Supervised learning is a technique where we have a dataset with known outcomes or labels, and we train our model using this data. Essentially, we're providing the model with a "teacher" that helps it understand the relationship between input features and output labels. The model's primary goal is to generalize this understanding and make accurate predictions for unseen data. An example of supervised learning would be predicting house prices based on features like the number of bedrooms, location, and square footage.
On the other hand, unsupervised learning deals with datasets that don't have any labels or known outcomes. The model's goal is to identify patterns, relationships, or structure within the data without any guidance. One common application of unsupervised learning is clustering, where the model groups similar data points together. For instance, we could use unsupervised learning to segment customers based on their purchasing behavior without knowing any specific group labels beforehand.