Supervised learning is like a teacher-student relationship. The teacher (our model) learns from examples with labels provided by the student (our data). We split data into training (teaching) and testing (evaluating) sets.
Regression is like predicting someone's weight based on their height. Linear regression draws a line that best fits the data, helping us predict continuous outcomes.
Classification is like sorting things into different boxes. For example, sorting emails into "spam" or "not spam." Algorithms like logistic regression and decision trees help us make such decisions.