As we described previously, PCA is a commonly used and very effective dimensionality reduction technique, which often forms a pre-processing stage for a number of machine learning models and techniques. For this reason, we will dedicate this section of the book to looking at PCA in more detail than any of the other methods. PCA reduces the sparsity in the dataset by separating the data into a series of components where each component represents a source of information within the data. As its name suggests, the first component produced in PCA, **the principal component** comprises the majority of information or variance within the data. The principal component can often be thought of as contributing the most amount of interesting information in addition to the mean. With each subsequent component, less information, but more subtlety, is contributed to the compressed data. If we consider all of these components together, there will be no benefit from using PCA, as the original dataset will...

#### Applied Unsupervised Learning with Python

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#### Applied Unsupervised Learning with Python

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#### Overview of this book

Unsupervised learning is a useful and practical solution in situations where labeled data is not available.
Applied Unsupervised Learning with Python guides you in learning the best practices for using unsupervised learning techniques in tandem with Python libraries and extracting meaningful information from unstructured data. The book begins by explaining how basic clustering works to find similar data points in a set. Once you are well-versed with the k-means algorithm and how it operates, you’ll learn what dimensionality reduction is and where to apply it. As you progress, you’ll learn various neural network techniques and how they can improve your model. While studying the applications of unsupervised learning, you will also understand how to mine topics that are trending on Twitter and Facebook and build a news recommendation engine for users. Finally, you will be able to put your knowledge to work through interesting activities such as performing a Market Basket Analysis and identifying relationships between different products.
By the end of this book, you will have the skills you need to confidently build your own models using Python.

Table of Contents (12 chapters)

Applied Unsupervised Learning with Python

Preface

Free Chapter

Introduction to Clustering

Hierarchical Clustering

Neighborhood Approaches and DBSCAN

Dimension Reduction and PCA

Autoencoders

t-Distributed Stochastic Neighbor Embedding (t-SNE)

Topic Modeling

Market Basket Analysis

Hotspot Analysis

Appendix

Customer Reviews