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, Classifying with Real-world Examples, uses real data to learn about classification, whereby we train a computer to be able to distinguish different classes of flowers.

Chapter 3, Clustering – Finding Related Posts, teaches how powerful the bag of words approach is, when we apply it to finding similar posts without really "understanding" them.

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, Classification – 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 find whether a user's answer to a question is good or bad.

Chapter 6, Classification 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, Classification – Music Genre Classification, 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 classification in the specific context of handling images by extracting features from data. We will also see how these methods can be adapted to find 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.

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.

We are using the following versions throughout the book, but you should be fine with any more recent ones:

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++. Thus the resulting code is fast and robust enough to be used in production as well.

In this book, you will find a number of styles of text that distinguish between different kinds of information. Here are some examples of these styles, and an explanation of their meaning.

Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "We then use poly1d() to create a model function from the model parameters."

A block of code is set as follows:

[aws info]
AWS_ACCESS_KEY_ID =  AAKIIT7HHF6IUSN3OCAA
AWS_SECRET_ACCESS_KEY = <your secret key>

Any command-line input or output is written as follows:

>>> import numpy
>>> numpy.version.full_version
1.8.1

New terms and important words are shown in bold. Words that you see on the screen, in menus or dialog boxes for example, appear in the text like this: "Once the machine is stopped, the Change instance type option becomes available."

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