Supervised Machine Learning, Simplified

Supervised learning is a type of machine learning where an algorithm is trained on a labeled dataset. In this approach, the dataset consists of input-output pairs, where the input data is associated with the correct output (label).

The goal of supervised learning is to learn a mapping from inputs to outputs, which can then be used to predict the output for new, unseen inputs.

There are 2 main types of supervised learning: classification and regression.

• An ML problem is a regression problem when the output variable is a numerical value, such as “weight” or “cost”.

• An ML problem is a classification problem when the output variable is a category, such as “large” or “small”