Data scientists input a limited quantity of labeled training data to an algorithm in semi-supervised learning. The algorithm then learns the data set's dimensions, which it may subsequently apply to new, unlabeled data. When algorithms are trained on labeled data sets, their performance usually improves. Labeling data, on the other hand, can be time-consuming and costly. Semi-supervised learning falls between supervised and unsupervised learning in terms of performance and efficiency. Semi-supervised learning is utilized in the following areas:

• Machine translation is the process of teaching algorithms to translate languages using a smaller set of words than a full dictionary.
• Fraud detection is the process of identifying causes of fraud when there are just a few positive examples available.
• Data labeling: Algorithms trained on tiny data sets can automatically apply data labels to bigger ones.