All right, everyone.
So the first day was sex and where we are going to start learning about the real natural language processing
stuff.
So let's get started with what we are going to learn in this section.
So this section is all about all those basics and l.p technique.
We will be learning.
So while analyzing data, the first day will matter.
You could to take it and apply those matter on your tax data, which is nothing but a tokenization.
Then we'll move towards finding a route from your data.
Stemming the limitation.
How you can remove all those only important words like stop words from your tax.
How to apply some kind of vocabulary and a rule-based matching.
How you can pack some particular part of your tax, a speech that is called as a part of speech tagging,
once you assign some kind of noun or a vote to some particular word in your sentence, it makes much
more sense for further interpretation of your tax.
And if you're just flat on all those words, maybe those tax will be completely different.
That in which context we are using those back.
So that is the kind of analysis we will do.
And that is called as a part of speech, tagging that whether your individual vote is known pronoun.
All right.
Well.
Name into Dietrick of mizin, one of the very important and useful thing, while analyzing your tax,
whether some particular work.
Let's say it's Animala.
So it will be like on a place or a mountain.
Let's see some.
But we will name like temps.
So it will be a reworked name or not.
So it's entity will be some posehn.
So this we identifying the entity from every single token we got earlier.
That is called EZA named Entity Recognition.
And then we will see a sentence segmentation.
So these are some of the basics related to natural language processing.
We will be learning in this section.
So see you in the next video.
We'll get started with our first very important tokenized sense step for tax analysis.