Supervised Learning

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Supervised learning is a fundamental machine learning paradigm that involves training models using labeled data. In this approach, the AI system learns from input-output pairs, where the input data is associated with corresponding target or output labels. The primary goal of supervised learning is to enable the model to generalize patterns and relationships in the training data, allowing it to make accurate predictions or classifications on new, unseen data.


The essence of supervised learning lies in its reliance on human-provided labels to guide the learning process. Supervised learning algorithms, such as decision trees, support vector machines, and neural networks, use the labeled data to learn mapping functions that can predict or classify future examples. This approach is widely used in various applications, including image classification, speech recognition, and natural language processing. The training process involves minimizing the discrepancy between the model’s predictions and the actual labels, ensuring that the model becomes adept at capturing relevant features and patterns from the input data. Ultimately, supervised learning empowers AI systems to learn from labeled examples, enabling them to perform tasks that involve making informed decisions or predictions based on patterns learned from historical data.

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