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Building Machine Learning Systems with Python

Building Machine Learning Systems with Python

3.9 (38)
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Building Machine Learning Systems with Python

Building Machine Learning Systems with Python

3.9 (38)

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

Chapter 9. Classification III – Music Genre Classification

So far, we have had the luxury that every training data instance could easily be described by a vector of feature values. In the Iris dataset, for example, the flowers are represented by vectors containing values for the length and width of certain aspects of a flower. In the text-based examples, we could transform the text into a bag-of-words representation and manually craft our own features that captured certain aspects of the texts.

It will be different in this chapter, however, when we try to classify songs by their genre. Or how would we, for instance, represent a three-minute long song? Should we take the individual bits of its MP3 representation? Probably not, since treating it like text and creating something such as a "bag of sound bites" would certainly be way too complex. Somehow, we will nevertheless have to convert a song into a number of values that describes it sufficiently.

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Building Machine Learning Systems with Python
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