Supervised Learning

Learning under Supervision

As the name suggests, Supervised Learning is learning under supervision. The model is trained with the user provided input and output. While it is being trained, the algorithm monitors the model being generated and continually measures the deviation and tries to correct it. This is generally a heavy load on the processor. But often leads to wonderful results.
There are two major types of problems in supervised learning. Regression and Classification. Regression deals with continuous data and classification (as the name suggests) deals with classification of data into discrete output values.

Supervised Learning Algorithms

There are several important algorithms that help us with Supervised Learning. There are no good or bad algorithms. Each has its own intricacy that makes it good for a particular scenario. One has to understand the given scenario and then use the appropriate algorithm.

Points to note

Most machine learning algorithms have a ready made code available in a choice of Python libraries. That does not require any more effort. But that does not make machine learning any simpler. The crux of the problem is choosing the right algorithm for the data available. Understanding the pitfalls and ensuring we steer clear of those.
There are many more aspects that become more relevant as we move further. We will check them in detail in the section on deep learning