ML Basics: supervised, unsupervised and reinforcement learning

Supervised Algorithms

I’ll start with supervised, because I believe it’s the simplest one to understand. In supervised algorithms, you may not know the inner relations of the data you are processing, but you do know very well which is the output that you need from your model. For example:

Unsupervised Algorithms

With unsupervised algorithms, you still don’t know what you want to get out of the model yet. You probably suspect that there hast to be some kinds of relationships or correlation between the data you have, but data is too complex to try to guess. So in this cases you normalize your data into a format that makes sense to compare, and then let the model work it’s magic and try to find some of these relationships. One of the special characteristics of these models, is that while the model can suggest different ways to categorize or order your data, it’s up to you to make further research on these to unveil something useful. You can think of it as augmenting your data with information about inner relationships, but it’s up to you to make sense of this new information.

Reinforcement Learning

The reason why I included reinforcement learning in this article, is that one might think that “supervised” and “unsupervised” encompass every ML algorithm, and it actually does not. There are algorithms that aren’t supervised nor unsupervised, like Reinforcement Learning.



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