Instructions

Unsupervised learning is a type of machine learning where the model is trained on a dataset without any labeled output. The goal of unsupervised learning is to identify patterns and relationships in the data, and group similar data points together. This is done through techniques such as clustering, dimensionality reduction, and anomaly detection. Unsupervised learning is useful in situations where labeled data is scarce or unavailable, and can be used for tasks such as customer segmentation, image and text classification, and anomaly detection. However, since there is no labeled output, the results of unsupervised learning are often more difficult to interpret and evaluate than supervised learning.
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