Hello guys.
So we are going to continue the discussion with respect to NLP.
And in this video we are going to discuss about some of the amazing use cases and these use cases.
We use it every day in our day to day activities.
Right.
So let me go ahead and let me share my screen and let me talk about every use cases.
One by one will be discussing about it.
And this will give you a clear idea about what is natural language processing.
Now here I have opened my Gmail tab.
Now in this specific Gmail you will be able to see that, uh, let's say that I really want to write
some email, right?
So I'll say hello, Krish.
Okay.
And let's say I made a spelling mistake.
Okay.
I wanted to clarify.
So I have actually written a wrong spelling.
So here you can see that automatically the spelling has been corrected.
Right.
And this is all because of NLP.
And in this also we can like suppose if we receive an email from someone, right, we can also write
an automated email from them.
So that basically means it will basically tell us that what kind of text it will auto generate and it
will give it for us, and we can actually send it.
So I would like to I wanted to clarify about the data science and let's say I'll write course.
Okay.
Now let's see what it basically gives.
It can also give you a suggestion like this.
And now you can basically select the course right.
So this is also one amazing use cases.
And we use it every day.
Now let's say I go to my LinkedIn.
So here uh Rudra Pratap Singh is one of my student.
And automatically we can see that two tags automated replies there in the LinkedIn itself.
And this is also an amazing application from NLP, so that if you really want to just provide this,
any of this particular two tag, you can just click it and you can reply back.
So in this, in short, is saving you a lot of time.
Now similarly, if I go with respect to one more application that is Google Translate, and I hope everybody
uses this, let's say that I just go ahead and write one language.
I'll just say, how are you?
Right.
And I probably want to convert this into Arabic or no.
Okay.
So it looks something like this with this specific language.
Suppose if I really want to convert this into Hindi.
So then you can basically say that automatically the text is coming like here right?
In Hindi we basically say kya haal for how are you.
So this is also happening because of NLP.
Amazing application.
And we use this and in most of the websites also, right.
Let's say in LinkedIn, if somebody is posting in some different language below, only you will get
an option of something called a C translation.
So once you click that particular thing and automatically gets converted to English.
Not only that guys, let's say that I'm just going to go ahead and search for Krishna.
So Krishna is my name.
I'm a YouTuber.
I'm also a co-founder of inherent AI.
But here if I probably go and collect on select on images.
Now here you can see that it is already been able to detect my image and it is in turn converting that
the text into images.
It is understanding the text and it is basically thinking okay, it knows that it is talking about myself
and based on the web, right?
It is being able to capture all the images that is available in the web.
So this is also an amazing thing.
And probably if I probably go ahead and select videos here.
Also, you'll be able to see all the videos of my in my YouTube channel, or probably in all the other
public platform that I have, which is being accessible over a web.
So this is an also amazing example.
Text to image, text to video.
And a lot of research is also currently going on with respect to different different companies.
There is an amazing company which is called as hugging face and hugging face have actually created an
amazing models and solutions for solving question answering session question answering.
It has basically question answering model, some summarization, text classification, translation.
Right.
All these particular use cases are there.
And you can see that how many different kind of models it has like for question answering.
Right.
It has 2380 models summarization.
It has five, eight, eight models uh for text classification as it has this much models.
Right.
And here you can see that how many companies are basically using this Google AI, Intel spin bridge.
Bob Microsoft, Grammarly.
Right.
All these things are basically using this.
Uh, and again, this is an amazing application of NLP itself because all these applications are, in
short, taking the text data only and performing different kind of tasks on top of it.
So one more amazing thing that I really want to show you as an application with respect to Alexa and
Google Assistant.
Uh, I have some of the Alexa home configured over here.
I can control my ACS and lights, but with respect to Google Assistant, a simple application, I can
go and open it.
So here I have my Google Assistant.
Now here you can see that hey Google, do I have any doctor appointments tomorrow?
Sorry, I can't find anything on your calendar that matches that.
So now here you can see that, uh, already it is being able to retrieve from my calendar.
It is able to see whether I have a doctor appointment or not tomorrow.
So this way it is really, really easy finding all the tasks with respect to most of the critical things
right that we do in our day to day activities.
So we use extensively NLP a lot in our day to day activities.
And uh, this is what we are going to learn in this specific course, how we can build this kind of
models, what kind of text pre-processing actually happens.
We'll get a crux idea behind it, how these applications also works.
Okay.
So yes, uh, this was about the second video of mine, uh, with respect to natural language processing
and machine learning.
I will see you all in the next video where we are going to start the syllabus.
I will see you all in the next video.
Bye bye.
Take care.