In this episode, we break down three of the most important learning paradigms in modern artificial intelligence: supervised learning, unsupervised learning, and reinforcement learning. Each of these approaches teaches machines in a fundamentally different way, and together they form the backbone of nearly every AI system we interact with today.

We start by exploring what it really means for an AI system to learn. Rather than receiving hand-crafted rules, machines discover patterns, structures, or strategies from data and experience. That shift changed the trajectory of AI and made learning-based systems central to the field.

From there, we walk through each paradigm in clear, simple terms:

• Supervised learning, where models learn from labelled examp ...