Online Learning

A machine learning approach where data is processed sequentially, allowing models to update predictions for future data at each step. Unlike batch learning, which learns from the entire dataset in one go, online learning adapts to new data as it arrives, making it ideal for applications where data is continuously generated or updated. This method ensures models remain current with the latest information, enhancing their accuracy and relevance over time. Online learning is especially useful in dynamic environments, such as stock market prediction or real-time user recommendation systems, where immediate responsiveness to new data is crucial.