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

Summary


DBSCAN takes an interesting approach to clustering compared to k-means and hierarchical clustering. While hierarchical clustering can, in some aspects, be seen as an extension of the nearest neighbors approach seen in k-means, DBSCAN approaches the problem of finding neighbors by applying a notion of density. This can prove extremely beneficial when it comes to highly complex data that is intertwined in a complex fashion. While DBSCAN is very powerful, it is not infallible and can be seen as potentially overkill, depending on what your original data looks like.

Combined with k-means and hierarchical clustering, however, DBSCAN completes a strong toolbox when it comes to the unsupervised learning task of clustering your data. When faced with any problem in this space, it is worthwhile to compare the performance of each method and see which performs best.

With clustering explored, we will now move onto another key piece of rounding out your skills in unsupervised learning: dimensionality...