Book Image

Python Machine Learning Cookbook - Second Edition

By : Giuseppe Ciaburro, Prateek Joshi
Book Image

Python Machine Learning Cookbook - Second Edition

By: Giuseppe Ciaburro, Prateek Joshi

Overview of this book

This eagerly anticipated second edition of the popular Python Machine Learning Cookbook will enable you to adopt a fresh approach to dealing with real-world machine learning and deep learning tasks. With the help of over 100 recipes, you will learn to build powerful machine learning applications using modern libraries from the Python ecosystem. The book will also guide you on how to implement various machine learning algorithms for classification, clustering, and recommendation engines, using a recipe-based approach. With emphasis on practical solutions, dedicated sections in the book will help you to apply supervised and unsupervised learning techniques to real-world problems. Toward the concluding chapters, you will get to grips with recipes that teach you advanced techniques including reinforcement learning, deep neural networks, and automated machine learning. By the end of this book, you will be equipped with the skills you need to apply machine learning techniques and leverage the full capabilities of the Python ecosystem through real-world examples.
Table of Contents (18 chapters)

Using denoising autoencoders to detect fraudulent transactions

In Chapter 4, Clustering with Unsupervised Learning, we dealt with the topic of autoencoders. In the Autoencoders to reconstruct handwritten digit images recipe, there is a neural network whose purpose is to code its input into small dimensions, and the result obtained, to be able to reconstruct the input itself. The purpose of autoencoders is not simply to perform a sort of compression of the input or look for an approximation of the identity function; there are also techniques that allow us to direct the model (starting from a hidden layer of reduced dimensions) to give greater importance to some data properties.

Getting ready

In this recipe, we will train an...