Book Image

Applied Unsupervised Learning with Python

By : Benjamin Johnston, Aaron Jones, Christopher Kruger
Book Image

Applied Unsupervised Learning with Python

By: Benjamin Johnston, Aaron Jones, Christopher Kruger

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

Stochastic Neighbor Embedding (SNE)


Stochastic Neighbor Embedding (SNE) is one of a number of different methods that fall within the category of manifold learning, which aims to describe high-dimensional spaces within low-dimensional manifolds or bounded areas. At first thought, this seems like an impossible task; how can we reasonably represent data in two dimensions if we have a dataset with at least 30 features? As we work through the derivation of SNE, it is hoped that you will see how it is possible. Don't worry, we will not be covering the mathematical details of this process in great depth as it is outside of the scope of this chapter. Constructing an SNE can be divided into the following steps:

  1. Convert the distances between datapoints in the high-dimensional space to conditional probabilities. Say we had two points, and , in high-dimensional space, and we wanted to determine the probability () that would be picked as a neighbor of . To define this probability, we use a Gaussian...