Data Labeling

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Data labeling is the process of adding meaningful tags to raw data so an AI model can learn from it. In supervised learning, models need examples where the correct answers are already known, like labeling images with the objects they contain or marking text as positive or negative in sentiment analysis. This helps the model understand what patterns to look for when it encounters new, unlabeled data.

Labeling can be done manually by people, through automated systems, or a mix of both. While it can take time, accurate and consistent labeling is essential for creating reliable AI models that make correct predictions and perform well in real-world tasks.

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