Incremental Learning is a method in machine learning where a model is trained gradually, allowing it to learn from new data without forgetting previously acquired knowledge. This approach is particularly useful in dynamic environments where data continuously evolves, enabling the model to adapt over time. Unlike traditional learning methods that require retraining from scratch, Incremental Learning updates the model incrementally, making it more efficient. This technique is often applied in areas such as natural language processing and computer vision, where models need to adapt to new information while retaining their understanding of earlier data.