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Introduction to

Machine

Learning

with Python

A GUIDE FOR DATA SCIENTISTS

Andreas C. Müller & Sarah Guido

Introduction to Machine Learning

with Python

A Guide for Data Scientists

Andreas C. Müller and Sarah Guido

Beijing

Boston Farnham Sebastopol

Tokyo

Introduction to Machine Learning with Python

by Andreas C. Müller and Sarah Guido

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Published by O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA 95472.

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First Edition

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2016-09-22:

2017-01-13:

2017-06-09:

First Release

Second Release

Third Release

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978-1-449-36941-5

[LSI]

Table of Contents

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

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

Why Machine Learning?

Problems Machine Learning Can Solve

Knowing Your Task and Knowing Your Data

Why Python?

scikit-learn

Installing scikit-learn

Essential Libraries and Tools

Jupyter Notebook

NumPy

SciPy

matplotlib

pandas

mglearn

Python 2 Versus Python 3

Versions Used in this Book

A First Application: Classifying Iris Species

Meet the Data

Measuring Success: Training and Testing Data

First Things First: Look at Your Data

Building Your First Model: k-Nearest Neighbors

Making Predictions

Evaluating the Model

Summary and Outlook

iii

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