Book Image

Building Machine Learning Systems with Python

Book Image

Building Machine Learning Systems with Python

Overview of this book

Machine learning, the field of building systems that learn from data, is exploding on the Web and elsewhere. Python is a wonderful language in which to develop machine learning applications. As a dynamic language, it allows for fast exploration and experimentation and an increasing number of machine learning libraries are developed for Python.Building Machine Learning system with Python shows you exactly how to find patterns through raw data. The book starts by brushing up on your Python ML knowledge and introducing libraries, and then moves on to more serious projects on datasets, Modelling, Recommendations, improving recommendations through examples and sailing through sound and image processing in detail. Using open-source tools and libraries, readers will learn how to apply methods to text, images, and sounds. You will also learn how to evaluate, compare, and choose machine learning techniques. Written for Python programmers, Building Machine Learning Systems with Python teaches you how to use open-source libraries to solve real problems with machine learning. The book is based on real-world examples that the user can build on. Readers will learn how to write programs that classify the quality of StackOverflow answers or whether a music file is Jazz or Metal. They will learn regression, which is demonstrated on how to recommend movies to users. Advanced topics such as topic modeling (finding a text's most important topics), basket analysis, and cloud computing are covered as well as many other interesting aspects.Building Machine Learning Systems with Python will give you the tools and understanding required to build your own systems, which are tailored to solve your problems.
Table of Contents (20 chapters)
Building Machine Learning Systems with Python
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
Index

Fetching the data


Luckily for us, the team behind stackoverflow provides most of the data behind the StackExchange universe to which stackoverflow belongs under a CC Wiki license. While writing this, the latest data dump can be found at http://www.clearbits.net/torrents/2076-aug-2012. Most likely, this page will contain a pointer to an updated dump when you read it.

After downloading and extracting it, we have around 37 GB of data in the XML format. This is illustrated in the following table:

File

Size (MB)

Description

badges.xml

309

Badges of users

comments.xml

3,225

Comments on questions or answers

posthistory.xml

18,370

Edit history

posts.xml

12,272

Questions and answers—this is what we need

users.xml

319

General information about users

votes.xml

2,200

Information on votes

As the files are more or less self-contained, we can delete all of them except posts.xml; it contains all the questions and answers as individual row tags within the root tag posts. Refer...