Unsupervised Learning is a machine learning category where a model is trained on an unlabeled dataset. The algorithm attempts to discover hidden structures, groupings, or distributions within the input data without external guidance.
Helps AI builders design and scale robust architectures; mastering the implementation of Unsupervised Learning improves latency, accuracy, and operational efficiency for customer segmentation, dimensionality reduction, and anomaly detection.
Unsupervised learning is a machine learning paradigm where models are trained on datasets without human-provided labels or target targets. The model learns to discover hidden patterns, groupings, and structures from raw data, commonly used in clustering (K-means), dimensionality reduction (PCA), and autoencoders.
The process of grouping similar data points together based on vector distances (e.g., K-Means clustering).
Supervised learning trains on labeled target answers. Unsupervised learning analyzes structures without any labels.
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