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Building Machine Learning Systems with Python

You're reading from   Building Machine Learning Systems with Python Expand your Python knowledge and learn all about machine-learning libraries in this user-friendly manual. ML is the next big breakthrough in technology and this book will give you the head-start you need.

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Product type Paperback
Published in Jul 2013
Publisher Packt
ISBN-13 9781782161400
Length 290 pages
Edition 1st Edition
Languages
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Table of Contents (20) Chapters Close

Building Machine Learning Systems with Python
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
1. Getting Started with Python Machine Learning FREE CHAPTER 2. Learning How to Classify with Real-world Examples 3. Clustering – Finding Related Posts 4. Topic Modeling 5. Classification – Detecting Poor Answers 6. Classification II – Sentiment Analysis 7. Regression – Recommendations 8. Regression – Recommendations Improved 9. Classification III – Music Genre Classification 10. Computer Vision – Pattern Recognition 11. Dimensionality Reduction 12. Big(ger) Data Where to Learn More about Machine Learning Index

Summary


That was a tough ride, from preprocessing over clustering to a solution that can convert noisy text into a meaningful concise vector representation that we can cluster. If we look at the efforts we had to do to finally be able to cluster, it was more than half of the overall task, but on the way, we learned quite a bit on text processing and how simple counting can get you very far in the noisy real-world data.

The ride has been made much smoother though, because of Scikit and its powerful packages. And there is more to explore. In this chapter we were scratching the surface of its capabilities. In the next chapters we will see more of its powers.

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