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Unsupervised learning is a type of machine learning where the model is trained on unlabeled data without any specific target variable. The goal of unsupervised learning is to identify patterns and relationships in the data, and to group similar data points together. This type of learning is useful when the data is unstructured and there is no clear way to label it. Unsupervised learning algorithms include clustering, dimensionality reduction, and anomaly detection.
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