Machine learning is the practice of letting a model find patterns in data rather than programming every rule by hand. In supervised learning, a model learns from labeled examples, such as past loan applications marked "repaid" or "defaulted," to predict outcomes on new, unlabeled cases. In unsupervised learning, there are no labels at all; the model instead finds structure, such as grouping customers into segments based on purchasing behavior. Within supervised learning, regression predicts a continuous number (like a house price), while classification predicts a category (like spam or not spam). You will also learn overfitting: when a model memorizes the quirks of its training data so closely that it performs brilliantly on that data but fails to generalize to new, unseen cases.