Book Image

Hands-On Unsupervised Learning with Python

By : Giuseppe Bonaccorso
Book Image

Hands-On Unsupervised Learning with Python

By: Giuseppe Bonaccorso

Overview of this book

Unsupervised learning is about making use of raw, untagged data and applying learning algorithms to it to help a machine predict its outcome. With this book, you will explore the concept of unsupervised learning to cluster large sets of data and analyze them repeatedly until the desired outcome is found using Python. This book starts with the key differences between supervised, unsupervised, and semi-supervised learning. You will be introduced to the best-used libraries and frameworks from the Python ecosystem and address unsupervised learning in both the machine learning and deep learning domains. You will explore various algorithms, techniques that are used to implement unsupervised learning in real-world use cases. You will learn a variety of unsupervised learning approaches, including randomized optimization, clustering, feature selection and transformation, and information theory. You will get hands-on experience with how neural networks can be employed in unsupervised scenarios. You will also explore the steps involved in building and training a GAN in order to process images. By the end of this book, you will have learned the art of unsupervised learning for different real-world challenges.
Table of Contents (12 chapters)

Advanced Clustering

In this chapter, we are continuing our exploration of more complex clustering algorithms that can be employed in non-convex tasks (that is, where, for example, K-means fails to obtain both cohesion and separation. A classical example is represented by interlaced geometries). We are also going to show how to apply a density-based algorithm to a complex dataset and how to properly select hyperparameters and evaluate performances according to the desired result. In this way, a data scientist can be ready to face different kinds of problems, excluding the less valuable solutions and focusing only on the most promising ones.

In particular, we are going to discuss the following topics:

  • Spectral clustering
  • Mean shift
  • Density-based Spatial Clustering of Applications with Noise (DBSCAN)
  • Additional evaluation metrics: Calinski-Harabasz index and cluster instability
  • ...