Building Machine Learning Systems with Python.pdf

Preface

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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Preface

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.

To summarize it, we are using the following versions throughout this book, but

you should be fine with any more recent one:

• Python: 2.7

• NumPy: 1.6.2

• SciPy: 0.11

• Scikit-learn: 0.13

Who this book is for

This book is for Python programmers who want to learn how to perform machine

learning using open source libraries. We will walk through the basic modes of

machine learning based on realistic

examples.

This book is also for machine learners who

want to start using Python to build their

systems. Python is a flexible language for

rapid prototyping, while the underlying

algorithms are all written in optimized C

or C++. Therefore, the resulting code is

fast and robust enough to be usable in

production as well.

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