Hello my friends.
Welcome to part one Data Preprocessing.
And mostly welcome to the practical activities of this course.
My name is Adam and this and I am more than happy
to welcome you into this course.
We're about to embark on a super huge journey,
but yet a very exciting one because we're going to build together
a series of machine learning models from all the branches of machine learning.
And speaking of these branches, will you have them in front of you?
That's the whole folder of this machine learning it is it course
structured into all the different branches of machine learning,
from data preprocessing, regression, classification, clustering,
association rule learning, reinforcement learning and NLP and deep learning.
And now we're about to start part one day data preprocessing.
And this is the first very important step of this journey.
Because indeed any time you build a machine learning model,
you always have a data preprocessing phase to work on, right?
You have to preprocess the data in the right way, so that the machine
learning model that you're going to build can be trained the right way on the data.
So we have to start with this step.
This will not be the most exciting step, but I will make sure to
do this very efficiently so that we can rapidly proceed
to the branches of machine learning, where we're going to build indeed some models.
But before that, we have to master data preprocessing.
And that's exactly what we're going to do in this part one.
All right.
So first of all,
make sure that you have the same as what I have on my computer right now.
This is the whole machine learning is that codes and data sets folder containing
old Python codes and all the R codes, as well as all the data sets
in each of the practical activities we're going to do together.
The link of this folder was provided to you in the previous section.
You know, section one.
So if we are on the same page let's do this.
Let's go inside this part1 data preprocessing folder
which is structured the following way.
It has only one section, luckily for us, so that we can quickly get to the machine
learning models and as any folder in this course,
well, it is structured the following way in two folders.
First Python and then R.
Despite and folder contains of course all the Python implementations
and the data set, and this folder contains all the R implementations
with the same data set.
So now we're going to go into Python because each time in in each section,
anytime we build a model we're going to start first with Python and then R okay.
So that if you are more interested in Python, will you take the first part.
And if you're more interested in R you take the second part.
And here I want to say something very important.
This course is not meant for you to master both Python and R.
We just covered the two tools so that anyone can learn machine
learning on their preferred tool.
So if you prefer Python, you can just do Python.
If you prefer R, you can just to R.
And if you want to learn both well you're welcome to learn both.
But you don't have to. That's what I want to say okay.
So we're going to start with Python here
I'm going to teach you all the tools on data preprocessing.
And there you go.
That's the files you will find in this data preprocessing folder.
In the Python folder you will first find this data preprocessing tools.
Implement version which contains all the different tools
of data preprocessing that you might have to use on your data sets
in order to preprocess them the right way for your machine learning model.
Then we have the data preprocessing template, which will be very useful
for us to tackle any data preprocessing phase of our future.
Machine learning model implementations in the following parts.
So you will see.
You will absolutely love this template.
And we also have this data in a CSV file,
data dot CSV, which is the data set on which
I will show you how to implement all these data preprocessing tools.
And just to give you some context, let's say that this data
set belongs to a retail company that collected some data
from their customers, whether or not they purchase a certain product.
So here each of the rows correspond to different customers.
And for each of these customers, well, this company gathered their country,
their age, their salary and whether or not they purchased their product.
All right. So that's a simple data set.
But I wanted to use a simple one so that we can really focus
on all the tools we're going to learn in this section.
And speaking of them, well, now I suggest that we start this data preprocessing
tools implementation because indeed this course is really action based.
You know, you're going to learn by doing in this course.
And therefore for this implementation and each future
implementation, we will re-implement it from scratch.
And so there you go.
Our first implementation will be for all the data preprocessing tools.
And now we're going to open this file.
And as I explained in the first section of this course you can either open it
in Google Colaboratory by just double clicking here or on Jupyter Notebook.
If you don't like Google Colaboratory.
So feel free to select your most comfortable environment.
And for Google colaboratory lovers, well, there we go.
Let's open our data preprocessing tools.
And first I'm going to show you exactly the tools that we're going to implement.
And mostly that you're going to learn for your machine learning models.