I’ll try to explain it in an intuitive way through two examples:
Supervised
An example of supervised learning algorithm can be a credit card fraud detection, where the learning algorithm is presented with credit card transactions classified as normal or suspicious.
The classification is done by hand and the algorithm learns by it. At the end the algorithm produces a decision model that classifies future transactions as normal or suspicious.
Unsupervised
Unsupervised learning discovers hidden patterns in the data. An example of unsupervised learning is an item-based recommendation system, where the learning algorithm discovers similar items bought together.
For example, people who bought book “Harry Potter” also bought book “Lord of Rings”.
@OriginalWorks please check my original post
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