And now we're going to tackle very quickly
the very first step of this toolkit, which is the libraries.
How to import the libraries, how to have them ready
any time we start building a new machine learning model will.
There we go.
I will show you this right away.
It's actually very simple.
We're going to import three libraries now which are numpy, matplotlib, and pandas.
NumPy will allow us to work with arrays because indeed you will see
that your future machine learning models will expect some arrays as inputs.
And therefore we need a library to work with these arrays.
And that's numpy.
Then we'll import matplotlib, which is the library that will allow us
to plot some very nice charts.
You will see that we will actually many charts and graphs in this course.
And finally pandas, which will allow us to not only import the data set
but also create the matrix of features and the dependent variable vector.
I will explain, of course, all these concepts later.
All right.
So let's import these libraries in order to import a library in Python.
It's very simple. You start with import.
That's just a command that will allow to import a library
or even a function or any type of modules.
By the way, a library is an ensemble of modules containing
functions and classes with which you can perform some actions and operations.
For example, the most well-known library in data science is scikit learn,
and this is a library that contains actually all the machine learning models
that you can build by simply creating an object of some classes.
So don't worry about all this now we will see that in great detail
each time we build a machine learning models.
But there you go.
You will see that libraries will be super useful for us
in order to build our machine learning models.
So there we go. Import.
Then you need to follow by the name of the library you want to import.
And the first one we want to import is called numpy.
And then what we usually do is we add a shortcut,
because each time we will use a function of the numpy library.
Well we'll have to call first numpy.
And in order to call it faster.
Well we can add a shortcut here so that each time we want to call number
we will actually call it with its shortcut.
And the shortcut we usually use for numpy is NP.
And as you can see, to add the shortcut you simply need to add as
and then the shortcut name. All right.
So each time we will call numpy we will actually call NP.
Then next library import.
So that next library is matplotlib.
And actually as I told you a library is an ensemble of modules.
And here we're interested in a particular module called Pyplot.
And that's the module that allows us to plot very nice charts.
And so here we're not only going to import matplotlib,
but particularly, well the pyplot module.
And as you can see in order to get this Pyplot module,
I added a dot here which allows you to access the different modules
of the matplotlib library and the module we chose is Pyplot.
And again we're going to add a little shortcut here,
which we're going to call plt.
That's the usual name for this Pyplot module.
All right.
And finally we're going to import a final library which is Defenders library
a super useful library to preprocess your data sets and mostly to import them.
And same for this library.
We're going to add the simple shortcut name p d.
And there you go. Congratulations.
Now not only you know how to import the libraries, but mostly
you have your very first data preprocessing tool in your toolkit.
So now we're going to move on to
the second tool which will be to import the data set.
And that's exactly what we'll do in the next tutorial.