Selection

13.2 Feature Construction with Kernels

13.3 Principal Components Analysis: An Unsupervised Technique

13.4 EOC

14. Feature Engineering for Domains: Domain-Specific Learning

14.1 Working with Text

14.2 Clustering

14.3 Working with Images

14.4 EOC

15. Connections, Extensions, and Further Directions

15.1 Optimization

15.2 Linear Regression from Raw Materials

15.3 Building Logistic Regression from Raw Materials

15.4 SVM from Raw Materials

15.5 Neural Networks

15.6 Probabilistic Graphical Models

15.7 EOC

A. mlwpy.py Listing

Index

Code Snippets

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