All language subtitles for 2. Linear Regression Theory

Hi, everyone, and welcome in this new video package.

I have a run and welcome in this new video in this video while going to explain theologically how the

linear regression works.

So to do it, I will take I have a run and welcoming this new video.

In this video, we're all going to talk about how the linear regression works, theoretically to do

it.

I have just take out an extract of my book.

So

the first thing that you need to know is that the linear regression is the most easiest algorithm in

machine learning.

Indeed, to compute the error between the prediction and the real value.

We just do the prediction minus the real value, and we put the difference at this.

Then we do the sum of the difference for each observation, and we have the total error of the linear

regression.

And the goal of the linear regression is to minimize this error because it will mean that if you have

a near equal to zero, your linear regression predict perfectly you dataset.

So the intuition of the linear regression can be resume by and that go, even that tries to find the

best way to minimize the distance between the predicted and the real values.

Exactly like this in red that we have the linear regression line and each blue point is an observation,

and the goal of the linear regression is to find a way to generalize the behavior of the blueprint.

Here we can see that the linear regression minimize the distance between each observation.

But in reality, this line cannot fit all the blueprint.

The real goal is to minimize the distance between all the bluepoint, because if you find an algorithm

in a train sets, we are going to see what is a trend set in the next video.

But if you have some algorithm, too much train when you want to use it, for example, to do prediction

with unknown observation, you will have very bad results.

So really, the goal is not to have a line that fits with all the points, but it's really a line that

generalize the behavior of each of all the points together.

Here I have put a little resume of all you need to know about linear regression.

So I have we need you a quick resume of this extract of my book, but I will invite you to read it because

it is attached to this video.