Hello my friends, welcome to this course.
My name is Adil.
On the point of this, I am the second instructor of this course
and it is with me
that you will be coding all the machine learning models of this course from parts
one, two parts in.
And so here that's the beginning of our journey.
It is this page containing all the resources of this course,
including the course slides and references, the Colab notebooks
on which we will be coding our models in Python.
And you also have the whole machine learning.
It is that folder containing all the codes in Python, and are all the data
sets and colorblind friendly images whenever we do some visualization.
All right.
So first thing first, make sure to have downloaded the slides on your machine
as well as the zip folder containing everything.
Now it is downloaded.
And that's the first thing I'm going to do here.
I'm going to introduce this folder very quickly.
So here it is machine learning. It is it codes in data set.
You have the ten parts containing all the codes and data set.
So for example let's go to part three classification
then into the logistic regression model folder.
And then there you go.
You will find the codes in Python and in R and in Python.
Know that you have the two formats ipynb.
If you want to code on Google Colab or Jupyter Notebook, and py
if you want to code on another ID like Spyder in Anaconda.
All right.
And of course in each folder
you have two data sets that we will use to train the model.
Okay. So now let's go back to the page.
And now what I want to do is introduce to you Google Colab, because it is for me
the best interface to start with when coding machine learning models.
And the reason for this is that not only it is super user friendly, you will see,
but also the beauty is that everything is already pre-installed
so we won't have anything to worry about when it comes to installing packages.
I'll show this to you.
In this tutorial, you will see that TensorFlow for Deep learning is already
installed, XGBoost is already installed, Lightgbm is already installed.
All the powerful libraries are used to code.
Machine learning models are already pre-installed,
so we can just import them and directly code.
And that's awesome compared to having to, you know, install packages
or libraries in the terminal. All right.
So this is exactly what you should get when opening
the link of the Colab notebooks.
To show you this, I created a brand new Gmail address,
rent Machine Learning at gmail.com so that we can all start
from the same page, and so that I can show you how to use Google Colab.
And to do so, let's go to part three classification.
I'm going to illustrate this with again let's take the logistic regression model.
So then you go to Python.
And in this folder you will find two files two logistic regression, the Ipynb
notebook of the whole logistic regression model and the data set okay.
So now we're going to open the notebook in Google Colab.
But first before you do this make sure to be connected
to your Google account with your Gmail address. Right.
Otherwise you cannot open the notebook in Collaboratory.
And if you don't have a Gmail dress with a Google account,
then that's totally fine.
That's exactly why we gave you the Pi and Ipy.
We found the whole machine learning.
It is a zipped folder so that you can open them in your favorite ID.
All right. So now let's open the notebook.
It will open the model in Collaboratory or Google Colab.
And now the first very important thing
to understand is that this is a read only notebook.
In other words, you can't modify it, you can't make a modification and save it.
And the reason for this is,
of course, that you all have access to the same Google Drive folder.
So of course no one can make modification.
But no worries.
What we'll do in the course is that we will recode each of the machine
learning models so that you can really learn by doing.
And to do so, what you have to do is create a copy of this notebook.
And so you go to file and then click save a copy and drive.
And as you can see this is creating a copy
in which you will be able to make some modifications.
All right.
So let me show you this.
First we're going to click this little folder here to import this data set.
To do so we then click this upload button here.
Then you're going to find the machine learning in that folder.
And then you're going to go to part three classification.
Then logistic regression Python.
And then you're going to upload the social network
that which is the data set for the logistic regression model.
Then you click open and it will open right here the data set.
There you go.
And then now just for fun let's run this whole code.
So we click run time here and then run.
Oh and as you can see all the cells are now running one by one.
And at the end well we will see you know, all the predictions.
And right now this cell is coding.
It will plot the training set results
of our logistic regression model and the test set results just below.
All right there we go.
So you see that's how Google Colab works.
Now note that this copy of this
logistic regression notebook is created in your drive.
More specifically in this folder Colab notebooks.
If we double click on it, there you go.
You will find the copy that we just created.
Okay.
And now to finish as I promised, I want to show you this
beauty of Google Colab, which is the fact that everything is already pre-installed.
So let's open a new notebook and let's for example, import TensorFlow or XGBoost.
You will see that it is already here and we don't even have to install it.
So there we go import TensorFlow.
That's the first one.
Let's run the cell.
And as you can see the cell was executed successfully.
And let's do the same.
For example with XY boost XGBoost.
Let's play the cell and same.
It will import extra boost without telling us that we need to install it.
Right. There you go.
So that's the beauty of Google Colab.
We will directly be able to code without worrying about anything.
And so I look forward to starting the implementations with you.
And until then, enjoy machine learning.