In unsupervised learning, the training data contains only inputs. Then programmers task the learning algorithm with producing groupings based on some specified weight of measuring the degree of similarity. For example, streaming video services such as Netflix use algorithms to identify clusters of customers with similar viewing habits in order to recommend additional streaming to those customers. But fine-tuning such algorithms can be complex: because most people have several interests, they are typically grouped within several clusters.
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