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hey everyone welcome back so finally excited to talk about this very cool concept of kernels in svm so as i mentioned in the last video on svm dual which by the way i would highly recommend watching first so you fully understand this as i mentioned in that video this idea of kernels and svm is at the intersection of very very useful topics in machine learning but also kind of difficult to understand at first and for me i learned kernels like several independent times over the course of the years and they never really stuck until now so i want to make sure that you donamp;#39;t have that experience and that you understand kernels a lot better from the get-go so letamp;#39;s start with a motivating example so letamp;#39;s look at this very simple looking 2d case of two classes of data so we have the triangles who have relatively low coordinates for x1 and x2 so this is just two-dimensional data and we have the xamp;#39;s which have relatively higher values of x1 and x2 and th
