Naive Bayes Theorem explained with simple example (easy trick)

[@ashawinimangate4908]

This is the best video on RUclips ..I have looked for more than dozens of video but this one is the easiest and superb.....recommended for all enthusiasts

[@navneetacharya831]

Good Explanation of Theorem!!.. probably for calculation of P(Weak/Strong...|Yes) , formula can be leveraged e.g. P(Weak|Yes)=P(Yes|Weak)*P(Weak)/P(Yes)=6/8*(8/14)/9/14=6/9...

[@shankrukulkarni3234]

A child can understand ur explanation bro... Thank u...
It should be P(yes/x) and P(No/x) at end

[@recordingsp7833]

Thank you very much yogesh..and very truly said by mr Prashant...thanks again...

[@StphRolland]

Thank you for making this video. I had only a vague understanding of Bayes theorem applications for machine learning. The choice of your real life example, and your detailled step by step calculation are perfect to get it totally. Many thanks.

[@RishabhJain-pk8ty]

Really nice, many people on youtube have taught this concept incorrectly. But you made it very simple. Thanks a lot.

[@Ayonokyoji]

this is also incorrect

[@venkys15]

One of the best explanations of the Bayes Theoram. Thank you so much

[@radhaingle4664]

Thank you very much.... I am happy to be the first person to like and comment... Thanks again... It's helping me a lot...

[@fatim3510]

THANK YOU VERY MUCH YOU JUST SAVED MY LIFE.

[@jordiferrer2940]

Thank you for showing the workings step by step.
⚠⚠Two clarifications to make your video 10/10: ⚠⚠
1. The final equation should be P(yes | x) and P(no | x). You got it the other way around.
2. Naive Bayes assumes all the parameters are intependent (P(A|B) = P(A)*P(B)). Although it is a naive assumption (e.g. sunny days tend to be warmer), it allows us to do nice estimations of probabilities.

[@prashantgore4725]

Thank you very much friend..once again you proved you are best.... you have made it very easy to understand with an example...thank you ..pl keep posting...

[@andersgalmen3795]

Great step by step explaination and slow enough to grasp it, for the listener you could just say "P" instead of repeating the word probability a hundred times =)

[@justinjose9618]

best video that is on internet, thanks man

[@shylashreedev2685]

Well explained, very clear with the concept and calculation, Thank u so much

[@theanonymous92]

Thank you so much this is a very simple approach the other table making approach used to confuse a lot❤❤❤❤

[@shizakhurshid4250]

Thank you Bro u really made my day. It is very easy to understand now and it really helps me in my final exams as well, so once again thank you.

[@joshuajenishzibhu101]

i search lot video for naive bayes but this is the best video for understanding

[@bharatsoft8886]

That's an awesome way to explain the things!!!!

[@PIYUSH74428]

this is actual teaching......amazing

[@aslamcoding]

Thanks ** 1000000 sir.
Very nice explanation.
in first attempt i understood .
Thanks a lot for teaching in a simple and easy way to understand.

[@sharanyask725]

Thankyou so much sir, it is a very easy and a nice explanation.

[@prasanthsaikolli4921]

superrrrrrrrrrrrrrrrrrr brooooooooooo🙏🙏🙏🙏🙏 Easy to under stand!!!!!!!!!!!!

[@fatmaabdulla9327]

I finally got it.. Thanx a lot 👍👍👍👍👍🌷🌷🌷

[@pujithagaddale7171]

After watching this video I have subscribed lots of love to u 💖💖💐💐

[@ompatil2120]

one of the best explanation for naive bayes theorem...your channel is best for machine learning and data science....big data analytics....as well as to learn python,R,tableau,power BI etc....thanks a lot for such a nice initiative..

[@akshaysahastrabuddhe1558]

clear and crisp explanation of naive bayes classification algorithm on youtube.....concept is cleared....thank you

[@abhishekkale3589]

Very easy to understand... simple and clear.... thank you!!!

[@micahlingle1060]

this taught me more in 20 minutes than a top 15 american computer science school has in several months

[@radhaingle4664]

Excellent... Explanation...

[@gajanannaik184]

Good explanation and easily understandable 👍👍

[@karthikadevis9809]

Thanks a lot. You made it crystal clear. Grateful

[@rakhikhotre3224]

Excellent!!!!! Awesome👍👍👍

[@summ4628]

Best video on youtube for naive bayes

[@nileshmurumkar5067]

Really good you are doing bro...Thanks a lot for sharing with all...

[@pavanpatil5773]

You deserve more subscriber bro. Just nailed it hope you will get good price for your efforts.

[@sudhanshushakya4579]

Good explanation. Appreciate it!

[@tejaspatil3760]

Thank you for explanation with simple examples and it is also easy to understand...

[@respect9477]

nice one , understood clearly
edit : was able to solve any type of problem like this without an error

[@danishiqbal8970]

best video ,, sir kindly upload such kind of best videos for adaboost and all other algorithims of machine learning

[@sheemawilson5281]

Very good explaination..great

[@nithyabalu1371]

Really a very clear explanation.thank you so much

[@jayam26]

Hey Yogesh Very good explanation with very simple notations

[@ahmadalijamali3239]

Hey man your explanation was so amassing. Thanks man. Keep going on

[@mariasanchezz7786]

Your explanation is great!

[@mohammedbadusha5353]

Very good explanation sir ..

[@agnishabhattacharjee330]

I got P(Sunny|Yes)=2/9, and there are two places of Sunny written as yes as per your table, so can you please explain why did you write it as 1/9?

[@bhavikdudhrejiya2502]

Very nice way to explain...Good job

[@yogeshmurumkar]

Hi Bhavik, I am glad if it helped you.....

[@subinraj3730]

Great sir, Good Explanation

[@Penujuri_Sudharshan]

clear explanation, thanks for your effort :)

[@kabilesh3531]

Really awesome...rock the world

[@toole2845]

You are amazing 🎉

[@Man2Mr]

Good explanation..... Thank you for uploading....

[@Sripooja.Mahavadi]

Good explanation !! Thanks!

[@radhaingle4664]

Awesome explaination!!!!!

[@meeragopal5687]

Nice teaching sir..thank you sir

[@dannyleone2623]

Very Good Explanation ! Thank You !

[@KilluaZolydck10]

Well explained!
In Python we can do all of these calculations in just 4 lines of code :)

[@sukrutsigma]

Nicely explained!

[@haftukahsay3309]

thank you for your Best Lecture /Presentation

[@yogeshmurumkar]

Thank you...I am glad if it helped you.

[@srishtisrishti7088]

Nice explanation.

[@anonymous_team]

Thank you for making this video. This is the best video on RUclips. I have ever watched but sir I have found one little mistake in this (Sunny | Yes) = 2/9 & (Sunny | No) = 3/5

[@ammabadi747]

Very good. Thank you very much sir

[@trambakbhakre456]

very good explanation....very lucid and clear..thanks a lot for this...pl keep posting

[@rajyalaxmiarjun4059]

Sincere efforts bro

[@Not_kodi]

Very clear explanation thanks a lot 💙

[@qassisa1]

you made it so easy !! thank you

[@_LAKSHAWADHWANI]

Great explanation!

[@bhaargavvadranam5426]

Really a great explanation. Broo thank u Soo munch

[@yogeshmurumkar]

hi Bhaargav , i am glad if it helped you!!!

[@kannanv8831]

Thanks for your wonderful explanation.

[@omkartelgade9757]

Good Explanation Sir🙂

[@TanvirKhan-ne3cp]

Well explained!!

[@ramsp35]

Cool. Thanks dude...!!

[@yogeshmurumkar]

Ramanathan S.P. thanks...pl subscribe for furure notifications..

[@nastarankianersi104]

Thank you so much now I can head to my homework😀

[@sagarmarri213]

great explanation bro.even 6th standard student also can understand

[@samrat2606]

Superb !!!

[@meghanareddy6035]

Nice video

[@chetanreddy8310]

Clarity in concept explanation is soooo good

[@ashwiniyadav6713]

superb !!! please make more videos on ML topics thankyou

[@zealit5404]

Excellent man!!!!!!!! really good explanation... thank you

[@saimanasaduri5345]

Thank you sir.for such a nice video

[@rbyourfriend09]

great video

[@singhpankaj7330]

only heavy rain prevents cricket ...thanks for crisp explanation...

[@chintunannepaga579]

good explanation ..thank you

[@ashamaheshk7306]

excellent bro

[@vireshchapte6590]

Thank you Boss for nice explanation!!!!!!!!keep up dude!!!!!!!

[@Janeilliams]

can you show where you have done logistic regreesion algorithm , i couldnt find it in your playlist >>>>

[@delwarhossain43]

Good explanation..

[@AYANN1000]

Very nice thank you so much

[@varunrachakonda8268]

Great explanation..

[@patrickbateman7665]

Thanks for explaining in English .

[@valentineoragbakosi378]

Lovely explanation

[@mlds1530]

Excellent!!!!

[@ChannelXplorez]

excellent explanation..too good...just a small final calculation question, how did you get 0.0053. i calculated those values, got another figure.

[@karthikkombrenjekumaraswam907]

same here

[@subinraj3730]

Same here

[@rupalichavan3307]

thank you really very much..its really awesome

[@naavu_neevu]

Really good job.

[@thirue8237]

Excellent

[@neelimaeadha1660]

well explanation

[@jithinjnair6322]

great..thanks a lot...

[@donepudichalapathikalyan1953]

Sir Am sorry to say that the probabilities in the last part are wrong when I put in the calculator it is giving 0.00264 for yes and 0.0274 for no and the totally final answer is right. Anyways the explanation is pretty clear enough. Thanks a lot sir.

[@sriharshavardhan299]

really simple and tq soo much

[@krishnaiyer2556]

in the last step are you using bayes formula better check,where is the denominator of bayes??? pls confirm/

[@yogeshmurumkar]

Yes Bayes formula only and there is a denominator...at the end that equation is written accordingly to represent relationship

[@vireshchapte9594]

very good explanation...