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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


Congratulations for sticking with us until the end! Together we have learned how Naive Bayes work and why they are not that naive at all. For training sets where we don't have enough data to learn all the niches in the class probability space, Naive Bayes do a great job of generalizing. We learned how to apply them to tweets and that cleaning the rough tweets' text helps a lot. Finally, we realized that a bit of "cheating" (only after we have done our fair share of work) is OK, especially, when it gives another improvement of the classifier's performance, as we have experienced with the use of SentiWordNet.

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