Final answer:
Supervised learning is trained on labeled examples, while unsupervised learning is trained on unlabeled data to discover patterns or structures.
Step-by-step explanation:
Supervised Learning: Supervised learning is a type of machine learning where the model is trained on labeled examples. The goal is to learn a mapping from input variables to the correct output variable. An example of supervised learning is training a model to classify emails as either spam or not spam based on labeled examples of spam and non-spam emails.
Unsupervised Learning: Unsupervised learning is a type of machine learning where the model is trained on unlabeled data. The goal is to discover patterns or structures in the data. An example of unsupervised learning is clustering similar customers together based on their purchasing behavior without any prior knowledge of their categories.