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

Comparing similarity in topic space


Topics can be useful on their own to build small vignettes with words that are in the previous screenshot. These visualizations could be used to navigate a large collection of documents and, in fact, they have been used in just this way.

However, topics are often just an intermediate tool to another end. Now that we have an estimate for each document about how much of that document comes from each topic, we can compare the documents in topic space. This simply means that instead of comparing word per word, we say that two documents are similar if they talk about the same topics.

This can be very powerful, as two text documents that share a few words may actually refer to the same topic. They may just refer to it using different constructions (for example, one may say the President of the United States while the other will use the name Barack Obama).

Tip

Topic models are useful on their own to build visualizations and explore data. They are also very useful...