Reinforcement Learning with Python Explained for Beginners
Complete guide to Reinforcement Learning, Markov Decision Process, Q-Learning, applications using Python & OpenAI GYM
Created by AI Sciences Team | 9 hours on-demand video course
Reinforcement Learning (RL) possesses immense potential and is doubtless one of the most dynamic and stimulating fields of research in Artificial Intelligence. RL is considered as a game-changer in Data Science, particularly after observing the winnings of AI agents AlphaGo Zero and OpenAI Five against top human champions. However, RL is not restricted to games.
The progress in Reinforcement Learning, especially during the last few years, has been sensational. RL is everywhere now, ranging from resource management to chemistry, from healthcare to finance, and from Recommender Systems to more advanced applications in stock prediction.
Since RL is goal-oriented learning, an understanding of RL is not only vital but also indispensable in all the fields of Data Science. This course will enable you to take your career to the next level, as it presents you with a clear explanation of the concepts and implementations of RL in Data Science.
The course ‘Reinforcement Learning, Theory and Practice in Python’ provides you with an opportunity for innovative, independent learning. The course focuses on the practical applications of RL and includes a hands-on project.
What you’ll learn
- The importance of Reinforcement Learning (RL) in Data Science.
- The important concepts from the absolute beginning with detailed unfolding with examples in Python.
- Practical explanation and live coding with Python.
- Applications of Probability Theory.
- Markov Decision Processes.
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