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in Machine Learning by (19k points)

I recently studied about supervised learning and unsupervised learning. From theory, I know that supervised means getting the information from labeled datasets and unsupervised means clustering the data without any labels given.

But, the problem is I always get confused to identify whether the given example is supervised learning or unsupervised learning during my studies.

Can anyone please give a real-life example?

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Supervised learning:

  • Hundreds of photos with information about what is on them and then you train a model to recognize new photos.

  • You have a bunch of molecules and information about which are drugs and you train a model to predict whether a new molecule is also a drug.

Unsupervised learning:

  • You have some photos of 6 people but without information about who is on which one and you want to divide this dataset into 6 piles, each with the photos of one individual.

  • You have some molecules, part of them are drugs and some parts are not but you do not know which you want the algorithm to discover the drugs.

If you wish to learn about the types, then visit this Types of Machine Learning Tutorial.

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