A machine learning approach where models discover patterns and structure in data without labeled examples.
Unsupervised learning finds hidden patterns — clusters, anomalies, and relationships — in data that hasn't been manually labeled. Key techniques include k-means clustering, principal component analysis (PCA), and autoencoders.
Clustering customers into segments based on purchase behavior without predefined categories to discover natural groupings.
Unsupervised learning reveals insights that humans might miss — customer segments, anomalous transactions, and natural data groupings that inform strategy.
A method of comparing two versions of a webpage, email, or feature to determine which performs bette...
The process of creating a visual representation of data structures, relationships, and constraints f...
Teaching AI models new tasks with just a few examples.
Adapting a pre-trained AI model to specific tasks or domains by training it on specialized data.
A machine learning approach where models learn from labeled training data — input-output pairs that ...
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