Semi-supervised learning is a machine learning technique that falls between supervised and unsupervised learning. It utilizes a small amount of labeled data alongside a larger pool of unlabeled data during the training process. This approach is particularly valuable when labeling data is expensive or labor-intensive. By leveraging the structure and patterns found in the unlabeled data, semi-supervised learning can enhance the performance of models, improving generalization and reducing overfitting. Applications of semi-supervised learning can be found in various fields, including natural language processing, image classification, and speech recognition, where labeled data is limited but abundant unlabeled data is available.