Three fundamentally different ways a model actually learns — and the difference isn't the algorithm, it's what kind of feedback the learning process has access to.
These three terms get lumped together as if they were three flavors of the same thing — three algorithms you could swap in and out on the same dataset until one scores best. They aren't. Each one requires a genuinely different kind of data and feedback to function at all. Supervised learning needs examples that already come with the correct answer attached. Unsupervised learning has no correct answers to check against, only raw data to search for structure in. Reinforcement learning has neither — instead it has an agent, actions, and a reward signal that only arrives after the fact. Mixing these up isn't a matter of taste. It's the difference between a working approach and one that can't function on the data you actually have.