Unsupervised Learning
Unsupervised learning is a type of machine learning where the model learns patterns from data without labeled outputs. It is used for clustering, dimensionality reduction, and discovering structure.
In Simple Terms
Think of it as sorting a pile without labels: the model finds natural groupings and structure on its own.
Detailed Explanation
The algorithm receives only inputs (e.g., documents, images) and finds groups, outliers, or compact representations. Applications include customer segmentation, anomaly detection, and pre-training representations for downstream tasks. Unsupervised methods do not optimize for a specific label, so success is often measured by usefulness of clusters or downstream performance. They are key in exploratory analysis and as a first step before supervised or semi-supervised learning.
Related Terms
Artificial Intelligence
The simulation of human intelligence processes by machines, especially computer systems.
Read moreTransfer Learning
Transfer learning is the practice of taking a model already trained on one task or dataset and reusing or adapting it for another. It speeds development and often improves performance when data for the new task is limited.
Read moreMachine Learning
A subset of AI that enables systems to learn and improve from experience without being explicitly programmed.
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