Reinforcement Learning with Python Explained for Beginners

In this 9 hr course, you will explore the fundamentals of Reinforcement Learning (RL) using Python. This beginner-friendly course covers essential concepts and practical applications, enabling you to start using RL techniques in real-world projects.

What I will be able to do after this course

• Understand the fundamentals of Reinforcement Learning (RL), including its motivation and applications.
• Learn the core concepts underlying Markov Decision Processes (MDPs), policies, and rewards.
• Gain experience implementing model-free methods like Temporal Difference Learning and Q-Learning.
• Develop skills in using OpenAI Gym to model, simulate, and solve RL problems including projects such as Frozenlake.
• Get hands-on insights and skills to design RL workflows and integrate them in practical domains with Python.

Course Instructor(s)

The instructors at AI Sciences are renowned for their expertise in data science and machine learning. With years of experience in research and teaching, they are committed to supporting learners at every level. They bring a hands-on, applied teaching approach that ensures you acquire practical skills and insights.

Who is it for?

This course is ideal for beginners in machine learning or data science who are curious about understanding and applying Reinforcement Learning. A passion for solving real-world problems using Python will help. You should aim to use RL to tackle practical challenges in various domains, improving your technical expertise.