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Building Machine Learning Systems with Python PDF 下载
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You could argue that it is a fortunate coincidence that you are holding this book in
your hands (or your e-book 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. You 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 how and why.
Most of this book will cover the how. How should the data be processed so that
machine learning algorithms can make the most out of it? How should you 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 field. After all, we only
covered some of the "hows" and just a tiny fraction of the "whys". But at 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 overfitting.
Chapter 2, Learning How to Classify with Real-world Examples, explains the use of
real data to learn about classification, whereby we train a computer to be able to
distinguish between different classes of flowers.
Chapter 3, Clustering – Finding Related Posts, explains how powerful the
bag-of-words approach is when we apply it to finding similar posts without
really understanding them.
Preface
Chapter 4, Topic Modeling, takes us beyond assigning each post to a single cluster
and shows us how assigning them to several topics as real text can deal with
multiple topics.
Chapter 5, Classification – Detecting Poor Answers, explains how to use logistic
regression to find whether a user's answer to a question is good or bad. Behind
the scenes, we will learn how to use the bias-variance trade-off to debug machine
learning models.
Chapter 6, Classification II – Sentiment Analysis, introduces how Naive Bayes
works, and how to use it to classify tweets in order to see whether they are
positive or negative.
Chapter 7, Regression – Recommendations, discusses a classical topic in handling
data, but it is still relevant today. We will use it to build recommendation
systems, a system that can take user input about the likes and dislikes to
recommend new products.
Chapter 8, Regression – Recommendations Improved, improves our recommendations
by using multiple methods at once. We will also see how to build recommendations
just from shopping data without the need of rating data (which users do not
always provide).
Chapter 9, Classification III – Music Genre Classification, illustrates how if someone has
scrambled our huge music collection, then our only hope to create an 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 – Pattern Recognition, explains how to apply classifications
in the specific context of handling images, a field known as pattern recognition.
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, Big(ger) Data, explains how data sizes keep getting bigger, and how
this often becomes a problem for the analysis. In this chapter, we explore some
approaches to deal with larger data by taking advantage of multiple core or
computing clusters. We also have an introduction to using cloud computing
(using Amazon's Web Services as our cloud provider).
Appendix, Where to Learn More about Machine Learning, covers a list of wonderful
resources available for machine learning
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