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Lecture 2: Introduction to Data Science
Nafees AI Lab
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5/7/2025
In this lecture, you will learn what is Data Science, AI and ML. How are all of these connected. Concept of Deep learning.
#Data Science
#Artificial Intelligence
#Machine Learning
Category
π
Learning
Transcript
Display full video transcript
00:00
We have a machine learning
00:03
which we have seen in computer
00:05
that there is a capability
00:07
of pattern or phase
00:08
or recognize
00:09
pattern
00:10
and a general example
00:12
of machine learning
00:13
and this pattern
00:15
how do we see
00:16
how do we see
00:17
some algorithms
00:18
that we will discuss
00:19
however
00:21
I will show you
00:22
a landscape
00:23
I will show you
00:24
a playground
00:25
in which we have
00:26
all data science
00:27
or machine learning
00:28
artificial intelligence
00:29
and we also have
00:31
a game
00:33
to play
00:34
to play
00:35
to play
00:36
to play
00:37
clearly
00:38
artificial intelligence
00:39
artificial intelligence
00:40
what she is
00:41
going to do
00:42
that
00:43
artificial intelligence
00:44
artificial intelligence
00:45
is
00:47
what is
00:49
that
00:50
Which we need
00:51
to exude
00:52
intelligence
00:53
that
00:54
we have
00:55
let's say replicate
00:57
let's say
00:59
if we copy
01:01
machines in this
01:03
artificial intelligence
01:05
artificial intelligence
01:07
basically
01:09
exist
01:11
normal artificial intelligence
01:13
specific
01:15
artificial intelligence
01:17
artificial intelligence
01:19
applications
01:21
specific to a job
01:23
for example self driving
01:25
car
01:27
facial recognition
01:29
this is a specific
01:31
artificial intelligence
01:33
application
01:35
imagine
01:36
a self driving car
01:37
Tesla
01:38
which is better
01:39
self driving
01:41
car
01:42
this is not
01:43
that if computer
01:45
car
01:46
can drive
01:47
you can eat
01:48
good
01:49
it
01:50
this
01:51
why
01:52
why
01:53
job
01:54
specific
01:55
specific
01:56
artificial intelligence
01:57
this is a specific
01:58
artificial intelligence
01:59
which is more
02:00
difficult
02:01
which is general
02:02
artificial intelligence
02:03
for example humans
02:05
generally
02:06
intelligent
02:07
that if a human
02:08
car
02:09
if a human
02:10
car
02:11
can be
02:12
good
02:13
computer
02:14
so
02:16
this
02:17
is the main
02:18
playground
02:19
now
02:20
artificial intelligence
02:22
basically
02:23
another field
02:24
exists
02:25
which is basically
02:26
called machine learning
02:28
machine learning
02:29
machine learning
02:30
machine learning
02:31
what does
02:32
algorithm
02:33
use
02:34
which is
02:35
pattern
02:36
which is single
02:37
application
02:38
machine learning
02:39
machine learning
02:40
under
02:41
one
02:42
field
02:43
exists
02:44
which is deep learning
02:45
which is called
02:46
ds
02:47
now
02:49
remember
02:50
this deep learning
02:51
basically
02:52
we can understand
02:53
that we have
02:55
machine learning
02:56
to be specific
02:58
that we have
02:59
machine learning
03:00
achieve
03:01
using deep learning
03:02
and the machine learning
03:03
basically
03:04
which is basically
03:05
machine learning
03:06
using machine learning
03:07
machine learning
03:08
remember
03:09
machine learning
03:10
that you have a task
03:11
I don't go to their
03:12
specifications
03:13
because they have a dedicated section
03:15
we will discuss them
03:16
in detail
03:17
but I will give you
03:18
generic holistic bird
03:19
idea view
03:20
so you can win
03:21
deep learning
03:23
or machine learning
03:25
and the goal
03:26
is that you have
03:27
artificial intelligence
03:28
created
03:29
another terminology
03:30
which basically
03:31
I have used
03:32
last lecture
03:33
was
03:34
data science
03:35
ds
03:36
basically
03:37
is
03:38
data science
03:39
and data science
03:40
remember
03:41
that
03:43
you have to work
03:44
in machine learning
03:45
or artificial intelligence
03:46
or deep learning
03:47
it will be based
03:48
on data science
03:49
so this means
03:50
one
03:53
data scientist
03:54
should be
03:55
artificial intelligence
03:56
and
03:57
an
03:58
artificial intelligence
03:59
let's say
04:00
engineer
04:01
or expert
04:02
data science
04:03
should be
04:04
data science
04:05
can be
04:06
idea
04:07
so basically
04:08
data science
04:09
is
04:10
that
04:11
deep learning
04:12
part
04:13
is
04:14
data science
04:15
deep learning
04:16
ds
04:17
by the way
04:18
deep learning
04:19
dl
04:20
and this is
04:21
machine learning
04:22
also
04:23
part of data science
04:24
because
04:25
data science
04:26
comes to
04:27
machine learning
04:28
deep learning
04:29
and
04:30
which
04:31
is
04:32
artificial intelligence
04:33
which
04:34
is
04:35
idea
04:36
now
04:37
data science
04:39
and artificial intelligence
04:40
which I have
04:41
talked about
04:42
approach
04:43
two
04:44
Cocaine
04:45
and
04:46
the
04:48
one
04:49
are
04:50
one
04:51
of the
04:52
two
04:53
two
04:54
three
04:55
here
04:56
is
04:57
two
04:58
two
04:59
two
05:00
three
05:01
three
05:02
one
05:03
one
05:04
three
05:05
two
05:06
three
05:07
three
05:08
four
05:09
You can see the roots in the roots and see how it is, which is basically the tree, or the seed, which is basic in the gut.
05:22
The other way is that you can go to mango and eat mango.
05:27
Either you can go to root or fruit.
05:31
This means that this data science and artificial intelligence is two ways.
05:36
Or you can see the roots, which are mathematical algorithms, how to design and how to design.
05:45
The other way, you can see the applications.
05:47
For the job specification, only the applications are important.
05:51
Because if any company has hired you, you can have an application.
05:57
Basically, we have to focus on the application.
06:01
Fruit and root.
06:04
Okay.
06:05
So far, so good.
06:06
We will close this lecture.
06:07
In the next lecture, we will do a short project.
06:09
Just any other time, everyone saw that.
06:12
You can go to our next lecture.
06:13
Ok, great.
06:14
Let's do this.
06:15
Just let the protection Europolet know.
06:16
You can see.
06:17
DΓ©b Wild!
06:18
You can see a couplePM freely.
06:19
It takes place with aδΈι.
06:20
your hair and the sense of catching bamboo.
06:21
You can see some of the instructions in the future.
06:22
If there are not too few clips on the top, you can see.
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