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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 2. Learning How to Classify with Real-world Examples

Can a machine distinguish between flower species based on images? From a machine learning perspective, we approach this problem by having the machine learn how to perform this task based on examples of each species so that it can classify images where the species are not marked. This process is called classification (or supervised learning), and is a classic problem that goes back a few decades.

We will explore small datasets using a few simple algorithms that we can implement manually. The goal is to be able to understand the basic principles of classification. This will be a solid foundation to understanding later chapters as we introduce more complex methods that will, by necessity, rely on code written by others.

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Building Machine Learning Systems with Python
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