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Machine Learning — Supervised, Unsupervised, and Reinforcement — Explanation with Example

🤖 Let's take an example of machine learning and see how it can be performed in three different ways — Supervised, Unsupervised, and Reinforcement.

 We want a program to be able to identify apple in pictures

Supervised Learning

You will create or use a model that takes a set of pictures of apple and it analyses the commonality in those pictures. Now when you show a new picture to the program, it will identify whether it has an apple or not. It can also provide details on how confident is the program about it.

Unsupervised Learning

In this method, you create or use a model that goes through some images and tries to group them as per the commonalities it observes such as color, shape, size, partern, etc. And now you can go through the groups and inform the program what to call them. So, you can inform the program about the group that is apple mostly. Next time you show a picture, it can tell if an apple is there or not.

Reinforcement Learning

Here the model you create or use will take a wild guess. Then you correct it if wrong, and now with this new information it analyses next image and you need to correct it again if wrong. This process goes on, and it gets better with every new image.

 These are the basic methodologies used by machine learning models.

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