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Hello and welcome back to the course
on Machine Learning.

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I hope you enjoyed the previous tutorials.

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And today we're going to talk about
the different types of kernel functions.

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So the final thing that you need to know
about the kernel SVM is that the radial

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basis function, which is also called
the Gaussian function,

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is a not the only kernel function
that is used in this method.

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so let's have a look at a couple.

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So here we've got the Gaussian or the RBF
kernel, which we've already spoken about.

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Then there's also a popular choice
which is a sigmoid kernel.

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And that's the formula on the right.

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It's in a bit of a different notation,
but the essence is the same is that you

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still select a, landmark and then,

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then from there,
depending on the distance,

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the landmark, different results
will occur.

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But in this case, as you can see,
this kernel function is directional.

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So anything, even like just looking at it
in this, two

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dimensional space is just a projection.

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You can see that anything to the right
will, be automatically right away.

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will have a high value.

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So it will be included
in your classification.

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Anything to the left will be excluded.

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So sometimes
you might require these situations.

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So maybe if you look at a two dimensional
space, just imagine those points

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that we had points on on
one side are not in your classification.

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On the other side,
are you in your classification.

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You want to somehow, outline

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that decision boundary, or, highlight

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that as, points past a certain points are
should be in your classification.

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Then a sigmoid kernel is a popular choice.

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Also, we've got, polynomial kernels,
which are also popular.

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one of the more popular choices.

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And, here
you can have, like, a, polling them,

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which dictates how your kernel behaves.

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And, again,
you can go into more detail on that.

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So this tutorial is not for us to discuss

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the specifics
of each one of these, kernels, but

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just to, show you that they exist and,
help you be aware of the different types.

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And so these are the most popular choices
the Gaussian, RBF,

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sigmoid and polynomial.

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Hope you enjoyed,

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learning about the kernel SVM
and I look forward to see you next time.

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Until then, happy analyzing.