This chapter is the final installment in the micro-series on dimensionality reduction techniques and transformations. Our previous chapters in this series have described a number of different methods for reducing the dimensionality of a dataset as a means of either cleaning the data, reducing its size for computational efficiency, or for extracting the most important information available within the dataset. While we have demonstrated many methods for reducing high-dimensional datasets, in many cases, we are unable to reduce the number of dimensions to a size that can be visualized, that is, two or three dimensions, without excessively degrading the quality of the data. Consider the MNIST dataset that we used in *Chapter 5*, *Autoencoders*, which is a collection of digitized handwritten digits of the numbers 0 through 9. Each image is 28 x 28 pixels in size, providing 784 individual dimensions or features. If we were to reduce these 784 dimensions down to 2 or 3 for visualization...

#### 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