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