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)

Finding patterns in stock market data

Let's see how we can use unsupervised learning for stock market analysis. Since we don't know how many clusters there are, we'll use an algorithm called affinity propagation (AP) on the cluster. It tries to find a representative datapoint for each cluster in our data, along with measures of similarity between pairs of datapoints, and considers all our datapoints as potential representatives, also called exemplars, of their respective clusters.

Getting ready

In this recipe, we will analyze the stock market variations of companies over a specified duration. Our goal is to then find out what companies behave similarly in terms of their quotes over time.

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