Hello, everyone, and welcome to the world of natural language processing.
So let's see what natural languages are in our daily life.
We do communicate with each other and all those communication happens inside the natural language.
Heider in terms of tax, in terms of speech, we are all surrounded by the text.
Like we keep sending e-mails as a mess.
We keep reading Web page.
And everything has been surrounded by texts.
Now, just think about the speech.
We speak to each other like we watch television, listening to the radio, like we use the smartphone
views the Aleksa, we use the Google now or Microsoft Cortona.
So every where there is a natural language, in one hand, natural language processing is all about
finding some hidden, meaningful insight out of those natural languages.
And this close is all about finding those useful insights from the texts, how you can do prepossessing
from the texts, and how you can apply some advanced machine learning and a deep learning algorithm
on the top up tax data.
So natural language processing is a field of artificial intelligence.
And here, mainly humans and computers are interacting in terms of natural language, not in terms of
computer languages.
Now, NLB natural language processing, a sort of revision of.
Natural language processing, which has an ability to go to machine like how machine can read, how
Machine can understand.
And something like derives some meaning from the human languages.
And that is what the main object to behind and epiphany.
Most of this natural language processing technique relying on machine learning and deep learning related
algorithm to find those meaning.
And we learn about a lot of machine learning and deep learning implementation.
Horner Text Data in discourse.
So let's see some of the applications of natural language processing like machine translation.
So, as we know.
Google Translate what I would say, Google machine translation.
It can do the translation between hundreds of different paths of human language with such a high accuracy.
And that become possible with a recent architecture of neural machine translation kind of architecture.
Speech recognition.
So in our daily life, we keep using of a smartphone, our smartphone can recognize your wise with very
high accuracy nowadays.
So your Alexa have publicity.
Google now or Microsoft Cortona, they can detect and recognize your voice with very high accuracy.
And that's all become possible because of natural language processing.
So speech recognition is one of the very hot topic nowadays.
And it is among the very large part of many of the products.
Sentiment analysis.
So in this particular cause, we will see implementation of this sentiment analysis with various different
views.
So finding some positiveness or a negativeness involved in attacks.
And it will help us to check whether customers are satisfied with your services are not.
So based on the reviews provided by the customer, we get the information like how satisfy a customer
is.
Question answering system.
So Apple City, Google, OK?
They're all state up.
I question answering system.
They communicate Vitas in terms of human language in their replies.
Also in terms of human language.
So they're kind of a virtual assistant for us.
And this is one of the very heart applications nowadays in an LP field.
A question answering system automated summarizes in.
Nowadays, there are lots of tax available on Internet and we don't have time to read every single piece
of tax.
So this automated summarization will give you a summary in a very brief tome so that you will get a
IDL, something like a 5000 word article into just maybe 200 word articles.
Chad.
So headboards are currently operate on several channels.
It will be on Internet.
It will be available on our mobile application.
It is available on a messaging platform.
And nowadays, many of the businesses are implementing those chatbot so that our virtual assistant will
interact with us based on your cratty.
He just cannot solve this query then only.
It will be forwarded to a legal human text classification.
Heider.
Text is spam or a ham.
Heider, Texas.
Good.
All right.
We'll be right back.
So that is also one of the main application of.
An AP.
Character recognition.
So out of your image, what exactly a character is that is the character recognition, spell checking.
So every time we do a Google search Google, we keep giving you suggestions that what you should search
with your spelling correction also.
So spell checking isn't one of the very important application of natural language processing.
You can go to a best application available on Internet for spell checking like a family app.
It has an ability to find all those grammatical check.
Scan your all the text.
Find all your mistakes, typos in a sentence.
Is there any structural problems associated inside tax and many more things beyond that?
It can detect out of your tax.
All right.
So that is all about the some of the few application I have listed, but many important application
about the natural language processing fee.
All right.
See you in the next video.
We'll get down to my installation part and straight into how to do some pre processing step with your
tax data.