Now we are ready to understand SVMs. SVM is an algorithm that enables us to make use of it for both classification and regression. Given a set of examples, it builds a model to assign a group of observations into one category and others into a second category. It is a non-probabilistic linear classifier. Training data being linearly separable is the key here. All the observations or training data are a representation of vectors that are mapped into a space and SVM tries to classify them by using a margin that has to be as wide as possible:

Let's say there are two classes A and B as in the preceding screenshot.

And from the preceding section, ...

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