Hey, everyone.
So there are some few things related to course I would like to discuss in this video.
So I would highly suggest you please, please do not skip this video.
This video will give you a complete overview of what we are going to exactly learn in this course.
And there are some few important things related to course.
I will discuss later on.
All right.
So we'll get started of our journey with a very basic introduction.
And I'll be white and I'll be full, sir.
What are the different applications of an LP?
And then no much theory will just tape dive into installation.
So we'll see how to get started on Google.
Carl Levin, what?
I mean, mainly data, two libraries, which will be our focus, a special library for an LP and an
antique.
So in the next section, we will see some of the basic natural language processing related tasks and
all those tasks like a tokenization stemming limitations, stop 40 moil vocabulary, part of speech,
tagging, everything we will see with this special library.
And this particular cause is very much hands on.
So there are many practical projects on which we are going to work upon.
So one of them is a spam classification.
Then whether a restaurant reviews production will be a good or bad Amazon.
Hi Emily B Yelp Review Classification Project.
And then we will see two more projects.
Actually, one of them is a automated text summarisation and Twitter sentiment analysis.
So that is mainly our very first part of the course where mainly we are going to work up on.
Machine learning related technique.
And we'll be applying this machine learning technique on the top of this tax data in the next part of
this course, or I would say a second part of this course.
Our main focus will be on a deep learning part.
So we'll start with the basics of deep learning.
And for deep learning basics, fight with me, my friend.
Veejays going to join with you.
We learn about avoid inbreeding.
We learn about how to do this.
Text classification with advance deep learning technique like a neural network.
A recurrent neural network.
And there is a one full fledged project.
We will see like automated text generation with pensive look at us.
Anna, LSD.
And for those of you who just want to refresh their concept related to data science and data processing
data analysis.
So for them there are three Bonnar section IO Edik like Numpty Library, Pendas Library and Immaculately
Library Chris course.
And then in the appendix, hieratic a text processing.
So whenever you want to deal with any kind of filing a patent or tax, filing, a patent video, filing
a patent, how to deal with that.
So that part is included in the appendix.
So overall, I can say that it's a very comprehensive cause, complete causes for of practicals only.
And this is about the basic cause.
Or what do you.