Machine learning algorithms learn from data through a process called training. During training, algorithms analyze input data, identifying patterns and relationships. This usually involves adjusting model parameters to minimize errors in predictions. Supervised learning uses labeled data, while unsupervised learning finds hidden structures in unlabeled data. Reinforcement learning involves learning through feedback from actions taken in an environment. Gradually, with more data, models become better at making accurate predictions, allowing them to generalize well to new data.