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Semi-supervised machine learning makes use of both of those unlabeled and labeled data sets to train algorithms. Commonly, throughout semi-supervised machine learning, algorithms are initially fed a small number of labeled data that can help direct their development and after that fed much bigger portions of unlabeled data to accomplish the product