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)

Shallow learning for spam detection

Spamming means sending large amounts of unwanted messages (usually commercial). It can be implemented through any medium, but the most commonly used are email and SMS. The main purpose of spamming is advertising, from the most common commercial offers to proposals for the sale of illegal material, such as pirated software and drugs without a prescription, and from questionable financial projects to genuine attempts at fraud.

Getting ready

In this recipe, we will use a logistic regression model for spam detection. To do this, a collection of labeled SMS messages collected for mobile phone spam research will be used. This dataset comprises of 5,574 real English non-encoded messages, tagged...