In unsupervised learning, the dataset is unlabeled, i.e., There is no output value associated with an input variable in the dataset. We need to find the hidden patterns and insights from the given dataset.
In the previous example, we learned about one apple but didn’t know there were other apples too. But on seeing other apples, we would be able to identify them on the basis of the characteristics which we identified. So here, no one told us that these are apples too but we figured it out on our own.
So suppose we have pictures of apples, bananas, and oranges. We can give it to our model and use unsupervised learning to group all the fruits into three groups i.e. apples, bananas, and oranges.
Note that here our model won’t know that this particular fruit is called an apple or a banana. Still, it can put an apple in the group of apples due to its characteristics which would be more similar to an apple instead of oranges or bananas.
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