Ever since 2014, Reinforcement Learning has taken the Machine Learning world by storm with successes like Atari DQN, AlphaGo and OpenAI Five. This book, now in its second edition, has practical Reinforcement Learning projects like stock trading, chatbots, web automation and robotic control. It also includes topics hardly found in other books e.g. AlphaGo Zero implementation, multi-agent learning and state-of-the-art model based techniques.

One of the best book on mastering networking in Python, written by an author who has worked as a network engineer in top companies. The third edition came out in January, 2020. This new edition is completely revised and updated to work with Python 3. In addition to new chapters on network data analysis with ELK stack (Elasticsearch, Logstash, Kibana, and Beats) and Azure Cloud Networking, it includes updates on using newer libraries such as pyATS and Nornir, as well as Ansible 2.8.

Genetic algorithms is machine learning inspired by Darwinian evolution. This method is slowly gaining popularity because of its speed and simplicity. In this book, you will learn genetic algorithms to solve search, optimization, and AI-related tasks, and improve machine learning models using Python libraries such as DEAP, scikit-learn, and NumPy

This book is about writing better Python code. It introduces big ideas that are rarely covered in other books, such as separation of concerns, encapsulation ,abstraction, extension and flexibility. All the usual suspects like performance and testing are also covered. If you are thinking of becoming a system architect, maybe this is a good book to get started.

This is a book on creating music using Generative Artificial Intelligence. Written by a generative music artist who has worked with many bands, it explains how to use Tensorflow and Magenta to generate music automatically.