The kind of machine learning that involves learning from labeled data is known as supervised learning. In this approach, a model is trained on a dataset containing input features paired with known outcomes, allowing it to make predictions or classifications based on new, unseen data. Supervised learning is widely used for tasks such as image recognition, spam detection, and fraud detection. By leveraging labeled data, models can achieve high levels of accuracy and performance, making them valuable in various applications.