All in this session, we are going to learn what exactly is this linear regression, which is exactly
a regression algorithm.
So you will see it is exactly a regression algorithm.
So how how this algorithm works and what does that what does the mathematics behind this algorithm?
We're also going to learn about all this stuff and what type of use use it in what type of practical
use cases this this algorithm is going to.
Very handy.
Would you?
So you have learned your school is something known as something you have learned in school X Y equals
to Amex.
Plus, you might have learned this in your I think when you are in class eight or maybe in your intermediate
or maybe in your twenties too, this is what you have learned in your school.
I'm just going to assume it.
So what is there to see and what are all these things.
So I.
Nothing, nothing.
What what is exactly.
Stop and see.
Just my constant and x x let's x is nothing.
X just our datapoint.
All you can see in terms of data it is nothing, but it is just a different feature.
So with respect to this independent feature, you can see with respect to this independent feature,
you have to predict something.
And this is why which is exactly my dependent feature that you have to predict.
That's it.
That's all about my linear regression.
Now, the question is, let's say when you have you let's say when X is given for how you have to find
Y, how you have to find where the location is given.
Let's say let's say you have some use.
Let's say you have some practical use.
Let's it on the let's say you have some data, let's say on the basis of oh, wait, let's say you have
some Vitória Aleksi here.
You have some height that you have to predict.
Let's say you have some vague let's say in terms of centimeter, in terms of centimeters.
Let's say you have some weight, one hundred one twenty, one forty one sixty and ended.
And here this is this is exactly why this is exactly your height and this is your weight.
Let me let me.
It is all these things.
So let me.
So this is exactly your, let's say height and on the basis of height.
So you have to predict fit.
You can you can assume wastewater treatment as it's already, so let's say its weight is somewhere,
let's say, in terms of cagy and which was you in terms of not not in terms of bone structure, which
is only 58 or so.
It has some 60 to somewhere, let's say 66 and it is 72 telltales.
And so this is a data that is given to you.
This is my data that is given to all you can.
Tomatis athletic training data.
And let's say in future hats, in future, you have to predict for what can be the weight of a person
if a height of a person will be let's.
One hundred eighty centimeter.
So what you will do, you will find some kind of relationship from this data, you will find some kind
of relationship in terms of why it goes to see how you have to find and how you have to find.
So what is over there?
Let me let me consider very simple values that because I think you let me just open a new page because
you're on the basis of, let's say, years of experience.
You have some felony.
You will definitely achieve centrality of years of experience in this.
So this is this this is this is this is this.
So what what exactly is the meaning of this concept for this constant is nothing.
But what what exactly is my salary?
What exactly is my salary when my years of experience is zero.
But I can say when I am a threshold, what exactly is my salary.
That's it.
That's all about my seat.
So what you will do, you will put X zero over it.
So this is the thing where my wife goes to see.
So it means it means what exactly you salani, what exactly you salani what exactly is salary?
Ranee you have a zero years of experience that a simple meaning of this.
Why Alex policy.
And here this is simple minimal.
Seewald yet another question will arise.
How to find this and how to find the C how to find this and how to find it.
T. Let's say in a previous use because it will see, let's say you have to find what can we do it for
apples and having that much height you have to find out how to find how to find it to date.
The problem statement you can be selected.
What I'm going to do, I'm just going to plot this data that I'm just going to plot this data and is
going to say this is something my Y-axis, this is my legacy Access's.
So on the basis of height, basically on the basis of height, you have to predict what can we go with.
So I'm just going to zoom in on Y-axis.
This is.
This is my X here.
You have some data.
These are all my data points to do that, all my data.
And let's say in future X in future, you have this data point, you have this data point for which
you have to predict for which you have to predict.
This is my data point for which I have to predict.
So the equation that we will also, which is which we all have learned in our school, is basically
y khoshjamal democracy.
So if I'm going to draw a graph over here, so I will draw or I can see if I'm going to draw a line
of this, which is nothing, but it is just like this one.
It is just like this one.
So this equation is nothing but viscose to see.
That's what we all have learned about basic math, basic math, basically.
So now you will think, what is this and what is this?
And which is basically my.
What is this?
What exactly is this slope?
What exactly is the slope?
So this this this line will tell nothing.
It is just this ratio, all this Y and X.
So this is exactly why by X, which is nothing what which is just my slope, which is nothing.
But we just my slope.
So here I am going to say it is nothing but.
The rate of change with respect to this X rate of change, with respect to X, so if you have learned,
if you learn a bit of calculus, so this is nothing, but this gets written in such a very weird rate
of Y rate, y reckons the rate of change of Y with respect to with respect to this X and this is this
is what what Maisto.
Or you can see this, it goes to 2010 as well here.
This is exactly this is exactly my title here.
This is exactly why.
Teto so if I have to summarize this or I guess if I have to explain the slope it in layman's terms,
then I can say this is nothing.
But let's say what my intuition was.
This is my letter.
This is why.
Karlstrom Explicit.
So what is this Amoa here?
If I if I will ask you, what is this over here?
And you have to explain it easiest way.
So it is nothing.
But if if this X will increase or decrease by one unit, then what is a change in Y, that symbol meaning
behind your slope.
Or you can see at what factor.
At what factor, at what factor.
This Y will change if this X will increase by one unit or X will decrease by one unit.
So let me consider some examples.
Let's say this is why closer to X.
So if I were to say Y by X, so did not want to buy one.
So I'm going to simply say if X is going to increment by one unit, then Y gets increment by two unit.
That that's a simple meaning behind this, and if I'm going to say why close to half of X, so if I
will, I will say if if let's say if X gets increased by one unit over there.
So if X get increased by one unit over here, so Y gets increased by just half unit, that's what that's
what the meaning of the slope.
So let's talk about some more perspective in which our slope come into existence.
Selected Alexei.
I have let's say you have to compute X, you have to compute.
Let's say you have to compute slope between descent.
You have to compute slope between this and this.
So what you will do, basically, you will basically join both these points.
You will basically join with this point and you will compute distance between this.
This and distance between this, so this is nothing but just just Y, y, X letters, resistance's,
let's say twenty four and the distance is 50.
So here Mike Slope is nothing but twenty four by 15 in this case between between what.
This point I had a slope.
But what if that's it, but what if that's it, but what if you have to compute?
You have to compute slope at a particular point.
You have to compute slope at this particular point.
So what can the slope in such scenarios for here?
What we will do so let's say you would say no.
Here I have a single point of how to compute, how I can compute slope to what what what you can what
you can think.
Let's see what you can think.
Let me let me consider a point very close to this point.
Let's say I would consider a very close point to this and then I will conclude this.
So you will say that nothing what zettabytes, you know, up slope is not zero zero in such case.
In such case, in such this slope is nothing but Delta Y, y, Delta X, which is nothing but which
is nothing but be small change in Y.
It's more genuine because if you are going to consider already brought us to this point very close,
this point to this point.
So definitely you have some Minuti, you have some minuti, and that is exactly a slope.
So my slope is nothing.
But I school is nothing but the statewide tax, which is nothing but a small change in Y, and it gets
divided by this small change in X.
So that is exactly your slope at a particular point.
So in the upcoming session we are going to learn more about the regulation.
We are also going to find what can be my best line or I can for my use case, because it is not necessary
that this is a best practice deadline.
So we have to find our best line as well, depending upon the Nagios.
So in all of our upcoming session, we are going to deal with the scenario as well.
So thank you.
Have a nice day.
Keep learning.
Keep growing.
Keep practicing.