Learn AI Game Development using Python

About this course

Artificial intelligence (AI) is transforming industries and everyday life. From self-driving cars to personalized recommendations on streaming services, AI is at the heart of innovations that are shaping the future. Reinforcement learning (RL) is a pivotal area within AI that focuses on how agents can learn to make decisions by interacting with their environment. This paradigm is particularly powerful for tasks where the optimal solution is not immediately obvious and must be discovered through trial and error.One of the most critical aspects of learning AI and reinforcement learning (RL) is the ability to bridge the gap between theoretical concepts and practical applications. This course emphasizes a hands-on approach, ensuring that you not only understand the underlying theories but also know how to implement them in real-world scenarios. By working on practical projects, you will develop a deeper comprehension of how AI algorithms can solve complex problems and create intelligent systems.Course Structure and TopicsDynamic Programming (DP):Introduction to DP: Understand the basic principles and applications of dynamic programming.Q-learning:Fundamentals of Q-learning: Learn the theory behind Q-learning, a model-free RL algorithm.Value Function and Policies: Understand how agents learn to map states to actions to maximize cumulative reward.Implementation: Hands-on projects using TensorFlow and Keras to build and train Q-learning agents.Deep Q-learning:Integrating Deep Learning with RL: Learn how deep neural networks can enhance Q-learning.Handling High-dimensional Spaces: Techniques to manage complex environments and large state spaces.Practical Pr

Course objectives

• bridge theoretical concepts of AI with practical applications
• equip students to solve complex problems using AI algorithms