Final answer:
Supervised learning involves using labeled data to predict outcomes, while unsupervised learning finds patterns in unlabeled data.
Step-by-step explanation:
Supervised learning involves having a labeled dataset where the algorithm learns from examples and predicts outcomes for new, unseen data. For example, in a supervised learning algorithm, a dataset of images with labels indicating whether they contain a cat or a dog would be used to train the algorithm to recognize cats and dogs in new images.
On the other hand, unsupervised learning does not have labeled data and the algorithm learns to find patterns and relationships in the data without specific guidance. An example of unsupervised learning is clustering, where the algorithm groups similar data points together based on their intrinsic characteristics.
In summary, supervised learning uses labeled data to predict outcomes, while unsupervised learning finds patterns in unlabeled data.