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

What Is Dimensionality Reduction?

Dimensionality reduction is an important tool in any data scientist's toolkit, and due to its wide variety of use cases, is essentially assumed knowledge within the field. So, before we can consider reducing the dimensionality and why we would want to reduce it, we must first have a good understanding of what dimensionality is. To put it simply, dimensionality is the number of dimensions, features, or variables associated with a sample of data. Often, this can be thought of as a number of columns in a spreadsheet, where each sample is on a new row, and each column describes an attribute of the sample. The following table is an example:

Figure 4.1: Two samples of data with three different features

In the preceding table, we have two samples of data, each with three independent features or dimensions. Depending on the problem being solved, or the origin of this dataset, we may want to reduce the number of dimensions per...