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The Unsupervised Learning Workshop
book

The Unsupervised Learning Workshop

by Aaron Jones, Christopher Kruger, Benjamin Johnston, Richard Brooker, John Wesley Doyle, Priyanjit Ghosh, Sani Kamal, Ashish Pratik Patil, Philip Solomon, Geetank Raipuria
July 2020
Intermediate to advanced content levelIntermediate to advanced
550 pages
9h 58m
English
Packt Publishing
Content preview from The Unsupervised Learning Workshop

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.

Introduction

In the previous chapter, we discussed clustering algorithms and how they can be helpful to find underlying meaning in large volumes of data. This chapter investigates the use of different feature sets (or spaces) in our unsupervised ...

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Publisher Resources

ISBN: 9781800200708Supplemental Content