What type of machine learning involves algorithms learning from labeled datasets?

Supervised learning is a branch of machine learning where algorithms are trained on labeled datasets. In this context, "labeled datasets" mean that each training example in the dataset is paired with an output label. The algorithm uses these input-output pairs during training to learn the mapping between the input data and the corresponding labels, allowing it to make predictions or classify new, unseen data.

During training, the model adjusts its parameters to minimize the difference between its predictions and the actual labels. This feedback loop is what enables the model to learn effectively. For instance, if a supervised learning model is trained on a dataset of images of cats and dogs, each image would be labeled as "cat" or "dog." The model learns to identify features of the images that correspond to these labels, enabling it to categorize new images accurately.

The other types of learning mentioned do not rely on labeled data in the same way. Unsupervised learning works with data that has no labels, seeking to find patterns or groupings in the data without supervision. Reinforcement learning focuses on agents that take actions in an environment, learning through feedback in the form of rewards or penalties rather than through labeled data. Deep learning, while it can be used as a technique within supervised learning, does