The Math Behind Bayesian Classifiers Clearly Explained!

In this video, I've explained the math behind Bayes classifiers with an example. I've also covered the Naive Bayes model.
#machinelearning #datascience
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[@BrianAmedee]

'Clearly Explained' - and it actually was. Thanks man

[@NormalizedNerd]

:D :D

[@hayleyH997]

How he manage to explain something that a 1-hr lecture couldn't! Thanks mate

[@pradyumnabada5118]

Dude.. I lost count of the videos I watched to understand this but lastly, after seeing your video the struggle ended. Thank you so much!

[@jaster_mereel7657]

This was a very clear explanation indeed. Thank you!

[@NormalizedNerd]

You're very welcome!

[@bluestar2253]

One of the best explanations I've ever seen!

[@NormalizedNerd]

Thanks mate! Keep supporting...

[@parisaghanad8042]

That was great! I'm really glad that I found your channel. Thanks a lot 👍👍

[@guangruli4486]

Very clearly explained, thank you!

[@sopegue]

It was clearly explained as mentionned in the title. Thanks a bunch !!!

[@sye9522]

HUGE thanks for perfectly delivering the whole concept in one video bro!!

[@EduAidClassroom]

LOVED IT!!!
Awesome Explanation! Can't thank you enough...

[@dannysammy8972]

Yes, this was actually well explained. Thank you :)

[@jefersondavidgalloaristiza3410]

Very nice explanation and perfect illustrations!!

[@leolei9352]

Very clear explanation!

[@aurorasart9458]

Thank you very much for your work! Nice explanation!

[@NormalizedNerd]

You are welcome!

[@miusukamadoto6805]

Thank you very much for the video. Clearly explained indeed, the only part I couldn't get completely was the discretization.

[@swethanandyala]

very nice explanation thank you so much

[@user-wr4yl7tx3w]

This is really well explained.

[@sobana653]

Nicely explained!

[@nikolai228]

Amazing video. thanks.

[@sayonsom]

Great explanation :)

[@dzmitryk9658]

Awesome! Thank you.

[@fmt2586]

hey, thanks man, very clear explanation.😀😀

[@thebjjtroll6778]

Amazing teaching skills

[@telusukondifirstuu9221]

I love this Exolaination 😍🥰😘
Thanks a lot ❤

[@atulyadav9712]

Great explanation

[@arielalvarez88]

Really good work, congrats

[@NormalizedNerd]

Thanks man!

[@vojinivkovic9533]

great explanation

[@dpaul3447]

Thank you so much man!!

[@hasben0]

Well done👊👊

[@mehditavakoli2492]

Thank you!

[@radoyapanic998]

In the last part of the video you said we can fit a known distribution to a continuous set of data. However, you continued to then write that the probabilities can be calculated by taking the product of the pdf evaluated at different values of the feature and label. The pdf does not provide probabilities however, as it needs to be integrated to inform one of the probabilities of an event. This part of the video seems imprecise.
However, the video in general was great. Thanks.

[@sumedha1051]

love this!

[@adityaprasad3356]

very helpful🥺🥺

[@SarahGhiyasi]

Thank u it was great.

[@daniilsukhovv]

bro, best explanation I could find

[@NormalizedNerd]

Thanks bro :)

[@user-or7ji5hv8y]

Great explanation.

[@NormalizedNerd]

Glad it was helpful!

[@MrDaniel560]

HELPFUL!!!!

[@prar_shah]

Love this

[@lucasqwert1]

in the last part at minute 11: What is the function f to fit a known distribution? Thank you for answering!

[@aditya.singh9]

truly amazing

[@NormalizedNerd]

Thanks!

[@noname-anonymous-v7c]

9:37 you made conclusion based on P(X=[0,2] | Y), I think the correct way is to calculate P(Y|X=[0,2]). In case P(Y=1) is very small, the answer can be Y=0.

[@nickgannon7466]

well done

[@AnasHawasli]

Great video man great
herre is a sub

[@imadeit6587]

I am appreciate your work

[@NormalizedNerd]

Thanks a lot!

[@DANstudiosable]

Well explained, a quick revision for Naive bayes. I forgot why it was called Naive until i watched this video 😂😂

[@NormalizedNerd]

Thanks! Haha.

[@zouhir2010]

thumbs up
thanks

[@PritishMishra]

If I search for any ML Algorithm I just first check your channel If you have created the video on the same... You are my first preference for ML/DL Algo Explanation. Just a request please make a video on Deep Learning Algorithm too like CNN, RNN & LSTM "from scratch". It will really help people who want to become practitioners in AI like me.

[@NormalizedNerd]

Thank you so much ❤
Writing CNNs and RNNs from scratch are pretty hectic...maybe some day I'll try.

[@PritishMishra]

@@NormalizedNerd Waiting... you are our only hope who can teach us Mathematics of ML with cool animation, That's why requested you! Thanks.

[@muhammadzubairbaloch3224]

sir please more lectures.
I am seeing after too days later your lectures
made some advance NLP and CV lectures or AI lectures thanks

[@NormalizedNerd]

I will try my best to upload more frequently.

[@aymericalixe1310]

Maybe i'm wrong but I think the hypothesis is not that X1 and X2 are independant but that X1 and X2 are conditionnaly independant. It was very clear otherwise thank you !

[@NormalizedNerd]

In naive Bayes every feature is treated as an independent feature that's why it's called naive.

[@chitranghosal879]

I think the hypothesis is that you assume each feature to be (w.r.t other features)
1) globally independent (in the global sample space)
2)conditionally independent w.r.t the occurence of each class label (under the subset sample space where the particular class event has occured)
If these assumptions are not met, then it does not seem possible to build the mathematics, because as far as I see,
if events A and B are independent, that does not naturally imply conditional independence between events (A|C) and (B|C)

[@high_fly_bird]

The explanation is so cool! But it would be even cooler if you added some examples with continious features and fitting a distribution, this part wasn't so clear...

[@mahirjain8898]

so goood

[@sayantansadhu6380]

It was like a revision for class 12 probability 😁😁

[@NormalizedNerd]

Yeah simple yet effective concept.

[@signature445]

Sir is this like
Bayesian classifier deals with conditional probability ?
Naïve bays classifier deals with joint probability ?
Thanks in advance.....

[@NormalizedNerd]

Yeah!

[@joaomatheusnascimentogonca7633]

10:51 How does this work? wouldn't the probability that Xi = xi be zero, given we're using a continuous distribution? Because of the "=" sign

[@quanghuynh1570]

you saved me

[@Fuktron13]

I wish you were my professor

[@kunalsoni7681]

Nice ⭐⭐⭐⭐⭐

[@Ilham-lj3me]

and how aboit gaussian NB?

[@plumSlayer]

You areee Amazing. I love your Indian Bengali accent ( just a guess hehe make me a voice analyzer if i am right XD
)

[@harshitdtu7479]

10:37

[@xritzx]

3b1b's bro is here

[@NormalizedNerd]

Haha :3

[@pushandeb187]

liked that

[@abdulkarim.jamal.kanaan]

Hello people from the future! :D

[@anon_148]

independant moment

[@davidmurphy563]

Ok, I've given up on the video after 45 secs. You said "stated clearly", if you hadn't I'd have kept watching.
You point to an array of features called X. What are they? Are they features of the array itself (its size / rank / dimension?), are they features of the thing the array of describing (measurements in a house?), or a list of possible attributes (the ingredients on a pizza?) Then you introduce a label. So what, is this like a python dictionary?
Plus, I've no idea what sort of issue we're supposed to be tackling? Is it probability? Is it rationality with limited knowledge? I only guess that because I've heard of Bayes before.
Instead you launch into calculations when I have not the first idea what you're calculating. Why would I listen to that?
Tell you what, I'll give it another 30 secs. If there's no illustrative example / clear explanation of what the hell you're covering I'm gone.

[@davidmurphy563]

Nope, 30 secs later and it's absolute horseshit.

[@mahedihassanrafin7493]

just quit confusing people