Hello, everyone.
This is a very special session on data science, so let us try to discuss about what is data science.
Let us try to understand what are the major films which are used interchangeably and what is the real
meaning behind them?
So the first thing which we will discuss is.
The difference between artificial intelligence, machine learning, deep learning and neural networks.
So when we talk about artificial intelligence, artificial intelligence is a simulation of human intelligence.
So you remember the movies which we used to watch, the movies where we had several Roubaud which were
trying to replace humans, which were capable of thinking, which were capable of doing different things
like.
Opening a house, like talking to people just like humans, talking on a phone call or doing different
tasks like preparing food or maybe doing our database could do so.
Artificial intelligence focuses on bringing these changes into a small robot or any technology, anything
which is capable of thinking like human or expressing itself in the five senses that human has is called
artificial intelligence.
And they are a little far from it.
We have not reached that stage when a robot can actually replace a human being, but we want to make
a world where a robot can take a lot of walk from human and give a little measure down to us human beings.
So thinking like robots can actually replace humans is very far from right now, and this is a completely
different point of discussion.
So without going further into that, we need to understand.
That artificial intelligence refers to the simulation of human intelligence in machines, and these
machines are programmed to think like humans and mimic the human actions to personify the human behavior.
We want these machines to do walks and different tasks, just like a human being would do.
So that may also be applied to any machine that exhibits the traits associated with the human mind,
such as cloning or Problem-Solving.
So if I have a particular robot or anything which is helping me to maybe cook, then I want that machines
to tank in a way so that it can help me out in creating new recipes.
So I can use it for that or different scientists are doing different searches on medicines, so for
a typical kind of behavior legacy, for a typical kind of disease, a particular chemical works really
fine, or that is one component which works very fine.
So a machine can actually learn what all of the chemicals work in a way to improve from that disease
or prevent from that disease and create different chemical constituents or different components, which
could actually help in curing the disease.
And then it can actually assist a human being by doing all the research work, which a human would have
done behind it and helping a human with those components, which he can basically try and experiment
and test for the.
So here is artificial intelligence is actually reducing the time of the research, which a human being
would be doing and assisting a human being with reducing a lot of time and a lot of effort.
This is what artificial intelligence is focused on.
Now, what is machine learning when we talked about artificial intelligence?
We wanted a machine to mimic the human behavior.
A machine was supposed to think like a human or walk like a human, like it could walk like a human
or maybe could talk like a human.
But to do these things or simply saying, let's say we want a machine to let us, they just simply go
off like a human.
Then in that also we need the machine to learn a lot from different languages.
Let us say we want the machine to learn the language English.
Then it would have to understand the difference between different words and different spellings.
It should need to understand what a about a particular word would mean.
And in English language, there are different words which could be used with different meanings.
It could have passed from present form.
So all these things, the machine has to understand that now we humans have learned these languages
over the time.
While the machine would have to understand this in the simplistic manner, in the form of zeros and
ones, because machines cannot really understand what the machines can only understand zeros and ones.
So here the machine would also have to actually be corrupt this entire language and convert it into
a form of zero and one by.
I'm trying to understand that.
So this learning experience and again, it will have to learn this all this information not in 20 years,
but maybe in one hour or two.
This is the kind of training time the machine would be taking.
We want the machine to learn over a smaller period of time and from a very vast space of knowledge.
So what machine learning helps us?
It is an application of artificial intelligence that provides the systems, the ability to automatically
known and improve from experience without being explicitly program.
So I don't want to bring my machine to do a specific task.
So let's say we want to go walk with let us say we want to work with a set of in-car.
So in case of a sense of anger, I don't want to program to walk with, let us say I want I don't want
the program to take a left or right or maybe see a stone and then it should stop or see someone crossing
the road and then it should stop.
I don't want to to each and every line for that one I wanted to learn is I want to give it a few examples.
I will give it a thousand or ten thousand examples where a person would be driving a car.
I'm the machine should learn from it that it should learn that OK, there was a person who was crossing
the road.
This is something which I found from the image at that point of time.
And often this image appeared of a person crossing from the road.
The action which was taken by the guy was to slow down or to put the brakes.
Then when something huge was placed in front of the car, the action which was performed by the driver,
was to slow down again or to.
The thick or divert its direction slightly towards the left or slightly towards the right.
So these are different examples which would be given to the court and it would automatically learn from
this, so we don't want to program anything.
We don't want to program the machine.
We just want to give it a lot of examples and a lot of samples from which the machine can learn different
and find out different patterns.
And from these patterns, it can predict what it should be doing in future.
So machine learning focuses on the development of computer programs that can access the data and use
it to learn for themselves.
The next thing which we have is neural networks.
So we have talk about artificial intelligence.
We have talked about machine known, now machine learning is a.
S field of artificial intelligence machine learning is a part of artificial intelligence when we talk
about a machine to go for a machine to work.
We want to learn different ways of working or we wanted to learn different languages using the process
of machine learning.
So machine learning is a part of the entire process which is undergoing artificial intelligence.
While Neural Network is one of the algorithms which helps implementing machine learning.
Machine learning has a lot and lots of algorithms in place and neural network is just one of them.
And the neural network is based on the how our brain works.
So in our brain, again, we have a lot of neurons which are interconnected and each of these neurons
keep sending signals from one to another so that the information is passed and converted and encrypted
internally in our brains, and then we could get a result out of it.
We can make an inference out of it.
So a neural network is a network which is trying to.
Somehow
depict the brain or does show how a brain works and it is a network or circuit of different neurons
or in a modern sense, artificial neurons composed of artificial neurons or nodes.
So these neurons and mode which are connected with each other, they try to pass information from the
input to the output so that it can internally create the network kind of thing.
Or let's us it just try to form a circuit of information which could give us the relevant.
How how does that what is going on behind Windows that later.
But for now, what you need to understand is it is just trying to become a human being, how the human
brain moves.
Next is deep learning, so deep learning is an artificial intelligence function that mimics the walking
of human brain in processing the data for use in detecting objects, recognizing speech, translating
languages and making different decisions.
So deep learning is in hired version of neural network, which would be said neural network is a very
basic symbol of neural network.
And when we have a complex neural network with a lot and a lot of layers.
So if we talk about just this much, this could be said as a neural network.
But when a lot of layers are included in case of the neural network, it becomes a deep network which
is used for deep learning.
So artificial intelligence without human supervision and throwing different data and structures out
of it is called deep learning.
So deep learning is basically a neural network with a lot and a lot of layers.
So now that we have understood about artificial intelligence, machine learning, deep learning, neural
networks, we need to understand what data science actually is.
So data science is the process of extracting knowledge and insight from large volume of data.
It is simply learning from the.
So let's try to understand the simple language, so when we're are trying to study for a particular
exam, what do we want?
We want a lot of material which has a lot of information in that.
I will figure out around two or three books, which will have all the data and all the information related
to the course, which I'm going to have to answer in the exam, which is let see tomorrow.
Now, once I have these books, I will read each and every page from the book.
And when I will read from the book, I will try to find out different pieces of information which are
important, which are the patterns.
So let us see.
I have a science exam, so I will try to pick out some important concepts from it.
I will learn about photosynthesis.
I will learn about how water evaporates.
I will learn about different things which are going on inside this particular lecture or in this particular
chapter.
And then when tomorrow someone would be asking me the question regarding this particular topic, I will
be able to answer that.
So to answer a particular question, I need to have visited that concept.
So this is one data sciences I want to extract and I want to know one from a piece of information,
and I want to learn different patterns from the information so that if any question is asking me around
this particular topic, I will be able to answer that.
Remember, I am not being given the exact questions which will present in the book if I I'm able to
know from the exact questions and.
Give answers to the only those exact questions, then that is what deboning if I was able to do that,
if I try to do that, then I can simply do that by simply switching the pieces of information in a database,
maybe, and then giving answers from that.
But that's not what I want to do.
I don't want to go to a question paper or a list of question papers or a bunch of question papers and
20 questions and being able to answer only the questions of the come out of those 20 questions.
No, I don't want to do that.
Here, what I want to do that I want to do is I want to learn the concept and this is what data science
is learning and extracting the knowledge and patterns and the insights from these structured and unstructured
data.
So data science is an interdisciplinary field that uses scientific methods, processes, algorithms
and systems to extract knowledge and insight from many structural and unstructured data.
So let us see, I have a lot of e-mails and I want to find out if a particular e-mail is a spam or not,
so I will try to find out some specific patterns which I find in spam emails such as a lottery or bank
account or free or Huddy.
These kind of words will mean then someone is trying to hurry up and try to grab this opportunity or
get something free goodies from someone.
So I don't want to get those.
These are completely spam, so I don't want to get into this family, so.
I'm finding out these pythons died when it was another meal comes out saying you will get these things
free or you have won something, you won the lottery, then I can completely ignore these and put them
in my spam folder.
So this is the pattern which I have extracted from the emails which I have caught, so data science
is related to data mining, machine learning and big data.
So I can have any amount of detail and I can use that to work with these.
So this is one big differences.
Now, why do we really need the signs?
We need data science for making decisions.
In an organization, there are a lot of organizations which are hiring different data scientists and
there are a lot of knowledge on a lot of information which is present.
Nowadays, we have so much of information in different files, in different databases, but no one really
knew how to use them.
But now we have data science and machine learning, which will help to find out different patterns from
this data.
And if I want to make a particular decision, then I can go back to this data, I can learn from this
entire data and then make a particular decision, because nowadays the decisions are not made based
on indications.
Someone cannot really see that, OK, if we want to increase the scenes, then let us add a bungalow
packaging.
We need to have a lot of data to prove that actually having a pink packaging will improve the sales.
So the decision making has to be fact based and it has to be a realtime decision, we need to make real
time decisions.
Legacy.
Is a flight which is about to take off in five or two minutes.
And I want to find out if the machinery is working fine, if the plane is flying.
So for that, I need to analyze all the mechanics of the plane so that things can not be done in one
or two or three hours time span.
I need to have a quick decision making here, so I need to have that machinery in place, the system
in place, which can check a plane in real time and find out, OK, this is good to go.
We can go for it, people.
And also, there is a lot of intense competition which makes having these real time decisions and precise
decisions very.
So this is life data science is very, very, very important.