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Updated in [August 18th, 2023]

Skills and Knowledge:
This course will provide learners with the skills and knowledge to understand and apply reinforcement learning techniques. Learners will gain an understanding of the fundamentals of reinforcement learning, including the concepts of reward, exploration, and exploitation. They will also learn how to code a neural network in Python and apply Deep Q Learning to the NChain game. Additionally, learners will gain an understanding of how reinforcement learning can be applied to other games, such as chess, Go, and Atari.
Professional Growth:
This course provides an introduction to machine learning and reinforcement learning, which are both important components of professional growth. By teaching students to code a neural network in Python, they will gain a better understanding of how these technologies work and how they can be applied to real-world problems. Additionally, the course will provide an introduction to Deep Q Learning, a powerful technique developed by Google DeepMind that has been used to teach neural networks to play chess, Go, and Atari. This knowledge can be used to develop more advanced machine learning applications and help professionals stay up-to-date with the latest advancements in the field.
Further Education:
This course is suitable for preparing further education in machine learning. It introduces the concept of reinforcement learning and teaches students to code a neural network in Python. The course also covers Deep Q Learning, a revolutionary technique invented by Google DeepMind to teach neural networks to play chess, Go and Atari. This course provides a great foundation for further study in machine learning.

Course Syllabus

Introduction

Creating your Agent and Environment

Q Learning

Neural Networks

Deep Q Learning