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)

Computing the Pearson correlation score

The Euclidean distance score is a good metric, but it has some shortcomings. Hence, the Pearson correlation score is frequently used in recommendation engines. The Pearson correlation score between two statistical variables is an index that expresses a possible linear relation between them. It measures the tendency of two numerical variables to vary simultaneously.

Getting ready

In this recipe, we will see how to compute the Pearson correlation score.

How to do it...

Let's take a look at how to compute the Pearson correlation...