The user wants to understand Semi-Supervised Learning in the context of Machine Learning Foundations and apply it to practical AI governance or compliance work.
Semi-supervised learning is a machine learning approach that uses both labeled and unlabeled data during training. It is useful when labeled examples are costly or limited but larger volumes of unlabeled data can help improve representation, classification, or prediction.
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