Text Vectorization NLP | Vectorization using Python | Bag Of Words | Machine Learning

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#RanjanSharma
As we know in Machine Learning we convert categorical Variables in to Numerical Columns by using One Hot Encoding and get_dummies.
Now Ever wondered How we will convert Text in NLP to numeric Values ?
To know , please check out this video.

Hit the Like button if you really liked the video.
Code is uploaded in to the Google Drive Link and Github link

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#vectorizationMachineLearning #vectorization #vectorizationPython #vectorizationNLP
#vectorizationinNLP#textVectorization #countVectorization #BagOfWords #countvectorizer #tfIDF
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I was checking infused on steps
Rwa to token text cleaning then vector and then apply ml models or algo
Thank you for clearing

smcgpra

Nice Ranjan...Good explanation..You have good Teaching Skills.. Your channel will have nice growth in Future

kirandeepmarala

Congratulations 1000 subcribers good going 👌 keep it up

crazypahadan

Great explanation but I'm confused why it only tokenized 11 words instead of the actual 12 words. It seems to be missing the token for the word "I".

rontackaberry

A very good explanation thank you. I'd like to see you demonstrate a matrix where lemmatization is used so I can visualize how the text pre-processing isn't just reduced to a numerical value but also see how sentiment is assessed, for cases where it is needed.

smudgepost

In your theoritical explanation "I" has considered as unique one but whenever it comes to practical implementation then "I" has not considered as unique one. Can you please explain why does it occur?

souvikganguly

i got error in line no. 42 in your code
sentenses =
NameError: name 'corpus' is not defined

teacode

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