Hello.
So before going deep down into the session, let's have a quick recap of what we all have learned in
our previous session.
So we have basically this youth case in which I have to predict what can be the weight of a person if
that person is dismissed.
So what we have to do, basically, we have to simply put, we have to simply consider this as my ex.
So here it is nothing.
But it is just my it just my 1:00 a.m. policy.
And basically, I have to find this and I have to find a C simple that's that's followed statement solved.
That's what we are trying to learn.
So basically for this, you need some basic math.
You need some basics like what is a slope, what is daddy with some basics like cooking.
So that's what we all have covered in the previous session.
So we have learned this is exactly my daddy do.
And what are the practical uses of all these things?
And and you know what what what this line is all about.
And we also learn.
Yeah.
How how to compute the slope between two points and what is a slope at a particular point.
So you will see, let's say, visualize our data and visualize already we we we have this type of line,
we have basically this type of line.
But but it is not necessary that but it is not necessary that it is my best foot line.
I can say it is my best prediction line for my model, because maybe that may be that that according
to data point that this line can be your best.
What either this one or either this one or either this one for how to compute, how to compute ridgeline,
how to compute which line is your best line, which one is the best, how to compute.
Because you see, you've got to have multiple you can have tons of lines in this data.
When you going to 100, you can have a lack of lines for how to compute with the regression line is
your best line, which is the best prediction line?
Hope.
Computer support is what we have to do.
We have simple terminology here.
Let me let me open a new page a little bit.
Yeah, let me open it.
So here what I'm going to do next.
Again, I'm going to visualize this, OK?
Let's say this is exactly my data security.
Don't get me again remove let me not consider this too much data points.
Let me first draw my line.
Let me draw this line so very forth.
I'm just going to say I'm just going to draw this line over here.
So let's say this is my prediction because that is prediction.
Alexa, I have some data points over here.
Let's say here I have some let's say here I have some.
If I were to consider my previous case in which basically I have to predict what can be the weight of
a person who had this much height.
So let me just consider that.
Here I had basically my height and I had basically my weight over there, and I have to tread this on
a basis of whatever height, whatever input I'm going to give to this production line.
So let's say these are all the data projected.
These are one data point.
These are all the data points.
And let's say this is the final line that I have achieved over here for what you have to do, so you
have to select you have to select that line as your best line, which has least Atala, which has at
least.
Now you will think how to compute your error a few hours.
Nothing.
But this is your Adam.
Similarly, this is your ATM.
This is your ATM, this is your ATM.
This is your ATM.
So go to our line.
Has at least atter that will get selected as Pascaline.
That's what that's what I am trying to achieve.
So let me let me write a very simple, simple example of the very first time with the celebrities.
It is.
Yeah, that's it.
Oh, let me let me go see the very simple wizard.
Let's say this is my let's say this is my I can't see this is my actual data point.
And let's say if I were to consider with respect to the spiritual aspect of this spiritual life.
So let's say this is my let's listen to and here's something I say.
My prediction happens.
So whatever is a difference between this, what is the difference between this is exactly my ATM is
exactly my ATM.
Let's say in actual your weight is eighty eight and but your line is predicting that your weight is
70 80.
It means you have Aronoff 18 Gidgee.
That's what I'm trying to say.
You I'm trying to make you understand.
So what you have to do, you have to minimize you have to minimize this error.
You have to minimize this at the journey that you will achieve your best.
Footlight, depending upon what you face, you have for how to achieve bastardly.
So it's a simple, simple mathematical equation or it is nothing.
But what is my actual prediction?
I can see what is my actual data and what I'm going to predict.
So there's nothing but is square of this now you will think what I'm going to consider is graph this
very first.
Let me tell you a scenario.
So very first you have to compute the entire atom.
So what exactly is data out of?
Let's say this is my data point one.
This is two.
This is three.
This is four.
And this is five.
This is six.
This is seven.
So it is nothing but some additional I close to one to seven.
This why I still you can consider it is why as well.
You can say it is why and it is why I buy.
So now you will think why I have a cloud over here.
Because you will see here you have whatever you have postulated and here you have negative at it.
So if you really do that summation, maybe it will cancel that.
It makes no sense at all of your error.
Therefore I would put it that way.
So now basically you have to compute something known as your mune is good atah, which is nothing but
one by act, which is nothing but my total number of data points.
To turn total number of the tabloids and and and hear what you have computed, I called two one until
seven and why minus y I all you can get is that it is nothing but my prediction value or because the
prediction is in such you guessed it is it is great.
It is your prediction and it is your actual.
It is you actually.
So who do ever lie.
Has this list mse whosever line has this least masc that line, that line gets selected as my best line,
best fit regression line for how to select this best foot regression line.
Simple that has EMC.
So now how to find this masc or you can see how to find your best regression and you will see here you
have multiple regression.
It means you have multiple matches as well.
Let's say you have something.
MSU one.
MASC two.
Three.
They'll tell you have hundred of a little meniscal, Adam, with respect to this, will have some your
slope and your your your constant let me go through the ceiling.
Similarly, you have to come whether.
Because you will see.
You'll see.
Let me let me open a previous U.S. exit with respect to this line.
Let's say with respect to this purple line, let's say it's messy.
That's get added.
That is one hundred.
And let's say it has slope of let's say point eight and exit as content of exit four.
So here you have another beer.
Similarly, with respect to each and every line, you have some pair.
So let me let me let me do I need to know every single this this will give something similar like this
will be something.
So if I'm going to plot if I'm going to plot all these things.
So it means definitely I need some kind of three dimensional visual or some kind of three dimensional
thought to understand this, to understand the scenario.
So let me open this plot.
Let me open it.
Yeah, this is exactly this is exactly three dimensional visual where you have something known as slope,
where you have something on a slope.
This is exactly the intercept.
You can either represent it a season and this is this is your square, which is nothing, which is,
I think, your cost function, which you have to be, which you have to optimize.
Would you have to minimize because whoever line, whosever line has a least MSE has a least MSI, that
line gets selected as my best line.
So that's all about the session.
In the upcoming session.
We are going to learn about this plot, what this plot is all about, and, you know, our very famous
approach, which is exactly gradient descent approach as well.
So that's all about it.
Thank you.
Have a nice day.
Keep learning.
Keep growing.
Keep practicing.