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)

Building machine learning pipelines

The scikit-learn library is used to build machine learning pipelines. When we define the functions, the library will build a composed object that makes the data go through the entire pipeline. This pipeline can include functions, such as preprocessing, feature selection, supervised learning, and unsupervised learning.

Getting ready

In this recipe, we will be building a pipeline to take the input feature vector, select the top k features, and then classify them using a random forest classifier.

How to do it...

Let's take a look at...