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Building Machine Learning Systems with Python

Building Machine Learning Systems with Python

By : Willi Richert , Luis Pedro Coelho
3.9 (38)
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Building Machine Learning Systems with Python

Building Machine Learning Systems with Python

3.9 (38)
By: Willi Richert , Luis Pedro Coelho

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)
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Building Machine Learning Systems with Python
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
2
Index

Summary


In this chapter, we discussed a more advanced form of grouping documents, which is more flexible than simple clustering as we allow each document to be present in more than one group. We explored the basic LDA model using a new package, gensim, but were able to integrate it easily into the standard Python scientific ecosystem.

Topic modeling was first developed and is easier to understand in the case of text, but in Chapter 10, Computer Vision – Pattern Recognition, we will see how some of these techniques may be applied to images as well. Topic models are very important in most of modern computer vision research. In fact, unlike the previous chapters, this chapter was very close to the cutting edge of research in machine learning algorithms. The original LDA algorithm was published in a scientific journal in 2003, but the method that gensim uses to be able to handle Wikipedia was only developed in 2010, and the HDP algorithm is from 2011. The research continues and you can find many...

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Building Machine Learning Systems with Python
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