Welcome back to your Machine Learning A-To-Z course.
In this tutorial, I'd like to give you a quick heads
up about the non-linear support vector regression.
So what we discussed previously was intuition for SVR.
Indeed.
But this was intuition of the linear support vector regression model.
And while it's a great start into the world of SVR, it is just the start.
There is so much more to support vector regression.
this was a simple linear model.
There are actually other models,
and the rest assured they will be covered in this course.
So they are coming up and we'll be talking about,
for instance, the RBF kernel, SVR, and the kernel trick
and all these important things that make this a model even more robust.
However, I wanted to give you this heads up because already in
upcoming Python tutorials that are coming right after.
Yeah, just following this tutorial, in this section,
you'll be working with had learned to create your first,
SVR model, and the SVR model will already be nonlinear.
So here you can see the results.
Like I've blurred them out on purpose because,
I wanted not to.
I wanted to make sure you don't get,
a spoiler by seeing the results in terms of the numbers,
but you can see the model itself here, and you can see that it's non-linear.
So just a straight line.
And that is because we're using or at least will be showing you
how to use, the radial basis function, kernel for this SVR.
So it's really going to be a nonlinear SVR.
So now what's the reason for this?
heads up.
Well, the thing is that the non linear support vector, regression
will be kind of further down the course beyond this section.
So it's a bit of a mismatch, but that's, done on purpose
because that's the easier way to grasp these concepts.
And I'll explain in this following slides.
So this is what
this is
what the nonlinear SVR will look like.
Right.
So compared to this
this is your linear model as you can see is much more complex over here.
We have to go into a third dimension and perform
some calculations or some modeling there and go back.
So it is quite involved.
And in order to get there, the following tutorials are essential.
So first in the section on SVM you'll find a tutorial called SVM intuition.
So that's an essential tutorial to take in the steps
to getting to kernel SVR, or nonlinear SVR.
Then in this section on kernel SVM, the following section, you
can find them in the part of the course that is to do with classification.
So in the section on kernel SVM there's a tool called kernel SVM intuition.
Then there's mapping to a higher dimension.
The kernel trick types of kernel function.
And then finally nonlinear kernel SVR.
So as you can see it's quite a complex topics topic.
And it comes at the very end of this list.
So from here you have two choices.
You can either just after this tutorial go straight to advanced Python
tutorials on kernel on the SVR there that are coming up and just do them.
And just keep in mind
that he's using a non-linear kernel, and that will come later in the course.
The intuition for it will come later.
And of course,
or if you would like to get the intuition before you do
the practical side of things,
then it's essential that you would need to go through all of these tutorials
that are mentioned on this slide over here.
Get to non-linear kernel SVR and then come back to the Python terms.
So there are two options.
the my preference if you ask me I would just go for advanced Python tutorials.
Now just keep in mind that he's using a non-linear kernel along with the code,
and then you will still get the intuition when the time is right when you get to
this section on kernel SVM.
So there you go.
That's just a quick heads up on what's coming up.
of course the choice is yours.
And in any case, please enjoy the course.
And there's lots of fun tutorials coming up,
and I look forward to seeing you next one.
Until then, happy analyzing.