Hands-On Unsupervised Learning with Python

What this book covers

Chapter 1, Getting Started with Unsupervised Learning, offers an introduction to machine learning and data science from a very pragmatic perspective. The main concepts are discussed and a few simple examples are shown, focusing attention particularly on unsupervised problem structures.

Chapter 2, Clustering Fundamentals, begins our exploration of clustering algorithms. The most common methods and evaluation metrics are analyzed, together with concrete examples that show how to tune up the hyperparameters and assess performance from different viewpoints.

Chapter 3, Advanced Clustering, discusses some more complex algorithms. Many of the problems analyzed in Chapter 2, Clustering Fundamentals, are re-evaluated using more ...