This eagerly anticipated second edition of the popular Python Machine Learning Cookbook, Second Edition, 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 an 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 for fields including reinforcement learning, deep neural networks, and automated machine learning.
By the end of this book, you will be equipped, through real-world examples, with the skills you need to apply machine learning techniques, and will be able to leverage the full capabilities of the Python ecosystem.
