Leveraging Unlabeled Data: A Guide to Semi-Supervised Learning

Semi-supervised learning (SSL) is a machine learning technique that aims to improve the accuracy and efficiency of models by leveraging both labeled and unlabeled data. In this technique, a model is trained using a small amount of labeled data, which is then used to make predictions on a much larger set of unlabeled data. The model then learns from these predictions and adjusts its parameters to improve its accuracy. In traditional supervised learning, a model is trained on a dataset that has both input features and corresponding output labels. The model then uses this labeled data to learn patterns and make predictions on new, unseen data.