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

P greater than N scenarios


The title of this section is a bit of inside jargon, which you will now learn. Starting in the 1990s, first in the biomedical domain and then on the Web, problems started to appear when P was greater than N. What this means is that the number of features, P, was greater than the number of examples, N (these letters were the conventional statistical shorthand for these concepts). These became known as "P greater than N" problems.

For example, if your input is a set of written text, a simple way to approach it is to consider each possible word in the dictionary as a feature and regress on those (we will later work on one such problem ourselves). In the English language, you have over 20,000 words (this is if you perform some stemming and only consider common words; it is more than ten times that if you keep trademarks). If you only have a few hundred or a few thousand examples, you will have more features than examples.

In this case, as the number of features is greater...