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Building Machine Learning Systems with Python

You're reading from  Building Machine Learning Systems with Python

Product type Book
Published in Jul 2013
Publisher Packt
ISBN-13 9781782161400
Pages 290 pages
Edition 1st Edition
Languages

Table of Contents (20) Chapters

Building Machine Learning Systems with Python
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
1. Getting Started with Python Machine Learning 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

Chapter 3. Clustering – Finding Related Posts

In the previous chapter, we have learned how to find classes or categories of individual data points. With a handful of training data items that were paired with their respective classes, we learned a model that we can now use to classify future data items. We called this supervised learning, as the learning was guided by a teacher; in our case the teacher had the form of correct classifications.

Let us now imagine that we do not possess those labels by which we could learn the classification model. This could be, for example, because they were too expensive to collect. What could we have done in that case?

Well, of course, we would not be able to learn a classification model. Still, we could find some pattern within the data itself. This is what we will do in this chapter, where we consider the challenge of a "question and answer" website. When a user browses our site looking for some particular information, the search engine will most likely...

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