Supervised Learning
A task is supervised if you are using labeled data. We use the term labeled to refer to data that already contains the solutions, called labels.
Unsupervised Learning
A task is considered to be unsupervised if you are using unlabeled data. This means you don't need to provide the model with any kind of label or solution while the model is being trained. Unsupervised learning involves using data that doesn't have a label. One common task is called clustering. Clustering helps to determine if there are any naturally occurring groupings in the data.
In supervised learning, there are two main identifiers that you will see in machine learning:
A categorical label has a discrete set of possible values. In a machine learning problem in which you want to identify the type of flower based on a picture, you would train your model using images that have been labeled with the categories of the flower that you want to identify.
A continuous (regression) label does not have a discrete set of possible values, which often means you are working with numerical data.
Clustering, an unsupervised learning task that helps to determine if there are any naturally occurring groupings in the data.
A categorical label has a discrete set of possible values, such as "is a cat" and "is not a cat".
A continuous (regression) label does not have a discrete set of possible values, which means there are potentially possibly an unlimited number of possibilities.
Discrete: A term taken from statistics referring to an outcome taking on only a finite number of values (such as days of the week).
A label refers to data that already contains the solution.
Using unlabeled data means you don't need to provide the model with any kind of label or solution while the model is being trained.