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

The Organization of Hierarchy


Both the natural and human-made world contain many examples of organizing systems into hierarchies and why, for the most part, it makes a lot of sense. A common representation that is developed from these hierarchies can be seen in tree-based data structures. Imagine that you had a parent node with any number of child nodes that could subsequently be parent nodes themselves. By organizing concepts into a tree structure, you can build an information-dense diagram that clearly shows how things are related to their peers and their larger abstract concepts.

An example from the natural world to help illustrate this concept can be seen in how we view the hierarchy of animals, which goes from parent classes to individual species:

Figure 2.2: Navigating the relationships of animal species in a hierarchical tree structure

In Figure 2.2, you can see an example of how relational information between varieties of animals can be easily mapped out in a way that both saves space...