We can divide ML approaches into two techniques, as follows:

• Supervised learning is an approach based on the use of labeled data. Labeled data is a set of known data samples with corresponding known target outputs. Such a kind of data is used to build a model that can predict future outputs.
• Unsupervised learning is an approach that does not require labeled data and can search hidden patterns and structures in an arbitrary kind of data.

Let's have a look at each of the techniques in detail.