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


In this chapter, we were introduced to t-Distributed Stochastic Neighbor Embeddings as a means of visualizing high-dimensional information that may have been produced from prior processes such as PCA or autoencoders. We discussed the means by which t-SNEs produce this representation and generated a number of them using the MNIST and Wine datasets and scikit-learn. In this chapter, we were able to see some of the power of unsupervised learning because PCA and t-SNE were able to cluster the classes of each image without knowing the ground truth result. In the next chapter, we will build on this practical experience as we look into the applications of unsupervised learning, including basket analysis and topic modeling.