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

Summary


We made it! For a very noisy dataset, we built a classifier that suits part of our goal. Of course, we had to be pragmatic and adapt our initial goal to what was achievable. But on the way, we learned about the strengths and weaknesses of the nearest neighbor and logistic regression algorithms. We learned how to extract features, such as LinkCount, NumTextTokens, NumCodeLines, AvgSentLen, AvgWordLen, NumAllCaps, NumExclams, and NumImages, and how to analyze their impact on the classifier's performance.

But what is even more valuable is that we learned an informed way of how to debug badly performing classifiers. This will help us in the future to come up with usable systems much faster.

After having looked into the nearest neighbor and logistic regression algorithms, in the next chapter we will get familiar with yet another simple yet powerful classification algorithm: Naive Bayes. Along the way, we will also learn how to use some more convenient tools from Scikit-learn.