Reinforcement learning is one of the most exciting and rapidly growing fields in artificial intelligence, powering innovations in robotics, autonomous systems, gaming, and financial trading. Artificial Intelligence: Reinforcement Learning in Python – From Bandits to Trading Bots provides a comprehensive and practical approach to mastering reinforcement learning algorithms, beginning with the basics and progressing to real-world applications.

The course starts with the multi-armed bandit problem, introducing the critical explore–exploit tradeoff and popular algorithms like epsilon-greedy, UCB1, and Thompson Sampling. You will then advance to high-level reinforcement learning concepts, learning to design and train agents through intuitive examples such as Tic-Tac-Toe. From there, the course explores the core mathematical framework of Markov Decision Processes (MDPs), giving you the foundation needed to understand and implement advanced reinforcement learning models.

You will gain hands-on coding experience with dynamic programming, Monte Carlo methods, and temporal difference learning, building step-by-step solutions that prepare you to tackle more complex problems. Function approximation techniques are introduced to help scale reinforcement learning beyond small state spaces, bridging the gap between theory and application.

The highlight of the course is a fully coded stock trading project where you will apply Q-learning and reinforcement learning models to financial data. This project not only demonstrates the practical power of reinforcement learning but also provides you with a strong portfolio piece to showcase your AI skills.

By the end of the course, you will have mastered reinforcement learning from its core principles to advanced applications, with the ability to build AI-driven agents and apply them to domains such as trading, automation, and real-world decision-making systems.