Unsupervised learning uncovers hidden patterns in unlabeled data from "summary" of Machine Learning by Ethem Alpaydin
Unsupervised learning is a type of machine learning where the algorithm is given a set of input data without any corresponding output labels. The goal is to find hidden patterns or structure within the data without any prior knowledge. This is in contrast to supervised learning, where the algorithm is provided with labeled data to learn from. By working with unlabeled data, unsupervised learning algorithms are able to discover relationships and patterns that may not be obvious at first glance. This can be particularly useful in scenarios where the underlying structure of the data is not well understood or where it is too complex for human interpretation. One of the key advantages of unsupervised learning is its ability to handle large am...
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