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

Chapter 3. Clustering – Finding Related Posts

In the previous chapter, we have learned how to find classes or categories of individual data points. With a handful of training data items that were paired with their respective classes, we learned a model that we can now use to classify future data items. We called this supervised learning, as the learning was guided by a teacher; in our case the teacher had the form of correct classifications.

Let us now imagine that we do not possess those labels by which we could learn the classification model. This could be, for example, because they were too expensive to collect. What could we have done in that case?

Well, of course, we would not be able to learn a classification model. Still, we could find some pattern within the data itself. This is what we will do in this chapter, where we consider the challenge of a "question and answer" website. When a user browses our site looking for some particular information, the search engine will most likely...