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

1. Introduction to Clustering

Overview

Finding insights and value in data is the ambitious promise that has been seen in the rise of machine learning. Within machine learning, there are predictive approaches to understanding dense information in deeper ways, as well as approaches to predicting outcomes based on changing inputs. In this chapter, we will learn what supervised learning and unsupervised learning are, and how they are applied to different use cases. Once you have a deeper understanding of where unsupervised learning is useful, we will walk through some foundational techniques that provide value quickly.

By the end of this chapter, you will be able to implement k-means clustering algorithms using built-in Python packages and calculate the silhouette score.