Book Image

The Unsupervised Learning Workshop

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

The Unsupervised Learning Workshop

By: Aaron Jones, Christopher Kruger, Benjamin Johnston

Overview of this book

Do you find it difficult to understand how popular companies like WhatsApp and Amazon find valuable insights from large amounts of unorganized data? The Unsupervised Learning Workshop will give you the confidence to deal with cluttered and unlabeled datasets, using unsupervised algorithms in an easy and interactive manner. The book starts by introducing the most popular clustering algorithms of unsupervised learning. You'll find out how hierarchical clustering differs from k-means, along with understanding how to apply DBSCAN to highly complex and noisy data. Moving ahead, you'll use autoencoders for efficient data encoding. As you progress, you’ll use t-SNE models to extract high-dimensional information into a lower dimension for better visualization, in addition to working with topic modeling for implementing natural language processing (NLP). In later chapters, you’ll find key relationships between customers and businesses using Market Basket Analysis, before going on to use Hotspot Analysis for estimating the population density of an area. By the end of this book, you’ll be equipped with the skills you need to apply unsupervised algorithms on cluttered datasets to find useful patterns and insights.
Table of Contents (11 chapters)
Preface

Summary

In this chapter, we were introduced to t-distributed SNEs 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 look at 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 by looking into applications of unsupervised learning, including basket analysis and topic modeling.