Answer / Dhruv Kumar

Semi-supervised learning is a type of machine learning that combines aspects of supervised and unsupervised learning. It uses a small amount of labeled data along with a large amount of unlabeled data to improve the performance of the model. Semi-supervised learning algorithms learn to transfer knowledge from the labeled data to the unlabeled data, allowing them to leverage the rich structure present in large datasets. Common semi-supervised learning techniques include self-training and multi-view learning.

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