Python Course — Reinforcement Learning Python
A comprehensive guide to Reinforcement Learning, Markov Decision Processes, Q-Learning, applications using Python & OpenAI GYM
What You Will Learn
-
The importance of Reinforcement Learning (RL) in Data Science.
-
Key concepts from absolute basics with detailed explanations and examples in Python.
-
Practical explanations and live coding with Python.
-
Application of Probability Theory.
-
Markov Decision Processes.
Requirements
-
No prior knowledge required. You will start from the basics and gradually build your knowledge on the subject.
-
Willingness to learn and practice.
-
Knowledge of Python will be an added advantage.
Description
Reinforcement Learning (RL) has tremendous potential and is undoubtedly one of the most dynamic and stimulating fields of research in Artificial Intelligence. RL is considered a game-changer in Data Science, especially after witnessing the victories of AI agents AlphaGo Zero and OpenAI Five against top human champions. However, RL is not limited to games.
Progress in Reinforcement Learning, especially in recent years, has been remarkable. RL is now ubiquitous, ranging from resource management to chemistry, from healthcare to finance, and from Recommendation Systems to more advanced applications in stock prediction.
Because RL is goal-oriented learning, understanding RL is not only important but also essential in all fields of Data Science. This course will enable you to take your career to the next level, as it provides you with clear explanations of RL concepts and implementation in Data Science.
Since this course is a compilation of all the fundamentals, it will inspire you to advance and experience more than what you have learned. You will be given homework / assignments / activities at the end of each module, which will assess / (further build) your learning based on the concepts and methods you have previously learned. As the goal is to make you ready and running for implementation, many of these activities will be coding-based.
Data Science is undoubtedly a rewarding career. You can solve some of the most interesting problems, and you are rewarded with a good salary package. A core understanding of RL will empower you with more AI tools and ensure progressive career growth.
After successfully completing this course, you will be able to:
-
Understand how RL techniques are applied to solve real-world problems.
