Support Vector Machine, Machine Learning, Data Science, Deep Learning.
SVMs only require a subset of the training data to be in memory at any given time, making them memory efficient. Versatility: SVMs can be used for both linear and non-linear classification, and there are various kernel functions that can be used to specify the type of classification boundary to be