This chapter is the first of a series of three chapters that investigate the use of different feature sets (or spaces) in our unsupervised learning algorithms, and we will start with a discussion around dimensionality reduction, specifically, PCA. We will then extend upon our understanding of the benefits of the different feature spaces through an exploration of two independently powerful machine learning architectures in neural network-based auto-encoders. Neural networks certainly have a well-deserved reputation for being powerful models in supervised learning problems, and, through the use of an autoencoder stage, have been shown to be sufficiently flexible for their application to unsupervised learning problems. Finally, we will build on our neural network implementation and dimensionality reduction as we cover t-distributed nearest neighbors in the final chapter of this micro-series.

#### 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