Having defined the streaming process, it is now time to glance at the learning process as it is the learning and its specific needs that determine the best way to handle data and transform it in the preprocessing phase.
Online learning, contrary to batch learning, works with a larger number of iterations and gets directions from each single instance at a time, thus allowing a more erratic learning procedure than an optimization made on a batch, which immediately tends to get the right direction expressed from the data as a whole.
The core algorithm for machine learning, gradient descent, is therefore revisited in order to adapt to online learning. When working on batch data, gradient descent can minimize the cost function of a linear regression analysis using much less computations than statistical algorithms. The complexity of gradient descent ranks in the order O(n*p), making learning regression coefficients feasible even in the occurrence of a large...