This 11-video course delves into machine learning reinforcement learning concepts, including terms used to formulate problems and workflows, prominent use cases and implementation examples, and algorithms. Learners begin the course by examining what reinforcement learning is and the terms used to formulate reinforcement learning problems. Next, look at the differences between machine learning and reinforcement learning by using supervised and unsupervised learning. Explore the capabilities of reinforcement learning, by looking at use cases and implementation examples. Then learners will examine reinforcement learning workflow and reinforcement learning terms; reinforcement learning algorithms and their features; and the Markov Decision Process, its variants, and the steps involved in the algorithm. Take a look at the Markov Reward Process, focusing on value functions for implementing the Markov Reward Process, and also the capabilities of the Markov Decision Process toolbox and the algorithms that are implemented within it. The concluding exercise involves recalling reinforcement learning terms, describing implementation approaches, and listing the Markov Decision Process algorithms.