There are numerous techniques for creating algorithms that are capable of learning and adapting over time. Broadly speaking, we can organize these algorithms into one of three categories – supervised learning, classification and regression.
Supervised learning refers to situations in which each instance of input data is accompanied by a desired or target value for that input. When the target values are a set of finite discrete categories, the learning task is often known as a classification problem. When the targets are one or more continuous variables, the task is called regression.
Article Link: https://blog.vectra.ai/blog/how-algorithms-learn-and-adapt