Unsupervised learning is a type of machine learning where the algorithm is trained on an unlabeled dataset, meaning that the input data is not paired with corresponding output data. The goal of unsupervised learning is to find patterns and structure in the input data without any prior knowledge of what the output should be. Unsupervised learning is commonly used in applications such as clustering, anomaly detection, and dimensionality reduction. It is a powerful tool for exploring and understanding complex datasets and can help identify relationships and patterns that may not be immediately apparent.
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