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As humans, we understand how to classify different animals based on repeated exposure.
When we were babies, we would look at a fluffy and adorable animal, and perhaps our parents would say, “That is a cat,” while another person might say, “That is a dog.” Over time, we develop the ability to recognize patterns and differentiate between things.
This is exactly what we do in supervised learning. We provide the computer with numerous dog images to train it on recognising and categorising dogs accurately.
We provide the computer with a lot of unstructured data and ask it to find patterns and structure it and identify how the data is organised.
One way it does this is through clustering, where the computer grouped data into separate clusters based on similarities. This helps us make sense of unstructured data and discover meaningful groupings within it.
When you scroll on Instagram, sometimes you bump into “Suggestions for you”. It suggests who you might be interested in following based on mutual connections, shared interests, or similar social circles.