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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 group similar data points together. This type of learning is useful when there is no prior knowledge about the data and the objective is to explore and discover hidden structures. Unsupervised learning algorithms include clustering, dimensionality reduction, and anomaly detection. Clustering algorithms group similar data points together based on their similarity, while dimensionality reduction techniques reduce the number of features in the data while preserving the important information. Anomaly detection algorithms identify data points that deviate from the normal behavior of the data. Unsupervised learning is widely used in various applications such as image and speech recognition, recommendation systems, and fraud detection.
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