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Hello my friends.

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Congratulations again for completing part
one Data preprocessing.

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You are now ready to build machine
learning models

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and the first models that we're going
to build together are regression models.

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Welcome to part two on regression.

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This is the branch of machine
learning that aims

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to predict some continuous real numbers,
like for example,

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a salary or a temperature
or any kind of continuous numerical value.

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And together we will build the best models
to make these predictions.

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And so now first of all, let's
make sure we are all on the same page.

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This is the whole machine learning A
to z folder

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containing all the codes in Python
and R and the data set.

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I gave you the link of this folder
right before this tutorial.

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You know in the article
to make sure to connect to this folder.

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And also make sure
to download the whole folder

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in order to get the data sets,
because we will have indeed to import them

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whenever we build a machine
learning model.

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All right.

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And so now, since
we're all on the same page, let's start

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our new journey within regression part
two regression.

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There we go.

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There are several sections
inside each section

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corresponding to a different regression
model.

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We're going to start with simple linear
regression,

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which is the simplest machine
learning model you could ever build.

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And so it's good that we start with this,
because indeed with this one

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we will only have one
independent variable.

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You know, one feature and of course one
continuous real value to predict.

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Then we will move on to multiple linear
regression,

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which is based on the same equation
than simple linear regression.

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Only this time we will have
several features instead of one.

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Then we will move on
to polynomial regression,

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which will allow us to tackle
some non-linear data sets.

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You know, data sets with non-linear
correlations as opposed to previous

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models, simple linear
regression and multiple linear regression,

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which can provide some amazing and
accurate predictions for linear data set.

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You know,
data sets with linear correlations.

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Then after polynomial regression,
we will move on to support

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vector regression,
which is another kind of non-linear model

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that can make some accurate predictions

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for non-linear
data sets with non-linear correlations.

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And then finally we will move on
to decision tree regression

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and random forest regression,
which can provide an alternative

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to predict an outcome for non-linear
data sets.

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So you will have many options.

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You know, after this part two,

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you will have basically a toolkit
of several regression models.

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And so whenever you end up with a new data
set where you have to predict

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a real continuous outcome, well,
you can just try all of them

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and select in the end
the one that has the best accuracy.

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And thanks to the code templates
that will result

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from each of these sections,
you know you will get very clear code

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templates that you can adapt
to your own data sets so that you can try

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these models on your own data sets
in a flashlight quickly and efficiently,

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so that you can select the best one,
giving you the best accuracy.

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And so now in this first section

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in regression, we're going to start
with simple linear regression of course.

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So there we go.

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Make sure to go inside this folder.

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We're going to start with Python.

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So let's go inside this Python folder
and you will find inside two files.

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First the data set salary data dot CSV.

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And of course our Python implementation
which has the IPython notebook format

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which I remind you can run on either
Google Colab or Jupyter Notebook.

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And of course we're going to implement
this model together on Google Colab.

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But first let me explain the data set
and the problem

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that we're going to solve
with simple linear regression.

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And to do this, I'm going to open our data

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set here and explain what this is about.

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All right.

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So first I want to reassure you that
indeed this is a very simple data set.

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You know, with only 30 observations.

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And of course in real life
the data sets are more complex.

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But I want to start
working on a simple data set

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so that we can really focus on
how to build the model itself.

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You know,
because if we had a complex data set,

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we would lose a bit
our focus on the model.

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And I really want us
to focus on the model.

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So let me describe the data set.

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It is a data set containing,
as you can see,

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30 observations and two columns with
of course one feature.

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This is the feature years of experience
and the dependent variable

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which we want to predict
which is the salary.

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So let's say that this data set belongs
to a company that gathered

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data of some of their employees,
collecting for each of them,

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their years of experience
and their salary.

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So you see, each row of the data set here
corresponds to different employees,

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corresponds
to one employee of the company,

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and for each employee of this company,
we have indeed the number of years

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of experience in the company
and their salary.

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Okay.

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And so the goal very simply is to build
a simple linear regression model

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that will be trained to understand
the correlations

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between the number of years of experience
and the salary,

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so that it can predict for a new employee,
you know, having a new number

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of years of experience.

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Well, the corresponding salary
or the salary that this person should get.

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So you see, that's a very easy problem.

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But at least you'll know how to build
a simple linear

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regression model
and perfectly master it in all the detail.

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And then I want to say something
very important.

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Remember
that we're going to build each time

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some code templates
which you can adapt to your own data set,

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so that when you want to use the code
template on your own data set,

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you will only have one thing to change,
which will be the name of your data.

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I will always make sure to make the code
templates as much generic as we can,

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so that you only have 1 or 2 things
to change when you want to deploy them

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on your data set.