The Machine Learning Workshop - Second Edition

2. Unsupervised Learning – Real-Life Applications

Overview

This chapter explains the concept of clustering in machine learning. It explains three of the most common clustering algorithms, with a hands-on approximation to solve a real-life data problem. By the end of this chapter, you should have a firm understanding of how to create clusters out of a dataset using the k-means, mean-shift, and DBSCAN algorithms, as well as the ability to measure the accuracy of those clusters.

Introduction

In the previous chapter, we learned how to represent data in a tabular format, created features and target matrices, pre-processed data, and learned how to choose the algorithm that best suits the problem at hand. We also learned how the scikit-learn ...