Unsupervised learning is a paradigm of machine learning where the emphasis is on discovering patterns, structures, or relationships within data without explicit labeled guidance. Unlike supervised learning, where models are trained on labeled examples with well-defined target outputs, unsupervised learning involves exploring data in its raw form to uncover inherent groupings or representations. It is particularly useful for extracting insights from unstructured or unlabeled data, enabling the detection of hidden patterns and nuances that might not be apparent to human observers.
Clustering and dimensionality reduction are two common techniques used in unsupervised learning. Clustering involves grouping similar data points together based on their inherent similarities or distances in feature space, aiding in data organization and exploratory analysis. Dimensionality reduction, on the other hand, aims to reduce the complexity of data by transforming it into a lower-dimensional representation while retaining important information. This can be especially valuable when dealing with high-dimensional data, as it simplifies visualization and subsequent analysis.
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