A method of ML in which data becomes available in a sequential order and is used to update our best model for future data at each step (unlike batch learning which first learns dataset and then develop an hypothesis). This technique is commonly used in situations where it is computationally infeasible to train over the entire dataset, for example: stock price prediction.