Unsupervised learning is another way of teaching computers to learn from data, but it’s a bit different from supervised learning.
Let’s say you have a big box of different kinds of toys and you want to sort them out. No one tells you what kinds of toys there are or how many groups you should make. You just look at the toys and start sorting them based on what they look like. Maybe you make a pile of teddy bears, a pile of toy cars, a pile of dolls, and so on. That’s a bit like unsupervised learning.
In unsupervised learning, we give a computer program, or model, a lot of data, but we don’t give it any labels or correct answers. The model’s job is to find patterns or structure in the data on its own. It might group the data into different categories, like how you sorted the toys, or it might find other kinds of patterns.
Unsupervised learning can be useful when we have a lot of data but we don’t know what kinds of patterns or information it might contain. It’s used in many areas of artificial intelligence, including clustering, anomaly detection, and feature learning.
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