Natural Language Processing|TF-IDF Intuition| Text Prerocessing

[@raghavendra2096]

This guys is a legend, he doesn't have to explain anything this deep but he's got that passion to teach, really happy to see this!!!!!! Good that i noticed his page thanks Krish :)

[@jesuscolin834]

I want to thank you, I've been watching your videos for a few months. I speak Spanish, and my English is not the best, because of your accent at first I thought I would never understand them because I had never heard that accent, but now I end up studying more English to be able to watch your videos faster. Thank you for your dedication and being part of the creators who democratize their knowledge, leaving everything in videos that are free for us.

[@Chuukwudi]

@jesus Colin. See the big words you’re using and you say you’re learning English. I don’t believe you!

[@gregmaland5318]

I like the way Krish really makes an effort to TEACH, vs simply regurgitating information.

[@M0N0T1C0L0URS]

Hi Krish, just wanted to say how helpful and well-explained your videos are. Looking forward to watching more of your series on NLP!

[@anonymoususer4949]

this is the most clear explanation of tfidf I've ever seen. Thank you so much!!

[@miguelfernandes657]

Wow, great explation! I not only understood how to calculate it, but also WHY it is used! Thanks!

[@danielejiofor3493]

Not a rocket science.. Absolutely mind blowing the way he breaks this to the simplest meaning.

[@kartikeyasharma8672]

Thank you so much! man i cant express how much this video has taught me since i missed the lecture in school! Keep up the good work

[@BalaguruGupta]

You're really amazing Sir. Neatly explained. This explanation is just an example for "THE POWER OF SIMPLICITY". Thanks a lot

[@davidw3351]

I've spent a bunch of time trying to understand TF-IDF, but with this video it just clicked right away. Thanks!

[@sakinafatima]

best channel for data science topics not focus only on theory but the implementation of theory as well.

[@Beyond_Bhaarat]

A great teacher is one who makes complex things simple. Thank you very much!!

[@SahityaChikkala1327]

Thankyou so much Krish sir❤️ I'm having exam tmrw. Without the revision now I can directly write my exam...

[@lakshmipriya5050]

The way he teaches is just Wow :)

[@rajshriingle1595]

I really want to thank you as I cleared my ME dissertation because of I am able to understand the working of TFIDF

[@01kumarr]

Finally I saw the picture behind tfidf.. All doubts cleared. Thankyou

[@webfalconstechnologies2850]

hi krish, your are indeed making the videos in a manner that is so easy to understand. you are doing real good for the community and for aspirants that find it difficult to learn all the algorthims. Dont stop making such videos. God bless you

[@joaopauloalvesdasilva9888]

Always in many articles that I've read 'TF-IDF' I could never understand the meaning, but now it's different. Thanks for your video.

[@ParthPatel-iy8ku]

Yeah Exactly

[@erickcordovagavilanes3873]

Thank you! You explain very good, I send you a hug from Ecuador

[@aj_actuarial_ca]

I really love your way of explanation. You're explanation of the intuition followed by a demonstration of the code is really useful.

[@Forever._.curious..]

You are such an amazing teacher ❤📖

[@lawvalerie5522]

A pity that your sentence 3 calculation got cut off from the video, but i got the gist of your explanation. Thanks for making this clear and simple to understand.

[@shreyasb.s3819]

I don't have any words to appreciate again and again. really awesome explained. Thanks for ur help

[@Raguna088]

Wish I had you as a professor back in my college days, great vid! Thanks so much for providing a great explanation.

[@arslanyousaf2426]

Your way of teaching is very impressive.

[@sruthiparvatha8790]

Thank you so much for this simple and clear explanation. Understood the concept on the first go, keep up the good work.

[@sheikhshahbaz2848]

Hey Krish M fan of ur teaching . You just teaches from very basic to advanced.

[@nadellagayathri]

Thanks for the NLP series of videos. Please make a video on domain knowledge gaining. Teach us the domain knowledge wrt Datascience Fine with any specific domain. Also make a video explaining what are the data points to look for choosing a machine learning algorithm for example when do we need to go for Random forest for classification and when for SVM etc. I know you have covered couple these concepts in your earlier videos but a playlist with only these details at deeper level would work. Keep the good work going. Thanks a ton. You are a great inspiration.

[@HarshSharma-tp8bi]

Hey Krish , I have been watching your videos for a while now .. i must say..you are AMAZING.. I have been doing small projects based on machine learning concepts , and thanks to you , your videos really helps me to understand the main concepts , both in theoretical and practical implementation..Thanks A Lot... Looking Forward For your New Videos ...

[@ArpitYadav-ws5xe]

Excellent krish. Keep Motivating. You are doing well

[@doelie2472]

Amazing explanation, very clear and short steps, love it!

[@Darwaishhh]

Sir tomorrow is my AI pre board, thank you for this video really helpful

[@mulukensefiw]

Let C = number of times a given word appears in a document; TW = total number of words in a document; TD = total number of documents in a corpus, and DF = total number of documents containing a given word; compute TF, IDF and TF*IDF score for each term

[@satwikchivukula8905]

C/TW, log(TD/DF), C/TW * log(TD/DF)

[@sudarshandev6369]

just wow Sir because ur explanation is like awesome.

[@iabhijitroy]

Something to be aware of is that TF-IDF cannot help carry semantic meaning. It considers the importance of the words due to how it weighs them, but it cannot necessarily derive the contexts of the words and understand importance that way.

[@Neuraldata]

Really loved it ❣️....u simplify things efficiently!!

[@ashishmondal5857]

You are the teacher of half of the subjects in my masters degree. Until I watch your videos, I feel the topic is not complete.

[@meysamjavadzadeh]

what an explanation . just love it . keep it coming sir👌

[@vivian_who]

You are such a talented teacher! Thanks!

[@sandhya266]

Wonderfully explained sir ....i understood very well 👍

[@MAYANKSHARMA_NITK]

Great Job Krish (Y)

[@moulikgupta6979]

Thanks that was an excellent explanation, could not have been any better...

[@aaroldaaroldson708]

Why Indians are so good in teaching?

[@bigo-b]

Great breakdown to layman words. Keep the work. I like the energy too :)

[@ParthPatel-iy8ku]

Great explanation ✌️👍

[@chung_jf]

Hi Khris, loved the explanation. Thanks for this.

[@amalaljohari9358]

You should teach in our college, this is a great video

[@zabilmaooz4435]

What an amazing guy! Very easy explanation, thank you.

[@trumanknight873]

Fantastic video! Well done and well explained. To the point, and clear.

[@MadiyarZhunussov]

This is truly beneficial! Thanks for tutroial

[@rohitsailas4028]

What a teacher!! Thank you

amazing lesson!! thank you so much for this well-explained video!

[@srivvivek]

awesome, very well explained.

[@mathketeer]

Thank you. You are a great teacher.

[@jeffvarghese7657]

Krish, great teching. WOW

[@oshtontsen5428]

Great Explanation! Thank you for making this video

[@KirtanPatel552]

Your videos are very informative bro...keep going 🤩

[@yashwanthkumar4890]

Correct me if I'm wrong. Bag Of Words ( count or Binary ) and TF-IDF will only give the values based on the frequency of the occurance and doesn't process based on semantics. To get the output based on semantics we need to use Word2Vec. @Krish Naik

[@duonghoangbang2106]

I understand, thank you so much Mr.

[@vickyk2447]

thank you for this video. very well explained!

[@saniakanwal3519]

Excellent explanation

[@MM-qq6xp]

I love your videos. Thank you, man.

[@sandipansarkar9211]

Thanks krish. Superb video once again.

[@Omhange]

When Anurag kashyab making scifi movies

[@danielmagical6298]

great tutorials Krish, keep it up my man!

[@fantasyapart787]

sent1, sent2, sent3 should have mentioned in rows instead of in columns and in the TF formula the edenominator is total no of words in sentence instead of just no of words in sentence ..BTw good job man...

[@nafassaadat8326]

I am confused about that, bcz in some video explained (number of docs containing term/total number of doc) but here he explained in contrast.

[@abdulmunaim7765]

Amazing sir👍

[@harshiit_singh]

Thanks a lot Sir for providing such great contents.

[@josecosta3878]

Excelent vídeo. Is there any videos where you explain how to implement TF IDF for better performance of sentiment analysis algorithms?

[@vaibhavgupta4413]

This is awesome 💛💛💛💛🙏🙏🙏❤️❤️❤️❤️❤️❤️❤️❤️❤️

[@vishnujatav6329]

Wonderful and very much useful sir. Thank you very. I request you to clear confusion between sentiment & semantic analysis

[@umeshtiwari800]

Very good explaination

[@vuinhquy8419]

Thank, this video help me so much. Very easily understand

[@deepankarpathak983]

Thank-You so much sir for Crystal clear explanation.

[@Ab_Jannadi]

Thanks for your effort. It was a good explain .

[@sarasmith1647]

I really appreciate the simple but effective explanation of the concept, but I have a huge dataset to get the tfidf of, so how to write a code for it as it can’t be calculated manually

[@arjitdabral8449]

Bhot sahi bro one day you'll surely blow up 🔥🔥

[@chetanmundhe8619]

very good explaination, really helpfull, plz continue your good will

[@fallen4fate]

Amazing video! Thanks!

[@avneetsingh7649]

Sir pls make your videos on SQL Server also sir, pls sir having lot of problems with sql server, SSRS etc. And sir want one advice from you sir, did I have to learn SSRS(Sql Server Reporting Services) also while learning SQL Server.

[@commenter5170]

good explanation!! quick quo though -why is it called IDF ?

[@neelimapatel1561]

Is stemming important before TFIDF or can we use *ing* *ed* etc with the words? Pls reply plsssssss🙏🏻🙏🏻🙏🏻

[@davebowman1968]

Incredible video - thank you

[@devarshshete8566]

Great explanation

[@GauravDuttaGD]

There is typo in title Actual : Natural Language Processing|TF-IDF Intuition| Text Prerocessing Expected: Natural Language Processing|TF-IDF Intuition| Text Preprocessing

[@junaidyousaf4602]

This is really good explanation. Sir pls make video on hate speech detection....

[@nomanshaikhali3355]

Where to use which NLP model like in which situation we have to use BOW and in which we have to use TF_IDF? Looking forward to hearing from you soon!!

[@farhana8417]

Really good sir

[@maulindusarkar4581]

I used TF-IDF on the para you considered here, but got something like this: paragraph = """The boy is good. The girl is good. The boy and girl are good""" array([[0.78980693, 0. , 0.61335554], [0. , 0.78980693, 0.61335554], [0.61980538, 0.61980538, 0.48133417]]) But I should get two zeroes in the first and second rows right, according to the formula?

[@aryasvn1434]

Hi krish naik, please answer my questions. Why TF IDF use log 2? What the purpose of the log 2?

[@aryasvn1434]

@krish naik

[@kalyansundarsamanta]

This is really good explanation! How to select top words when the sample size is large? Suppose, if I select Journal Articles for the study which contain more than 5k words, then how can I select top words from them?

[@heteromodal]

great video, thanks so much!

[@scientistgeospatial]

It was a good tutorial. It rather shows how poor the TF-IDF technique is, by removing frequent terms (key terms).

[@pathikghugare9918]

So how do we use it to test on unseen data ? since there is a possibility that some unseen word could come which is not present in our vocabulary because of which denominator of IDF may become zero right?

[@skviknesh]

Really great videos!!!

[@VishalNaik-gq4vl]

This can't be true that I learned this concept in 8 minutes

[@tejassrivastava6971]

at the end do we just add up all the features of every word? like : i love to code OP: 0.458962 = f(i) + f(love) + f(to) + f(code) ?

[@harikrishnanm5109]

It was really helpful. Can u make videos on Grammer Correction using Rule based methord, Language Models & classifiers.

[@bedantasaikia7074]

Very helpful video.. thank you so much! 🙏🏼

[@stupidbutcurious6206]

Hi Krish, very well explained. However it still leaves me with another question. While i know this is the correct way of calculating the TF, IDF and finally the tf-IDF i still don;t understand why sklearn comes up with different tf-idf values, even with your set of examples in this tutorial Would you help me understand

[@anonymousxyz3856]

The sklearn uses a modified IDF value while calculating TF*IDF