Python机器学习实战：从入门到高级应用

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"《利用Python构建机器学习系统（第二版）》是由Willi Richert编著的一本专为现代应用开发者和分析师讲解如何在实际项目中运用机器学习的书籍。该书共计326页，出版于2015年3月31日，由Packt Publishing发行，适合英语读者。ISBN号分别为1784392774和9781784392772。本书的核心目标是通过Python帮助读者掌握数据挖掘中的关键技能，特别强调了Python作为一种动态语言在快速探索和实验方面的优势。书中汇集了丰富的开源机器学习库，使读者能够专注于任务本身，同时尝试各种创新思路。作者Luis Pedro Coelho与Willi Richert合作，确保了内容的实用性和深度。全书分为12章，内容涵盖了从基础入门到高级技术的广泛领域。第1章介绍了Python机器学习的入门，随后章节逐步深入，包括：第2章通过实例展示分类方法；第3章讲解聚类，如发现相关帖子；第4章探讨主题模型；第5至7章分别涉及分类技术，如检测低质量答案、情感分析以及回归分析。第8章着重于推荐系统，而第9章则应用在音乐风格分类上，展示了机器学习在不同领域的实际应用。第10章介绍了计算机视觉，这是机器学习的一个重要分支，而第11章讨论了维度缩减，有助于处理高维数据。最后一章探讨如何处理更大的数据集，适应不断增长的数据挑战。通过本书，读者不仅能掌握具体的技术，还能学会如何根据实际问题定制自己的机器学习系统，解决数据分析中的实际问题。值得注意的是，本书尊重版权，所有内容未经许可不得复制或传播。尽管作者和出版社尽力保证信息的准确性，但本书不提供任何形式的担保，也不对因本书内容导致的直接或间接损失承担责任。此外，书中提及的所有商标信息已得到适当标注，但并非详尽无遗。《利用Python构建机器学习系统（第二版）》是一本实用且全面的指南，对于希望提升机器学习技能的读者来说，是一部不可或缺的参考资料。"

[ vii ]

Preface

One could argue that it is a fortunate coincidence that you are holding this book in

your hands (or have it on your eBook reader). After all, there are millions of books

printed every year, which are read by millions of readers. And then there is this book

read by you. One could also argue that a couple of machine learning algorithms

played their role in leading you to this book—or this book to you. And we, the

authors, are happy that you want to understand more about the hows and whys.

Most of the book will cover the how. How has data to be processed so that machine

learning algorithms can make the most out of it? How should one choose the right

algorithm for a problem at hand?

Occasionally, we will also cover the why. Why is it important to measure correctly?

Why does one algorithm outperform another one in a given scenario?

We know that there is much more to learn to be an expert in the eld. After all, we

only covered some hows and just a tiny fraction of the whys. But in the end, we hope

that this mixture will help you to get up and running as quickly as possible.

What this book covers

Chapter 1, Getting Started with Python Machine Learning, introduces the basic idea of

machine learning with a very simple example. Despite its simplicity, it will challenge

us with the risk of overtting.

Chapter 2, Classifying with Real-world Examples, uses real data to learn about

classication, whereby we train a computer to be able to distinguish different

classes of owers.

Chapter 3, Clustering – Finding Related Posts, teaches how powerful the bag of

words approach is, when we apply it to nding similar posts without really

"understanding" them.

Preface

[ viii ]

Chapter 4, Topic Modeling, moves beyond assigning each post to a single cluster and

assigns them to several topics as a real text can deal with multiple topics.

Chapter 5, Classication – Detecting Poor Answers, teaches how to use the bias-variance

trade-off to debug machine learning models though this chapter is mainly on using a

logistic regression to nd whether a user's answer to a question is good or bad.

Chapter 6, Classication II – Sentiment Analysis, explains how Naïve Bayes works, and

how to use it to classify tweets to see whether they are positive or negative.

Chapter 7, Regression, explains how to use the classical topic, regression, in handling

data, which is still relevant today. You will also learn about advanced regression

techniques such as the Lasso and ElasticNets.

Chapter 8, Recommendations, builds recommendation systems based on costumer

product ratings. We will also see how to build recommendations just from shopping

data without the need for ratings data (which users do not always provide).

Chapter 9, Classication – Music Genre Classication, makes us pretend that someone

has scrambled our huge music collection, and our only hope to create order is to let a

machine learner classify our songs. It will turn out that it is sometimes better to trust

someone else's expertise than creating features ourselves.

Chapter 10, Computer Vision, teaches how to apply classication in the specic context

of handling images by extracting features from data. We will also see how these

methods can be adapted to nd similar images in a collection.

Chapter 11, Dimensionality Reduction, teaches us what other methods exist that can help

us in downsizing data so that it is chewable by our machine learning algorithms.

Chapter 12, Bigger Data, explores some approaches to deal with larger data by taking

advantage of multiple cores or computing clusters. We also have an introduction to

using cloud computing (using Amazon Web Services as our cloud provider).

Appendix, Where to Learn More Machine Learning, lists many wonderful resources

available to learn more about machine learning.

What you need for this book

This book assumes you know Python and how to install a library using easy_install or

pip. We do not rely on any advanced mathematics such as calculus or matrix algebra.

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Python机器学习实战：从入门到高级应用

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