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The Unsupervised Learning Workshop

You're reading from   The Unsupervised Learning Workshop Get started with unsupervised learning algorithms and simplify your unorganized data to help make future predictions

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Product type Paperback
Published in Jul 2020
Publisher Packt
ISBN-13 9781800200708
Length 550 pages
Edition 1st Edition
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Authors (3):
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Benjamin Johnston Benjamin Johnston
Author Profile Icon Benjamin Johnston
Benjamin Johnston
Christopher Kruger Christopher Kruger
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Christopher Kruger
Aaron Jones Aaron Jones
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Aaron Jones
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Toc

Table of Contents (11) Chapters Close

Preface
1. Introduction to Clustering 2. Hierarchical Clustering FREE CHAPTER 3. Neighborhood Approaches and DBSCAN 4. Dimensionality Reduction Techniques and PCA 5. Autoencoders 6. t-Distributed Stochastic Neighbor Embedding 7. Topic Modeling 8. Market Basket Analysis 9. Hotspot Analysis Appendix

4. Dimensionality Reduction Techniques and PCA

Overview

In this chapter, we will apply dimension reduction techniques and describe the concepts behind principal components and dimensionality reduction. We will apply Principal Component Analysis (PCA) when solving problems using scikit-learn. We will also compare manual PCA versus scikit-learn. By the end of this chapter, you will be able to reduce the size of a dataset by extracting only the most important components of variance within the data.

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