Semi-Supervised Learning
Semi-supervised learning is a machine learning paradigm that combines a small amount of labeled data with a large amount of unlabeled data during training. It leverages the structure in the unlabeled data to improve learning accuracy beyond what could be achieved with labeled data alone. This approach is particularly useful when labeled data is scarce or expensive to obtain, while unlabeled data is abundant.
Developers should learn semi-supervised learning when working on machine learning projects where labeling data is costly or time-consuming, such as in natural language processing, computer vision, or medical diagnosis. It is used in scenarios like text classification with limited annotated examples, image recognition with few labeled images, or anomaly detection in large datasets. This technique helps reduce the dependency on extensive labeled datasets while maintaining or improving model performance.