This video explores three main types of learning in AI: supervised, unsupervised, and reinforcement learning. Supervised learning involves using labeled training data (input and output) to create a model that can predict outcomes for new inputs. It's like having a teacher (the training data) who provides instructions. Unsupervised learning, on the other hand, uses unlabeled data to find patterns and structures within it. This is similar to discovering relationships in data without prior knowledge. Reinforcement learning focuses on training an agent to make decisions by rewarding desired actions and penalizing undesired ones. Think of it as learning through trial and error with rewards and punishments. The video uses real-life examples like the PK movie and election exit polls to illustrate the differences between these learning types.