Building Machine Learning Systems with Python

封面

Title Page

Copyright and Credits

Building Machine Learning Systems with Python Third Edition

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Contributors

About the authors

About the reviewers

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Preface

Who this book is for

What this book covers

To get the most out of this book

Download the example code files

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Conventions used

Get in touch

Reviews

Getting Started with Python Machine Learning

Machine learning and Python – a dream team

What the book will teach you – and what it will not

How to best read this book

What to do when you are stuck

Getting started

Introduction to NumPy SciPy Matplotlib and TensorFlow

Installing Python

Chewing data efficiently with NumPy and intelligently with SciPy

Learning NumPy

Indexing

Handling nonexistent values

Comparing the runtime

Learning SciPy

Fundamentals of machine learning

Asking a question

Getting answers

Our first (tiny) application of machine learning

Reading in the data

Preprocessing and cleaning the data

Choosing the right model and learning algorithm

Before we build our first model

Starting with a simple straight line

Toward more complex models

Stepping back to go forward - another look at our data

Training and testing

Answering our initial question

Summary

Classifying with Real-World Examples

The Iris dataset

Visualization is a good first step

Classifying with scikit-learn

Building our first classification model

Evaluation – holding out data and cross-validation

How to measure and compare classifiers

A more complex dataset and the nearest-neighbor classifier

Learning about the seeds dataset

Features and feature engineering

Nearest neighbor classification

Looking at the decision boundaries

Which classifier to use

Summary

Regression

Predicting house prices with regression

Multidimensional regression

Cross-validation for regression

Penalized or regularized regression

L1 and L2 penalties

Using Lasso or ElasticNet in scikit-learn

Visualizing the Lasso path

P-greater-than-N scenarios

An example based on text documents

Setting hyperparameters in a principled way

Regression with TensorFlow

Summary

Classification I – Detecting Poor Answers

Sketching our roadmap

Learning to classify classy answers

Tuning the instance

Tuning the classifier

Fetching the data

Slimming the data down to chewable chunks

Preselecting and processing attributes

Defining what a good answer is

Creating our first classifier

Engineering the features

Training the classifier

Measuring the classifier's performance

Designing more features

Deciding how to improve the performance

Bias variance and their trade-off

Fixing high bias

Fixing high variance

High or low bias?

Using logistic regression

A bit of math with a small example

Applying logistic regression to our post-classification problem

Looking behind accuracy – precision and recall

Slimming the classifier

Ship it!