Supervised learning is a machine learning technique that involves training models with labeled data. Models in supervised learning must discover a mapping function to connect the input variable (X) to the output variable (Y) (Y).
To train the model, supervised learning requires supervision, like how a student learns in the presence of a teacher. There are two types of problems that can be solved with supervised learning: classification and regression.
Unsupervised learning is another machine learning method that uses unlabeled input data to discover patterns. Unsupervised learning aims to extract structure and patterns from unstructured data. There is no need for monitoring when learning unsupervised. Instead, it searches the data for patterns on its own.