Linear regression and decision trees, the two workhorses
Linear regression is the simplest useful regression algorithm: it finds the best straight-line relationship between your input features and the number you're predicting. Given square footage, it might learn something like 'price goes up by roughly $180 per additional square foot.' It's fast, easy to explain to a non-technical stakeholder, and a genuinely strong baseline, plenty of real numeric prediction problems are close enough to linear that fancier models barely improve on it.
Decision trees take a completely different approach, and work for both classification and regression. A tree learns a series of yes-or-no questions about your features, 'is square footage over 2,000? is the neighborhood in zip code X?', splitting the data at each question until it reaches a final prediction. The appeal is interpretability: you can trace the exact chain of questions that led to any given prediction, which matters a lot when a decision needs to be explained or justified.