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Hello my friends, and welcome

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to this new practical activity
on support vector machines.

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All right.

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So we already built two classification
models logistic regression and k.

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And then we got the best results
so far with Kinen.

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And now let's see if SVM can beat it.

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All right.

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So before we start as usual let's make
sure everyone here is on the same page.

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And if that's the case then follow me
into part three classification

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and then section 16 Support
Vector machine SVM.

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

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And in this Python folder
you will get two files.

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The first is the same data
set social network at dot csv

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containing 400 observations, where each
observation is actually a customer

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who but yes or no SUV
that is advertised on social networks.

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And for each of these customers
you have the age, the estimated salary.

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So these are the two features.

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And with these two features
you will predict the dependent variable

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purchased meaning
whether or not the customers but the SUV.

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So one means yes, the customer
but a previous SUV and zero means no.

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The customer didn't buy any SUV.

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All right so same data set.

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And of course the second file is this
support vector machine implementation.

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And the Ipynb format,

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which you can either open with Google
Colaboratory or Jupyter Notebook.

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And as far as I'm concerned, I'm going
to open it with Google Collaboratory.

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But feel free to choose your favorite ID.

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All right.

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So let's put that file
here. Actually here.

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And right now it is loading the notebook
laying it out.

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And in a second we should have it open.

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There we go.

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All right.

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So that's the whole support
vector machine implementation.

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And of course it is
exactly the same as before.

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In order to re-implement this
we will only have to change

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the code cell
where we build and train this model.

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Because indeed this implementation results
from the exact same classification

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template that we made when we built
the logistic regression model.

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We saw clearly when implementing

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the K-nearest neighbors model,
how indeed we only had to change one cell

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and how this template worked super
well for that model.

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So here for SVM
we're going to do exactly the same.

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We're just going to leave all the cells

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as they are, as they actually were
in the logistic regression model.

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And we will only re-implement
the cell where we built the SVM.

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All right.

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So let's do this.

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Let's create a new copy of this file.

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Because this file is in read only mode.

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So let's click
here. Save a copy and drive.

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And this will create a copy inside
which we will indeed be able to modify

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the implementation.

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And mostly to re-implement
that could sell to build the SVM model.

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All right. Perfect.

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So at the beginning of course,
we start with the data preprocessing phase

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with all the same outputs
displayed on the notebook.

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So that's all good.

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Then we apply feature scaling
because you know

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it improves the training performance.

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And anyway it's never bad
to apply feature scaling.

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And finally there we go.

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That's the cell
we have to re-implement together.

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Because indeed

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it is the one that differs with respect
to the previous implementations.

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So let's click this trash button here to,
you know, re-implement it again.

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Let's create a new code cell.

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And now my friends, over to you
once again I would like you to please

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press pause on the video
and try to implement that code.

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Sell yourself.

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And that's
because I not only want to train you

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in machine learning, but also train you on
how to be independent

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with machine learning.

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So right now, the exercise
I want you to do is to do some research

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in the cycle Learning API to figure out

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which class allows to build the SVM model.

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So you will find it very easily actually,
because

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there is no trap in the name of the class
or the name of the module.

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So I trust

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you will totally be able to do this
exercise successfully and mostly know

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which method to use at the end to train
that SVM model on the training set.

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All right, so please press pause.

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And now in two seconds
I'm going to give you the solution.

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All right let's do this.

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So I already have the cycle API open.

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You know
that was for the nearest neighbors.

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The k nearest neighbors
which we implemented previously.

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In the previous section
we used this class k neighbors classifier.

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And now the next thing we would like
to find in this API documentation

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is the module that contains the class
that allows to build the SVM model.

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So naturally where can we find it?

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You know here
should we scroll back up or scroll down?

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Well, let's hope that you know,
the name of the module starts with an S,

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because here, you know the modules
are organized by alphabetical order.

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So since here we are at N use neighbors,
let's hope that the name of the module

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we're looking for starts with a nest
like support vector machine.

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So let's scroll down and random
projections semi-supervised learning.

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And there we go support vector machines.

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Hello.

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That's exactly what we were looking for.

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Support vector machine.
So that's not the name of the module.

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The name of the module is SVM. It's same.

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That stands for Support Vector machines.

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All right.

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And then
well you know the hardest part is done

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now according to you which estimator is
you know because here you have

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all the basically support vector machines
based machine learning models.

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And so according to you
which one do we need to take here.

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Well, we actually have two options.

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We could either take the linear SVC
which will directly

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build the linear support vector
machine model,

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or we can take this one
SVC and choose a linear kernel.

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All right.

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And we will actually go for this option
because in the next section

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we will study the kernel SVM models,
which as you might guess

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allow us to choose some different kernels
in our SVM, including the linear one

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and the nonlinear ones,
like for example, the very famous one RBF.