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Hello my friends, and welcome

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to this new practical activity
on this time the kernel SVM model.

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So please follow me into part
three classification

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to implement the kernel SVM model.

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And as usual
we're going to start with Python.

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And here we are with our two files.

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The first one that contains

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the whole implementation of the kernel
SVM model in the Ipynb format.

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And the second one the data set,
that same data set on which we trained

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and will train all the classification
models of this part.

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So let's have a quick look at it again.

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This data set contains
some information of 400 customers

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represented by each row here.

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So each row corresponds to a customer.

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And for each of these customers, well
we have the age which is a first feature.

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The estimated salary
which is a second feature.

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And the dependent variable purchased
which tells whether or not

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they bought no or yes, a previous SUV.

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All right.

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And so in our practical activities

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of this part three classification,
we build several classification models

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to understand the correlations
between these two features here.

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And this dependent variable purchased
in order to predict which future customers

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will buy brand new luxury SUV
that is advertised on social networks.

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All right.
So still the same story. And now.

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Well, let's begin
our kernel SVM implementation.

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I'm going to open it
with Google Colaboratory.

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But you have the choice to open it
with Jupyter Notebook as well.

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Choose your favorite.

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And now it is opening
the notebook loading it, laying it out.

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And there we go.

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Welcome to the kernel SVM implementation.

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All right.

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And of course it results from the exact
same classification template

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which we made in the first section
of this part logistic regression.

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Indeed, when we built the logistic
regression model,

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we made at the same time
a great classification template

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which allows us to only change one cell
when building another classification

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model, which is exactly,
you know, the cell where we build

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and train this classification model
on the training set.

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So this cell is the only one
we have to modify here.

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However,
this notebook is in read only mode.

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So in order to modify this cell
to build a kernel SVM model,

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we have to indeed click file here in order
to create a copy of this notebook.

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And to do this you just need to click
save a Copy and drive.

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This will create a copy of this notebook.

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And in a second
you'll have it opened on your machine.

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All right so I'm going to put everything
in that order.

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All right.

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And now this is our copy.

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And so inside the only thing we have to
re-implement is indeed that cell

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where we build and train
the kernel SVM model on the training set.

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So let's not look at it.

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Let's put it in the trash.

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And now let's re-implement
it from scratch.

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All right. Perfect.

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So it's actually super, super easy.

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We can even do it even more efficiently
than, you know, the usual way.

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When we went to the cycling API,
then got the name of the function

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because, as I explained in the previous
section, when building the SVM model,

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the classic one, well, the way we built
it was actually to, you know, build

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an object of this class as VC
and then choose a linear kernel.

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And now actually,
you know, we're about to do the same.

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We're about to use the same class
to build this kernel, SVM objects.

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But this time,
instead of choosing a linear kernel,

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we will choose a nonlinear one.

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And that's the best example of it.

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You know, the most commonly used it
is the RBF radial basis function kernel.

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And you also have some other ones like the
polynomial kernel or the sigmoid kernel.

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But really
this one is the best option to start with.

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And that's the one we'll choose
for our kernel SVM implementation,

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as you might have guessed.

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And therefore
remember how I also want to train you on

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how to juggle with the different tools
of your machine learning toolkit.

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You know, the data preprocessing toolkit
or the regression toolkit, or even now

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the classification toolkit
containing all your classification models.

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The most efficient way now to build
this kernel SVM model is just to go back

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to our SVM implementation, then
find that cell where we built the SVM.

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And indeed, since we built that SVM
with the same class as VC,

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but choosing a linear kernel,
well now as simple as that.

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In order to build a kernel SVM,
we just need to take this code cell

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and paste it
right here in a new code cell.

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And then guess what?

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Just change that kernel parameter.

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All right.

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And as we said this time
we're not using a linear kernel.

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We're going to use an RBF kernel.

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The radial basis function which was
explained in the intuition lectures.

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And there you go, my friend.

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That implementation is over.

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You see how I'm doing

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all these templates
to make you as most efficient as possible?

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Well, that's what I mean.

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Now we can juggle between
our different codes, different tools

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of machine learning,
and we can build new models in flashlight.

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And that's what we just did
with kernel SVM.

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All right. So basically we're done.
We're ready.

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So the next natural step is to upload
the data set inside the notebook.

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And to do this
you need to click this folder button here.

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Then your notebook will be connecting
to a runtime to enable file browsing.

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Still the same story.

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In a few seconds we should be able to see
the upload button.

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Perfect. There we go.

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And so now we're going to click it

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and we will directly get access
to social network.

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As if you know
you were here in the SVM section.

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But let me show you the path once again.

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So please find first your whole
machine learning is that folder.

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Then please go to part three,
then section 17 kernel SVM,

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then Python
and then social network ads dot CSV.

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Perfect.

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It will upload as a data
set inside the notebook.

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And now.

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Now we can run everything
by clicking runtime here and then run