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Andreas C. Müller & Sarah Guido

Introduction to

Machine

Learning

with P y t hon

A GUIDE FOR DATA SCIENTISTS

Table of Contents

Preface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii

Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Why Machine Learning? 1

Problems Machine Learning Can Solve 2

Knowing Your Task and Knowing Your Data 4

Why Python? 5

scikit-learn 5

Installing scikit-learn 6

Essential Libraries and Tools 7

Jupyter Notebook 7

NumPy 7

SciPy 8

matplotlib 9

pandas 10

mglearn 11

Python 2 Versus Python 3 12

Versions Used in this Book 12

A First Application: Classifying Iris Species 13

Meet the Data 14

Measuring Success: Training and Testing Data 17

First Things First: Look at Your Data 19

Building Your First Model: k-Nearest Neighbors 20

Making Predictions 22

Evaluating the Model 22

Summary and Outlook 23

iii

2. Supervised Learning. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Classification and Regression 25

Generalization, Overfitting, and Underfitting 26

Relation of Model Complexity to Dataset Size 29

Supervised Machine Learning Algorithms 29

Some Sample Datasets 30

k-Nearest Neighbors 35

Linear Models 45

Naive Bayes Classifiers 68

Decision Trees 70

Ensembles of Decision Trees 83

Kernelized Support Vector Machines 92

Neural Networks (Deep Learning) 104

Uncertainty Estimates from Classifiers 119

The Decision Function 120

Predicting Probabilities 122

Uncertainty in Multiclass Classification 124

Summary and Outlook 127

Unsupervised Learning and Preprocessing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131

Types of Unsupervised Learning 131

Challenges in Unsupervised Learning 132

Preprocessing and Scaling 132

Different Kinds of Preprocessing 133

Applying Data Transformations 134

Scaling Training and Test Data the Same Way 136

The Effect of Preprocessing on Supervised Learning 138

Dimensionality Reduction, Feature Extraction, and Manifold Learning 140

Principal Component Analysis (PCA) 140

Non-Negative Matrix Factorization (NMF) 156

Manifold Learning with t-SNE 163

Clustering 168

k-Means Clustering 168

Agglomerative Clustering 182

DBSCAN 187

Comparing and Evaluating Clustering Algorithms 191

Summary of Clustering Methods 207

Summary and Outlook 208

Representing Data and Engineering Features. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211

Categorical Variables 212

One-Hot-Encoding (Dummy Variables) 213

iv | Table of Contents

剩余391页未读，继续阅读

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xikakaxi222

2023-07-09

very classic book for ml and python learning!

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