Hello, everyone, in this session, we are going to learn about machine learning concepts, OK, but
before seeing the machine learning concepts, let's understand what is A.I.?
Yeah, if you really see the computer software that thinks and acts like human beings, it it's a software,
please know they can decide inside the hardware entity.
OK, but it's essentially your software.
It's a rapidly growing field.
We all know that.
Right.
And the impact is already there.
Some of the examples that you can think of, it is already being used to predict diseases, diseases
like heart disease, cancer diseases, proactive detection.
That's what we're talking about.
It's being used to detect and predict diseases even before it occurs.
It is used to detect fraud again proactively.
It is used in recommendations as to what is this recommendation system.
You watch a movie or listen to a song or watch a movie clip either on the Facebook or YouTube or any
or voted up.
After a while, you realize that the app is making some suggestions to you, right?
You can watch this.
You will enjoy this song.
Right.
So what is it?
What is the suggestion based on?
It is built on what is known as a recommendation system and recommendation system is built using artificial
intelligence.
Right.
Face detection.
Once upon a time, we used to mark attendance by signing and then we had access cards.
Then the fingerprint reader came and now we have face addiction serving as a means to mark attendance
and also to prevent intrusion.
You know, for surveillance activities.
We are using face detection to open apps.
Right.
So all this are applications of here.
So that's why I said is already here and its impact is only going to increase in the months and years
to come.
Right.
So having seen what is here is a software.
Right.
Let's understand the two different types of here.
One is called strong A.I. and the other is called Vecchi, strong A.I. We are talking of systems that
are thinking at the level of human beings.
OK, that is that can think and act like human beings.
Right.
What you see in movies.
Right.
The fictional characters that you see in movies.
Right.
They are all talking of strong A.I. It is also called this artificial general intelligence and artificial
superintelligence.
But if you really see we are not there yet, OK?
It's in the realm of research establishments, OK?
People are scientists are actually researching this.
We are far, far away from a world where all systems, artificial intelligence systems can think and
act like human beings.
OK, we are not there yet.
So where are we today?
We are in the realm of what is known as Vecchia, but don't get fooled by the term vecchia.
It is only to make a distinction between strong A.I., right, the soup of the artificial super intelligence
that we are calling the current level of artificial intelligence as we get into even with the so-called
Vijaya.
And we are solving a lot of problems.
Right.
But how are we doing it?
We are solving problems by detecting patterns.
OK, we are going to see what is a pattern.
And please know that this is the dominant mode of A.I. today.
OK, so which brings us to the question, what is the pattern?
Right, a pattern is something that gets repeated in a recognizable way, OK?
In fact, I will go a step further and say that pattern is a consistent, recurring characteristic.
It's a consistent but a recurring characteristic.
Right.
So the system, the computer programming will identify something that gets repeated.
Right.
And uncover the pattern, you know, in your data.
And through that, it will solve the problem.
Right.
That's how the world of A.I. is working today.
And as I said earlier, this is the dominant mode of A.I..
OK, so it is through these Vecchi systems, the so-called V.K. systems, that we are solving many,
many problems.
Right.
So that's why I said Wikia is a misnomer.
OK, don't get fooled by that.
It's only in comparison to strong A.I. that we are categorizing the pattern recognition type of artificial
intelligence, as Rekia is only for comparing and contrasting.
Right.
So now when you see this graph right, what are the things you can not just take a moment if you want,
you can even fast for a while, right past the video to see what are the patterns you can note in this
graph.
Right, if you see the peaks are occurring in the month of July, which drops the lowest points are
occurring in the month of January, the other pattern is the highest point in a calendar year.
Right.
Is progressively increasing.
Right after nearly the same break, you see the difference between this and this, this and this, you
know, it seems to be nearly at the same rate.
Right.
And the pattern gets repeated every calendar year.
Right.
So those are some of the patterns that we are able to uncover and we are able to uncover even with our
audience.
Right.
But what if the number of data points are more likely, if there are millions of data points?
How will you uncover the pattern?
That is why you need the software, right?
That is what an assistant will do, because in this, you are able to uncover the pattern even with
your eyes.
And you will see that for the next year.
The highest point will be in July 16 and the lowest point will be January 16.
And the highest point will be somewhere close to two hundred thousand.
You can make such a rough estimates, right?
You don't need even a calculator for that.
You can do that even with simple manual calculations.
Right.
So we are able to uncover a pattern, you know, when through our eyes that just by observing if the
number of data points or fewer, if the number of data points are more, we need systems.
Right.
That's what we are going to see, you know, in the course of this program.
Now, let's see how is applied and this is prediction, right?
I mentioned this example earlier.
The typical database that is used to build the pattern will consist of patient records with sugar,
B.P., heart disease, cholesterol, genetic predisposition, whether anyone in the family has had a
heart disease or not.
OK, BMI, body mass index.
So these details of people who have had heart attack and people who have not had a heart attack will
be there in our dataset.
So the system will uncover the pattern contributing to heart disease.
Probably people who have a heart disease have a certain level of a certain level of sugar, certain
level of cholesterol, so on and so forth.
So it is those values, right in combination is what is called a Zapater.
So the system will uncover the pattern that is contributing to heart disease and venera in future.
Any such values of BP, sugar, cholesterol, BMI, genetic predisposition and such values are fed into
the system.
The system will see if the pattern that it previously identified as contributing to the heart disease
is present in the current dataset.
If it is present, the system will see this individual is likely to develop a heart disease.
If it is not there, the system will say the individual is not likely to develop heart disease.
Please note that I'm using the word lightly.
That is because we are talking of a probability think we are talking of a chance occurrence because
we can't be 100 percent right in prediction, because we are not God and we are still human beings who
have developed the system right.
We are trying to play God, but we are not God.
Right.
But so are accuracy is never 100 percent so which is why we use the terminology likely to develop.
So the same concept is what is applied in loan default or prediction also by someone who is consistent
in paying the loan, you know, will have a regular stream of income, probably supported by his significant
other income also.
Right.
As a stable job isn't a thriving career and so on and so forth.
Right.
Someone who is likely to default will exhibit the exact opposite.
I probably experience the layoff rate works in an industry which is in a downfall.
Right.
And so on and so forth.
So the algorithm, the computer program will try to uncover the pattern.
Right.
That is contributing to a loan default.
Once the pattern is identified and I say pattern, it will it will create another set of values that
taken together leads to a scenario of a loan default rate.
So once that pattern is detected, it will check whether the same set of values are the same characteristic
is there in the current dataset.
Also, once it is there, it will say that this individual is likely to default.
Right.
So this is how the world of ER works.
It tries to uncover the pattern.
OK, from the given dataset, it tries to uncover the pattern and applies it for future.
I hope you understand now how the world of works.
Right.
So having seen examples, having seen how it works, let's see some more aspects related to it.
What do you see?
You know, in in headlines and in media, it's all about the right.
But if you see in real in this case itself, we are trying to learn about machine learning.
So what is and what is machine learning?
If you really see.
Yeah.
Is the headline that that's what is flashed in the media.
But the technology that is driving A.I. is actually machine learning.
Right.
So all the pattern recognition that I talk about, the core underlying sciences, machine learning,
and when whenever we talk of machine learning and artificial intelligence, you will also hear about
another technology called deep learning.
Deep learning, you know, use an advanced form of machine learning can be thought of as a subset of
machine learning.
Right.
I hope you understand this.
Now, let's understand machine learning in more detail using an example.
OK, so I'm inputting three apples, the red colored apples, the green leaf, OK, it's in this particular
shape, I'm giving three images and I'm referring to these three images as apples.
I'm helping the computer system to understand that an object, if it looks like this, it is known as
an apple.
So the system, the model has been developed in such a way that if any object is input, it will check
whether it these characteristics are displayed or the characteristics are displayed.
It will say it is an apple.
The characteristics are not they are not displayed.
What will it say?
Can you think of that?
It will say it is not an apple, OK?
Why does it say it's not an apple?
Because all that the system knows is if these characteristics are there, the red color, little green
leaf, right, this particular shape, if these characteristics are there, it is known as an apple
in these characteristics are not there is not an apple.
Right.
What if I feed this particular apple?
If I feed this particular apple, what will the system say?
It will still say it is not an Apple one, because in this the color is different, right?
This is pink, this pinkish apple Pinkel an apple that has this is a dark red apple, right, 100.
OK, now let's see one more example, if I feed this image of the apple, what will the system say?
Will it say it's an apple or not an Apple thing?
It will say it is not an apple.
Because the leaf, right, the the proportion of leaf is on the right, but here, if you see the progression
of the leaf is on the left.
We are human beings, right?
We know that the pollution can be on the right or left on both sides.
The system doesn't know, the system knows based on what you're fed.
What we are fed is a dark colored apple with this particular shape and with the conclusion of leaf on
the left side.
Those are the characteristics that we're fed and we have labeled them as apples.
But here, the leaf is protruding on the right.
So the system will say that it is not an apple because the characteristic of protrusion of leaf is on
the right and not on the left right.
What if I feed an orange?
And you said it's not an apple?
Right.
So this is known as what is called a supervised learning, that is a reference is provided.
If an object were to look like this, I'm giving the reference, OK, or I'm giving a label known as
apples.
OK, if those references are annotations or labels are not there.
The system will try to group similar type of objects based on the characteristics you see, I have fed
images of apples, the brightly colored, the pink colored and the bananas.
Right.
So the system has grouped the red coloured apples together.
The pink coloured apples are grouped together and bananas the group together.
So this type of machine learning is known as unsupervised learning because the references or labels
are not.
If you see the the the models that are used today, majority of the models are in the realm of supervised
learning only.
OK, the example that we saw this is prediction and loan default prediction would come under the area
of supervised learning only.
OK, then why do we have unsupervised learning?
Unsupervised learning is used in the area of segmenting customers.
Normal way of segmenting customers is based on one or two dimensions.
Based on the revenue the customer generates are based on the type of the customer.
The customer is a large customer of small and medium business customer or an individual retail customer.
Right.
So those are small things.
But what a systems can do is uncover new segments and that wealth of information for any organization.
Right.
So supervisor unsupervised learning or what are used extensively as as of today.
Right.
And even between the two supervised learning is what is used more than unsupervised learning because
unsupervised learning is primarily used when references are not there.
The third type of reinforcement, the third type of machine learning that is also applied but not as
widely used as supervised learning is reinforcement learning.
You're the system learns from mistakes and responses.
If you feed an apple, it will probably say a mango, right?
Then you say, no, it's not a mango, it's an apple.
Right.
System notes it right.
And this kind of cycle goes on and on, probably tens and thousands of cycles.
OK, the system learns and then it correctly says that it is an apple.
Right.
So this is known as reinforcement learning, as I said, is not as widely used.
OK, so the one that is widely used is supervised learning, unsupervised learning.
As I said, it's used primarily in the world of customer segmentation.
What is the other benefit of unsupervised learning?
It is also used to detect outliners fraud detection, unsupervised learning is used right.
The biggest advantage with unsupervised learning is you don't need these references or annotations or
labels.
Right.
So this is something that we saw earlier, machine learning, the three types of machine learning,
supervised and supervised and reinforced.
Right, there's one more way in which you can classify or categorize machine learning, what is called
classification type of machine learning or regression type of machine, but we also call it a classification
algorithm or regression algorithm.
Let's understand this with an example.
Right, in the case of regression, we are telling what is the temperature going to be?
Right.
It's going to be eighty four degree Fahrenheit if I see whether it'll be hot or cold based on the temperature,
right.
Based on the temperature for tomorrow, then I am talking of classification, if you are to extrapolate
this to the student example, if I tell how many months the student is going to get OK, then I am talking
of regression.
If I say whether the student will score high marks or low marks or medium range marks, then I am talking
of classification.