A machine learning approach where models learn from labeled training data — input-output pairs that teach the model the correct mapping.
In supervised learning, you provide examples with known correct answers (labels). The model learns patterns to predict labels for new, unseen data. Common algorithms include linear regression, decision trees, and neural networks.
Training a model on historical sales data with outcomes (won/lost) to predict which new leads are most likely to convert.
Supervised learning powers classification and prediction tasks — from spam detection to customer churn prediction to demand forecasting.
AI systems that can interpret and understand visual information from images and video.
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.
The branch of AI focused on enabling computers to understand, interpret, and generate human language...
A training technique where AI models are refined based on human preferences and evaluations of their...
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