The main difference is how the machine learning system receives information and learns from it.
- Supervised learning: The model learns from labeled examples where the correct answer is already provided. For example, training a model with emails labeled “spam” or “not spam.” It is commonly used for classification and prediction.
- Unsupervised learning: The model receives data without predefined labels and tries to discover patterns or structures on its own. For example, a retailer could use clustering to group customers based on purchasing behavior.
- Reinforcement learning: An agent learns by interacting with an environment. It takes actions and receives rewards or penalties, gradually learning a strategy that maximizes its long-term reward. Game-playing AI and robotic control are common examples.
| Type | Training signal | Typical goal |
|---|
| Supervised | Labeled data | Predict the correct output |
| Unsupervised | Unlabeled data | Discover patterns or structure |
| Reinforcement | Rewards and penalties | Learn effective actions |
A simple way to remember it is: supervised learning learns from answers, unsupervised learning discovers patterns, and reinforcement learning learns from consequences.
