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Unsupervised learning is a type of machine learning where the model is trained on a dataset without any supervision or labeled data. The goal of unsupervised learning is to find patterns and relationships in the data that can be used to gain insights or make predictions. This is done by clustering similar data points together or by reducing the dimensionality of the data. Unsupervised learning is often used in exploratory data analysis, anomaly detection, and recommendation systems. One of the main challenges in unsupervised learning is evaluating the performance of the model since there are no labels to compare the predictions against.
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