Online Learning

Online learning is a machine learning technique where the model is updated continuously as new data becomes available.

It is used when there is a need to adapt to new data in real-time or when data arrives in a sequential manner. Online learning is commonly applied in scenarios such as stock price prediction, recommendation systems, and real-time analytics. Thus, it helps mitigating model and data drift, ensuring it adapts to changes in the data distribution and maintains its performance over time.

The technique works by updating the model incrementally, rather than retraining it from scratch with the entire dataset. For example, in stock price prediction, an online learning model can be updated with each new stock price as it becomes available, allowing it to adapt to market changes quickly. In recommendation systems, the model can be updated with each new user interaction, improving the recommendations in real-time.

Online learning is important because it allows for continuous adaptation to new data, making it suitable for dynamic environments. It is a powerful approach in machine learning, enabling models to remain relevant and accurate as new data is received.