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)

Getting Started with Unsupervised Learning

In this chapter, we are going to introduce fundamental machine learning concepts, assuming that you have some basic knowledge of statistical learning and probability theory. You'll learn about the uses of machine learning techniques and the logical process that improves our knowledge about both nature and the properties of a dataset. The purpose of the entire process is to build descriptive and predictive models the can support business decisions.

Unsupervised learning aims to provide tools for data exploration, mining, and generation. In this book, you'll explore different scenarios with concrete examples and analyses, and you'll learn how to apply fundamental and more complex algorithms to solve specific problems.

In this introductory chapter, we are going to discuss:

  • Why do we need machine learning?
  • Descriptive, diagnostic, predictive, and prescriptive analyses
  • Types of machine learning
  • Why are we using Python?