📚 Syllabus & Course Curriculum
Data Science & Analytics
Module-by-module breakdown of Introduction to Reinforcement Learning, from foundations to a certified capstone project.
Outline
What is Reinforcement Learning? • Difference Between Supervised, Unsupervised, and Reinforcement Learning • Key Concepts: Agent, Environment, Actions, Rewards • Real-World Applications of Reinforcement Learning
Outline
Interaction Between Agent and Environment • Trial-and-Error Learning • Understanding Rewards and Penalties • Goal-Oriented Learning Behavior
Outline
Introduction to Policies and Decision Making • Value-Based Learning Concepts • Exploration vs Exploitation • Simple Examples of Learning Strategies
Outline
Reinforcement Learning in Games and Robotics • AI in Recommendation Systems and Automation • Decision-Making Systems in Business and Technology • Responsible Use of RL Systems
Outline
Introduction to Advanced Reinforcement Learning • Career Opportunities in AI and Robotics • Learning Path for Deep Learning and RL • Mini Learning Activity / Concept-Based Practice
Earn government-registered certification in Introduction to Reinforcement Learning
e-Certificate and e-Marksheet issued on successful completion.
