Text Preprocessing | NLP Course Lecture 3
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- čas přidán 15. 06. 2024
- In this video, we'll break down the steps involved in getting text data ready for analysis. Think of it as cleaning and organizing text so that it's easier to understand and work with. This process helps us get valuable insights when we're dealing with large amounts of text information.
Code used: www.kaggle.com/campusx/text-p...
Assignment Links:
api.themoviedb.org/3/movie/to...
api.themoviedb.org/3/genre/mo...
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✨ Hashtags✨
#DataScience #TextPreprocessing #Stemming #Tokenization
⌚Time Stamps⌚
00:00 - Intro
1:01 - Introduction
4:03 - Lowercasing
7:53 - Remove HTML Tags
12:44 - Remove URLs
15:16 - Remove Punctuation
23:29 - Chat word treatment
26:20 - Spelling Correction
28:11 - Removing Stop words
31:25 - Handling Emojis
34:11 - Tokenization
49:18 - Stemming
57:50 - Lemmatization
1:01:33 - Assignment
Anyone following this playlist, my recommendation to them is to please do the assignment, I was shocked at how little we learn by just watching, I did the assignment and what can I say, I was stuck a lot of times and at the end, I completed and now I regularly do Text Preprocessing by making my datasets from Rapid APIs, It gives one soo much flexibility to work on a dataset they created.
Mam can you explain me or refer some notes or videos on using API's and Create own Dataframe
Hey Hari! The assignment links given above are not directing to the tmdb website, and if I search of TMDB directly on google, it doesn't work as well. Can you tell me how you did that?
You are a rare gem , I can simply put that in clear short words❤️❤️
Exactly, rarest !!
Again Sir your are a great person on you tube.. your explanation in every domain and for every topic is great...i followed you ML playlist A-Z and now i start watching NLP.. i hope you will complete your ML series soon and this too and also making great series for us with new and needed emerging thigs ...Thanks Alot Sir!
your lectures really help me to understand NLP Text Preprocessing , Thank you so much!
This series is amazing!
Thank a lot Nitish ....i dont have enough words to express my gratitude.
Very good explanation. your explaining every single details. it's very helpful for beginners. and assignements also very intresting.
i feel like why im not found your channel before but lucky to have right now
You are really a great teacher, thank you so much for coming up with such informative videos, Thanks a lot
Ur way of explaination shows ur concept clearity and ur efforts to prepare this topic...keep it up.
Your videos are full of knowledge. Thanks a lot for this 🙏 you deserve more subscribers... it can attract more viewers if you divide your videos into smaller parts. People generally don't want to engage with long lectures.
Hi, Could you please make the next video on the same IMDB data set and show us how to analyze the linguistic features of the training dataset? I have recently gone through your previous NLP (Movie Review Sentiment Analysis) videos. However, I was quite interested in finding out how can we analyze the linguistic features and what all different algorithms can we apply apart from the Naive Bayes on the same IMDB dataset. PS - your videos are amazing!!! the way you teach the concepts has helped me to understand the basics of NLP. Thank you so much!!
Your lecture are really helpful...all consept are very clear
Gold contents. Thanks for the video
very detailed explanation. Kudos to you.
Sir you are a lifesaver.Thankyouuuuuu
so far so good.....awesome x 100
Literally, All In One !
Nice assignment Sir. Thankyou
sir could you please share notebook, it is not available on given link
Series is amazing sir 👏 kindly provide the regex lecture in the description
Awesome lecture 🤗🤗🤗❤️❤️❤️❤️
please tag notbook in description,also please complete NLP playlist
You didn't link the video for regular expression in description, can u update it
You are God for me in learning data science
Thank you, you are just awesome. Much waited for this video. You explain things better than other youtubers. Keep it up...!!!
You are the best sir😊.
Amazing video but from where can i download the notebooks.
I would also request you to share the notebook url's in the video description.
Sir I have been following you for long time and glad that I found your channel and learning so much from you and for that I am greatful and thank you from bottom of my heart.
Till now I was working with Google colab but as I am moving towards deep learning now I think it's time for me to buy high end laptop..
But I am at a loss which one should I pic if I go for rtx 3080 then the price is way to much for me ... Having this confusion for past few weeks can you please please please suggest me a laptop for ml&Al&dl learning projects and my budget is 1400-1500$
I will be greatful .
Or you may make a video on this topic
Thanks! for the great content!! One small suggestion can you also give us sometime to write code you are explaining otherwise it becomes theoritical.
Congo sir for third video🥳🥳
great content
Its helpful for me ❤️
Does anyone know how to apply word/sentence tokenizer on columns? if you know please reply.
Thanks!
I checked both methods (removing punctuation)but they are similar in speed sometimes the second one is slower why is it so
You are Amazing Sir Love from Pakistan.
Hi Sir. Regarding the assignment, how can we meagre genre id and genre type with movies data-frame?
I got stuck there.
Sir thank you so much😊
Can we get the pdf of code that you have written in ths vedio
sir can you please share the link of "chatword" used in chatword treatment
Thankyou Sir .
Easy way to remove punctuations.
import string
import re
def remove_punctuation(text):
# Define the set of punctuation characters
punctuations = string.punctuation
# Remove punctuation using regular expressions
text_no_punct = re.sub('[' + re.escape(punctuations) + ']', '', text)
return text_no_punct
While using the lowercase conversion function shown at 7:23 , I am getting below warning,even though conversion is successful. Can you let me know if any other way is there to do conversion or we can ignore the warning?
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
Ignore
Thanku Bhaiya
that was awsome tutorial can you pls link to your Regular expression video ?
The way of teaching is cool loved it.
One doubt 12:00 in remove_html_tags() it only removes the tags but in real time when we scrap data from a website it contains tags like style, script etc which aren't required in the text mining or NLP process.
Just wanted to know is there any other better approach or method that could solve this thing.
Thanks in advance for everyone who tries to solve this.
where is the notebook link?
the above link only showing csv file.
Thank you
You are the best
where is the template notebook?
Sir at timestamp 3.30 you said you will provide notebook , can you please provide that , Thank you
How to use textblob for a large dataset?
Awesome
Thanks sir
@campusX I cant find the codes. can you plz plz give the link?
where is notebbok of this lecture?? could u please just upload the notebook
Nice video👍
Sir the notebook link is dysfunctional .....pls upload the notebook discussed in the video
can anyone send the link to the notebook, the given link does not work
Hi Sir, Can you please re add the data links here as unable to load it.
thank sir
Hello Sir, can you reshare code, the link you shared has no code....Thanks !
awesome
Sir when you will start series on Deep learning..
Nice video
@campusX : can you please suggest how can we use text for regression (for eg. use comments to predict number of subscribers)
please tag notebook used in this video in description,
I am not able to find the notebook of the code.
Could anyone please help?
Great
Getting problem while doing assignment as I have no idea how to get data into a dataframe using api.
Dhanyavaad. Can you also start a series on web development ?
You're just an excellent teacher
hey
are you working in NLP or other in python?
i need your help
can you help me?
can you please share the colab file
Can Anyone explain me how to create dataframe for assignment using thia API . PLEASE!🙏
the notebook/code is not available .!!!
how to make this dataset ?
sir code page nai mil raha hai kaggle me ,can any one help?
In the assignment, Can anyone have the solution on how to change genres ID to it's Name ?
Hello sir your code is unavailable please make it available.
Couldn't find the Notebook link!
please someone help me with converting that chat words file into dictionary
Done.
One suggestion: sir, ek udemy course banaiye.... Data science bootcamp...
I got an error by using spacy library which is OSError
56:30 with 'e' probable hai...
I understand but it was confusing me.
And Thank you Sir such a good video ❤
where is the notebook ?
How to explain a data science project in interview for fresher please make it one video.
notebook ka koi saved version nahi dikhara hai.
Where is the video on Regular Expressions?
anyone tried the assignment? if please reply I have few doubts
best
actually tokenization doesn't work in dataset. can u write code to tokenize only the reviews in ur dataset
sir note book link ?
do you have videos on Nlp with deep learning ?
Yes. Check my playlists
can any one share the notebook ?
Can you please provide solution for this assignment
❤️
👍
sir TMDB website is blocked in india
how can i convert the chat txt data to a python dictionary?
mila kya iska solution?
text = '''AFAIK=As Far As I Know
AFK=Away From Keyboard
ASAP=As Soon As Possible
ATK=At The Keyboard
ATM=At The Moment
A3=Anytime, Anywhere, Anyplace
BAK=Back At Keyboard'''
dictionary = {}
# Split the text by new line and iterate over each line
for line in text.split('
'):
# Split the line by the equal sign to get key and value
key, value = line.split('=')
# Add the key-value pair to the dictionary
dictionary[key] = value
print(dictionary)
Bhaiya how you converted chat text data to python dictionary?
text = '''AFAIK=As Far As I Know
AFK=Away From Keyboard
ASAP=As Soon As Possible
ATK=At The Keyboard
ATM=At The Moment
A3=Anytime, Anywhere, Anyplace
BAK=Back At Keyboard'''
dictionary = {}
# Split the text by new line and iterate over each line
for line in text.split('
'):
# Split the line by the equal sign to get key and value
key, value = line.split('=')
# Add the key-value pair to the dictionary
dictionary[key] = value
print(dictionary)