Machine learning algorithms learn from data through a process called training, where they analyze large datasets to identify patterns and make predictions. Initially, the algorithm receives input features and corresponding labels during the training phase. It applies mathematical models to minimize the difference between its predictions and the actual outcomes by adjusting its internal parameters. Once trained, the model can then make predictions on new, unseen data. Over time, with more data and iterations, the accuracy of the predictions can improve, reflecting the algorithm's ability to glean insights from the training dataset.