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Python Machine Learning By Example

You're reading from   Python Machine Learning By Example Implement machine learning algorithms and techniques to build intelligent systems

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Product type Paperback
Published in Feb 2019
Publisher Packt
ISBN-13 9781789616729
Length 382 pages
Edition 2nd Edition
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Author (1):
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Yuxi (Hayden) Liu Yuxi (Hayden) Liu
Author Profile Icon Yuxi (Hayden) Liu
Yuxi (Hayden) Liu
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Table of Contents (15) Chapters Close

Preface 1. Section 1: Fundamentals of Machine Learning FREE CHAPTER
2. Getting Started with Machine Learning and Python 3. Section 2: Practical Python Machine Learning By Example
4. Exploring the 20 Newsgroups Dataset with Text Analysis Techniques 5. Mining the 20 Newsgroups Dataset with Clustering and Topic Modeling Algorithms 6. Detecting Spam Email with Naive Bayes 7. Classifying Newsgroup Topics with Support Vector Machines 8. Predicting Online Ad Click-Through with Tree-Based Algorithms 9. Predicting Online Ad Click-Through with Logistic Regression 10. Scaling Up Prediction to Terabyte Click Logs 11. Stock Price Prediction with Regression Algorithms 12. Section 3: Python Machine Learning Best Practices
13. Machine Learning Best Practices 14. Other Books You May Enjoy

Mining the 20 Newsgroups Dataset with Clustering and Topic Modeling Algorithms

In the previous chapter, we went through a text visualization using t-SNE. T-SNE, or any dimensionality reduction algorithm, is a type of unsupervised learning. Moving forward in this chapter, we will be continuing our unsupervised learning journey, specifically focusing specifically on clustering and topic modeling. We will start with how unsupervised learning learns without guidance and how it is good at discovering hidden information underneath data. Then we will talk about clustering as an important branch of unsupervised learning, which identifies different groups of observations from data. For instance, clustering is useful for market segmentation where consumers of similar behaviors are grouped into one segment for marketing purposes. We will perform clustering on the 20 newsgroups text dataset...

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