I'm a little bit about the SVM. I'm actually understanding for which purpose Support Vector Machines are used. The aim is to find the biggest (best) hyperplane between two different data clusters. The question is here do we apply SVM after we have classified data points or is Classification also a task of SVM?

The examples on videos begins always with predefined positive and negative / or blue and red data points.



In machine learning, there are two types of problems (there are more, but start with 2) -- supervised and unsupervised.

SVM (also logistic regression, etc) is part of supervised learning where you are given two datasets. One with predictors and known desired output (blue/red) called training set, another one with only predictors. Your task is to learn the structure from the training set and try to predict the output of the test set.

What you saw in the video is the training part.

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