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Unsupervised Learning

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Unsupervised Learning is a method used to enable machines to classify both tangible and intangible objects without providing the machines any prior information about the objects. The things machines need to classify are varied, such as customer purchasing habits, behavioral patterns of bacteria and hacker attacks. The main idea behind unsupervised learning is to expose the machines to large volumes of varied data and allow it to learn and infer from the data. However, the machines must first be programmed to learn from data.

Source: Unsupervised Learning

ddsf/public/guidebook/06_append/glossary/u/unsuperlearn.txt · Last modified: 2021/07/14 16:45 by murphy