Reinforcement Learning

Create intelligent systems that take the best path possible using reinforcement learning

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Key Highlights

  • 15 Hours of Live Instructor-led Classes
  • 5 sessions of 3 hours each on Weekends
  • Live Project on real-life Case Studies
  • Practical Assignments
  • Lifetime Access to Learning Management System
  • 24x7 Expert Support
  • Course Completion Certificate
  • Online Forum for Discussions
  • Cloud lab for hands-on experience

Course Price

$249.00 $499.00

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Available Courses Delivery

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Course Overview

Reinforcement learning is a part of machine learning. In this kind of training, the intelligent system learns to take the best path by trying all the possible paths. There is no training dataset involved in this machine learning process. In this course, the candidates will learn primarily about the Markov decision processes, dynamic programming, bandit algorithms, and more.

Course Objectives

  • Teach the basics of reinforcement learning and best-path deduction process
  • Familiarize learner with the OpenAI gym
  • Teach about the Markov Decision Process and Bandit Algorithms
  • Impart understanding on dynamic programming, temporal difference, and Deep Q Learning

Career Benefits

  • Better skills as a machine learning engineer
  • Better opportunities in the AI and machine learning industry
  • Work on advanced and promising AI projects that use machine learning
  • Better remuneration with new machine learning skills
  • Improve business processes using created/enhanced intelligent system


  • None, but knowing the fundamentals of AI, machine learning, neural networks, deep learning, and Python programming will make the course easier.

Who should take up?

  • Software Developers who want to explore machine learning
  • Python programmers interested in AI
  • Inexperienced machine learning engineers
  • Freshers looking to make a career in machine learning and AI

Course Content

  • Branches of Machine Learning
  • What is Reinforcement Learning?
  • The Reinforcement Learning Process
  • Elements of Reinforcement Learning
  • RL Agent Taxonomy
  • Reinforcement Learning Problem
  • Introduction to OpenAI Gym
  • Bandit Algorithms
  • Markov Process
  • Markov Reward Process
  • Markov Decision Process
  • Introduction to Dynamic Programming
  • Dynamic Programming Algorithms
  • Monte Carlo Methods
  • Temporal Difference Learning Methods
  • Policy Gradients
  • Policy Gradients using TensorFlow
  • Deep Q learning
  • Q learning with replay buffers, target networks, and CNN
  • The aim of this module is to provide you hands-on experience in Reinforcement Learning.


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