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Reinforcement Learning with Python Explained for Beginners

Reinforcement Learning with Python Explained for Beginners


Reinforcement Learning with Python Explained for Beginners Complete guide to Reinforcement Learning, Markov Decision Process, Q-Learning, applications using Python & OpenAI GYM


What you'll learn

  1. • The importance of Reinforcement Learning (RL) in Data Science.
  2. • The important concepts from the absolute beginning with detailed unfolding with examples in Python.
  3. • Practical explanation and live coding with Python.
  4. • Applications of Probability Theory.
  5. • Markov Decision Processes.


  1. • No prior knowledge is needed. You will start from the basics and gradually build your knowledge in the subject.
  2. • A willingness to learn and practice.
  3. • Knowledge of Python will be a plus.


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. The course is:

  1. · Easy to understand.
  2. · Descriptive.
  3. · Comprehensive.
  4. · Practical with live coding.
  5. · Rich with advanced and the most recently discovered RL models by the champions in this field.

This course is designed for beginners, although complex concepts are covered later.

  • As this course is a compilation of all the basics, it will inspire you to move forward and experience much more than what you have learned. You will be assigned homework/ tasks/ activities at the end of each module, which will assess / (further build) your learning based on the concepts and methods you have learned earlier on. Since the aim is to get you up and running with implementations, many of these activities will be coding based.
  • Data Science is unquestionably a rewarding career. You get to solve some of the most interesting problems, and you are rewarded with a handsome salary package. A core understanding of RL will empower you with more AI tools and ensure progressive career growth.
  • As we have already said, RL possesses immense potential. Don’t miss out on this opportunity to learn the advanced concepts and methodologies of RL at a highly competitive price. The tutorials are subdivided into 75+ short HD videos along with detailed code notebooks.

Teaching is our passion:

  • Our online tutorials have been created with the best possible expertise to help you in understanding the RL concepts clearly. We have taken great care to ensure the code base is up to date. We really want you to accomplish a strong basic understanding of RL before you move onward to the advanced version. The perks of this compelling course include high-quality video content, assessment questions, meaningful course material, course notes, and handouts. You can also approach our team whenever you have any queries.

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