What is the difference between online learning and batch learning?

Online learning and batch learning are two different approaches to training machine learning models. Online learning involves updating the model parameters incrementally as new data becomes available, allowing the model to adapt to changes in the data over time. Batch learning, on the other hand, involves updating the model parameters using all of the available data at once, which can be computationally expensive but may lead to better performance if the data is static. The choice of whether to use online learning or batch learning depends on the specific task and the properties of the data.