As discussed in the Introduction section, the goal of any dimensionality reduction technique is to manage the sparsity of the dataset while keeping the useful information that is provided, so dimensionality reduction is typically an important pre-processing step used before a classification stage. Most dimensionality reduction techniques aim to complete this task using a process of **feature projection**, which adjusts the data from the higher dimensional space into a space with fewer dimensions to remove the sparsity from the data. Again, as a means of visualizing the projection process, consider a sphere in a 3D space. We can project the sphere into lower 2D space into a circle with some information loss (the value for the *z* coordinate) but retaining much of the information that describes its original shape. We still know the origin, radius, and manifold (outline) of the shape, and it is still very clear that it is a circle. So, if we were given...

#### 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