There are several distinct types of learning tasks that are partially defined by the type of data that they work on. Based on this, we can divide learning tasks into two broad categories:
- Unsupervised learning: Data is unlabeled so the algorithm must infer a relationship between variables or by finding clusters of similar variables
- Supervised learning: Uses a labeled dataset to build an inferred function that can be used to predict the label of an unlabeled sample
Whether the data is labeled or not has a predetermining effect on the way a learning algorithm is built.