This course explores two of the most dynamic and in-demand areas of machine learning: Deep Learning and Reinforcement Learning. You’ll begin by diving into Deep Learning, a subset of machine learning that powers many modern AI systems—from image and speech recognition to natural language processing.
The course introduces the foundational theory behind neural networks, explaining how they are structured, how they learn, and why they are effective for handling complex, high-dimensional data. You’ll gain hands-on experience building and training neural networks and explore modern deep learning architectures that are widely used in industry.
You’ll then move on to Reinforcement Learning (RL), a rapidly growing field in AI that focuses on decision-making and learning through interaction with an environment. Reinforcement Learning has practical applications in research, across robotics, autonomous systems, game-playing AI, and beyond. You’ll learn the key concepts of RL, including agents, environments, actions, rewards, and policies, and how these elements work together in the learning process.
By the end of this course, you will have developed a strong understanding of how Deep Learning and Reinforcement Learning differ from traditional supervised and unsupervised learning approaches. You’ll also be able to design, build, and interpret basic deep learning models, as well as grasp the foundational principles that drive reinforcement learning strategies.
