I've seen a lot of naïve bayes explanations but this one is the best one and yet written in paper! THANK YOU SO MUCH. Thank you Indian guy, Indians are one of the most intelligent race in this planet
thank you dude. your approach of putting the words into the P(thing|thing) formulas is golden. ive watched a lot of videos trying to teach this concept and they just hit you over the head with the formula with little relevance to the variables and its awful. people are actually awful at teaching this. but you're actually doing it right. thank you.
2:14 I thought it was P(Sport|A very close game) and P(Non sport|A very close game) because if you write P(A very close game|Sport) based on the Bayes Theorem it will be P(Sport|A) P(Sport|very) P(Sport|close) P(Sport|game) . Not a big deal though just to clarify
At the end where you were deducing which category the sentence falls in, you did not actually use the Bayes' formula. You only calculated the P(B|A) part, where B = A very close game, A = Sports/NonSports. Am I right? If not, pls clarify. Thanks.
A posterior algorithms says. P(A|B) = P(B|A) * P(A) / P(B). Then P(A very close game|Sport) should be P(A very close game|Sport) = P(Sport|A) P(Sport|very) P(Sport|close) P(Sport|game) * P(Sports) / P(A very close game). Please clarify this point
dear u r very genious . keep it up. pls help me how to implement the naive example u taught us in python. pls tell me with the same example how to implement.
Very nicely explained @Code Wrestling. But I doubt your explanation on Laplace Smoothing. Alpha is not mainly 1. It can be 1, 10, 100, 10000. And 'd' is not the number of distinct words but distinct categories i.e 2 (sports and non-sports). Correct me if I am wrong.
But probability of sports is not multiplied which should be done according to Naive bayes likelihood * prior. I don't know why prior probability is missed
You are very smart, your video was informative. A little constructive criticism, If you are going to teach something in English and you have a foreign accent, you have to slow it down when you speak.
Coneptual Q : Does N Bayes assume independence. Because i guess you can only apply the formula if the events are independent. But in real world ebents are not really independent.
You need to work at a UNI. You make me proud to be indian bhai, seriously. Clear, step by step explanation while mitigating unnecessary info and focusing on fundamentals. You have gained a subscriber.
Why is it 11 (total words in the category) instead of 10 (unique words in the category)? Seems like the probability of a word being in the sentence is all about unique words.
can you pls cover a knn like that naive videos with code explanation ...i think you are a busy man ...just explain it on jupyter with pdf there is no need of webcam bhai ..pls _||_ pls bhai pls
I've seen a lot of naïve bayes explanations but this one is the best one and yet written in paper!
THANK YOU SO MUCH. Thank you Indian guy, Indians are one of the most intelligent race in this planet
His name is Adarsh he is my neighbour
thank you dude. your approach of putting the words into the P(thing|thing) formulas is golden. ive watched a lot of videos trying to teach this concept and they just hit you over the head with the formula with little relevance to the variables and its awful. people are actually awful at teaching this. but you're actually doing it right. thank you.
Very clear and detailed explanation. Thanks so much !
Excellent explanation.Now I got to know that why we use Naive Bayes algorithm to text classification.
2:14 I thought it was P(Sport|A very close game) and P(Non sport|A very close game) because if you write P(A very close game|Sport) based on the Bayes Theorem it will be P(Sport|A) P(Sport|very) P(Sport|close) P(Sport|game) . Not a big deal though just to clarify
I loved ur voice... Explination tooo👌👌👌❤️
Thanks a lot 😊
Worth for watching
Really great explanation...👏👏👏
Thanks a lot #CodeWrestling
Exactly what I was looking for.
Because of you we got 10 gpa in our final year project 🙏 ... Thanks anna..
hey can you share your code?
That was really nice...Can we expect whole Machine Learning series from your channel 😀
yeah definitely.. we will be uploading the whole series soon.
Very nice explanation.
such a clarity in explanation, waiting for more Algol's
Bro , you are great....Thanks for explaining NB-algorithm in such simple manner.
Thanks alot
Bro can u explain problems on this naives Bayes classifier please
super explanation, hats off .. please make some more videos on ML concepts
What a great explanation!
Perfect explanation, do you have any physical code to share.
you are handwriting so neat dude
Distinct words in training or also including test?
best explanation man thank you! you just earned a subscriber.
Thanks for the sub! Stay tuned and also share with your friends
I love your voice 😍 oh yes... And your explanations too!
Thank you ❤️
Thank you! 😃
At the end where you were deducing which category the sentence falls in, you did not actually use the Bayes' formula. You only calculated the P(B|A) part, where B = A very close game, A = Sports/NonSports. Am I right? If not, pls clarify.
Thanks.
Thank u so much.....very nice explanation
Please make more videos on ML
That's needed...thank you so much...brother👏
dude, you didn't calculate p( sport) and p (non-sport), you should multipy it at the final
Yes you are right ,He did not multiply prior.There was mistake.P(Sport) = 3/5, P(NoSport) = 2/5 .If you would multiply you can do it right
Yes prior probability is not multiplied
A posterior algorithms says. P(A|B) = P(B|A) * P(A) / P(B).
Then P(A very close game|Sport) should be
P(A very close game|Sport) = P(Sport|A) P(Sport|very) P(Sport|close) P(Sport|game) * P(Sports) / P(A very close game).
Please clarify this point
The P(a | sports) should be 2/3. Please correct me if I am wrong.
dear u r very genious . keep it up. pls help me how to implement the naive example u taught us in python. pls tell me with the same example how to implement.
i wanted to ask why have u used the laplace smoothing for non zero frequencies also? please explain
your final answer miss the prior?
Thank you so much sir...I was searching for the explanation of this algorithm from long time..Very well explained👍It helped me a lot😊
Thank you Supriya.... Stay tuned with us.. We will try to help as much as we can....
Kudos to u sir ....a big 🙏.....plze provide other ML algorithms explanation!!
Very nicely explained @Code Wrestling. But I doubt your explanation on Laplace Smoothing. Alpha is not mainly 1. It can be 1, 10, 100, 10000. And 'd' is not the number of distinct words but distinct categories i.e 2 (sports and non-sports). Correct me if I am wrong.
Awesome. Hats off to you.
Gud explanation.....please post videos about bayesian learning
Thankyou so much for this video.
It helped a lottt!!♥️
Thank you for the explanation.
Glad it was helpful!
But probability of sports is not multiplied which should be done according to Naive bayes likelihood * prior. I don't know why prior probability is missed
You are very smart, your video was informative. A little constructive criticism, If you are going to teach something in English and you have a foreign accent, you have to slow it down when you speak.
Thanks a lot!! I will surely work on your suggestion.
Coneptual Q : Does N Bayes assume independence. Because i guess you can only apply the formula if the events are independent. But in real world ebents are not really independent.
Yeah it assumes the events to be independent. For the dependent events we have other classifiers. #codewrestling
@@CodeWrestling Are they also probabilistc? anyexamples
Great explanation! But you should have multiplied by P(Sports) or P(No Sports) to respective formula
You need to work at a UNI.
You make me proud to be indian bhai, seriously.
Clear, step by step explanation while mitigating unnecessary info and focusing on fundamentals.
You have gained a subscriber.
Why you have not multiplied the probability of Sports and probability of Non-Sports to the final result
Please upload more videos
Why is it 11 (total words in the category) instead of 10 (unique words in the category)? Seems like the probability of a word being in the sentence is all about unique words.
anyone have link to download the ppt of this video.
Gud work. Please, share the video for Hunt’s Algorithm/Decision Tree as well
good video
really good one
can you make a video on instance learning?
Yeah definitely stay tuned with #codewrestling
really great
Thanks for appreciating!! #CodeWrestling
Word "a" appears 3 times in your text sentences (2 as first word, but that is irrelevant) so P("a") is 3/11
I think you only count 'A's in the sports category, because you are looking for the probability of 'A' being in the sports category.
@@66Horses66Lover66 yes you're right... There are 11 words in total in sentences that belongs to the sports category... My bad!
Thnku bro....
make more videos please.....
pro bubbly tea
:D :D
Nice
Thanks bro
BEST..
can you pls cover a knn like that naive videos with code explanation ...i think you are a busy man ...just explain it on jupyter with pdf there is no need of webcam bhai ..pls _||_
pls bhai
pls
Will definitely try to. :)
It's Bayes' Theorem... Not Baye's Theorem...
VTU student? xD
u too ?
me too 😂
@@CodeWrestling mai bhi :)
which college btw bro ??