[MUSIC] So now we have some training data and
some desk data. And what we can do next is #Build, there's a sub-header here, #Build the regression model. So GraphLab Create has many
algorithms pre-implemented for various machinery and tasks or
you can implement your own if you like. And during the sequence
of the specialization, you're gonna write many algorithms
of your own, but for this course, we're just gonna use
pre-implemented algorithms so can really try to understand
how machine learning works. So what you can do with machine learning? So, we're gonna do is a build
a square foot prediction model. Just call it sqft_model. And in GraphLab,
there something called linear regression. So, linear regression is what we learned,
what we explored and talked about, and we understood quite well
during the module that Ann was teaching. And so
if you just say linear _regression.create, it's going to create a linear
regression model for you. That creates the traits with
projects GraphLab Create, and create is also the verb to use to create
various things with GraphLab Create. So we're now going to create
a linear regression model, and what I have to give as input
I give it the training data. So here's the training data. You have to say what variable
you're trying to predict. So that's the target. So I'm gonna say the target
is going to be price. So I'm trying to predict price. And the question is what are the features,
what is the input. So, I'm going to say that the features,
and by the way, you can use top complete even if [INAUDIBLE] parameters of
function, which is pretty cool. So, the features is
a list of the features. If you don't give anything, it will use
all features, all columns of the data. But here, the only feature input we want
to use is square feet of living space. And that is a string
that I need to put in, so I forgot to put it in quotes, so
let me fix that real quick here. So open quotes, close quotes. All right, very good. So let me just reread that for us. So sqft_model
graphlab.linear_regression.create on the training data to predict the target
price using features sqft of living. Let's go an execute this. On training data training,
I wrote data twice here. You see the arrow came out here,
training data and the score data. So now let's execute. So what's happening here? It's done actually, but what we did
was create a linear regression model, it used a particular algorithm
called Newton's Method. But GraphLab Create chooses
an algorithm automatically for you unless you tell it which one to use. And this algorithm allows you to
make some predictions on the data. [MUSIC]