Naive Bayes Classifier ll Data Mining And Warehousing Explained with Solved Example in Hindi

Data Science Noob to Pro Max Batch 3 & Data Analytics Noob to Pro Max Batch 1
👉 5minutesengine...
Myself Shridhar Mankar an Engineer l TH-camr l Educational Blogger l Educator l Podcaster.
My Aim- To Make Engineering Students Life EASY.
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[@devarpanatribedi1281]

I couldn't understand the topic despite watching many videos on TH-cam. However, after watching your video, all my doubts were cleared.I hope you can understand how amazing teacher you are!

[@sushantkulkarni97]

I hope you get a lot more views on this channel man, great explanation. You'll make a great professor.

[@5MinutesEngineering]

Thanks a lot for your valuable response.

[@sj1997]

@@5MinutesEngineering 84k subscribers all over the globe and still counting

[@yogitajain8003]

@@jamirraja1397 i think he needs to divide by p(orange) and p(banana) and p(others) and multiply p(fruits ) in all but only p(fruits) will be same in all case so it can be neglected

[@sherleyrn]

You are going to / already are a great teacher. Aapka teaching style is just fantastic. Watching your video once is just enough to grasp the concept. You have a long long way to go! please post many many videos and keep up the good job! (Y)

[@souravsaha7206]

Whenever i went for any cs related course in TH-cam I found many problems regarding understanding the topic..But when i started following you,its going to be change day by day

[@piyusharya2741]

Ary bhai kya karke maanoge, Itna to college me 4 saal me ni aaya samj jitna aap 10 min me bata dete ho🔥 Big fan of your way of teaching❤ Precise, to the point, and Easiest on YT❤

[@harchitgulati3065]

bhai tu kya karwaake manwana chahta batade

[@vipnirala]

I was hopeless...but now I can see a ray of hope😇

[@manikuits]

I always thinking Machine learning is very hard, but when i saw this video then machine learning is very easy.
Thanks Brother

[@saiprem5380]

bro it is hard

[@akashsingh967]

bro you made it easy because you learn it . if you have time build your algoritm
then you will know its essence

[@sierra_victor1]

Nice Explanation. One correction, I think: in calculating the final probabilities, it should be P(Orange/Fruit) because we are calculating the probability of the item being orange given it has the characteristics of the mentioned fruit.

[@sierra_victor1]

and also multiply by P(Orange)

[@ibtehazahmad]

Thank you so much, i have my semester final exam tomorrow on this. Great help indeed, you make it look so easy.

[@TECHNODER]

I don't need decode or easy solutions... 5 minutes engineering is enough for BE exams! ❤️

[@tanmaypatil9806]

Did you cleared your be

[@bhalsodnirva104]

👏👏👏u deserve thousands of views..best explanation skills..

[@zakariahussain2393]

superb methodology to convey the knowledge .. I saw DR.VIVAK BINDRA in your expressions.

[@akshayjadhav216]

Sir plz Data analytics ke remaining videos dalao naa sir...plz....C4.5, CART, evaluating decision tree, Smoothing, Diagnostics, classification of diagnostic, sir plz hosake to last 3 unit mein ke syllabus topic explain kro naa sir bahoot hard language hai sir plz

[@mithunchandrasaha403]

Very Nice Explanation,Sir. Needs More This Type Of Tutorial From You.

[@tanmayzala877]

Sir thank you so much for all kind of help you did this long. I would say I've passed Engineering just on your lectures. the spirit you show in your lectures motivates us a lot.

[@yuvrajseth8604]

Sir ....aap nahi hote toh data mining to kabhi pass nahi hota...thank u for the wonderful videos...

[@madhukrishnanipankar563]

That was clear and crisp 🔥

[@kajalzite4219]

plz... make a video on ID3 algo,smoothing,Diagnostic...

[@pranayrewane3794]

or we can use P(A/B) =P(A intersection B) / P(B) works in every case bayes theorom is derived from this property

[@anuraggorkar5595]

IMP 🛑But if we have Fruit = { Yellow, Sweet, Sweet, Sweet, Sweet, Long} then by conventional logic seeing Sweet so many times and having sweet as a higher probability for orange we might conclude that it is orange, but the single term Long will make it 0, as compared to other fruits. To counter this we add a small number say 1 to all the zeros in the dataset so that we do not get incorrect output because of zero being present.

[@Xxter2]

i think sir you should calculate P(banana/fruit) instead of P(fruit/banana) and same for oranges and others because calculating P(banana/fruit) provides the probability of the fruit being a banana given the observed features, which is the desired outcome in classification tasks, whereas calculating P(fruit/banana) gives the probability of observing the features given that the fruit is a banana but does not directly inform us about the likelihood of the fruit being a banana.

[@krishnaas5927]

exactly

[@bisheshtalukdar2163]

just love u sir....i was zero in datamining before i found this channel

[@Agrajsingh1]

great exactly, 1 hour left for my exam lol. good.

[@gargnakshatra]

Thanks for an amazing explanation.
Can't hope of getting a better explanation than this.
Thanks a lot!

[@sapnaarora9546]

Very nice video. Also, please explain the case of zero probability in Naive Bayes and respective laplace smoothing technique.

[@lifeachieve1386]

My college professor used your examples to explain.

[@mudasar8629]

The best channel to get the concepts cleared :)

[@mohammedsufiyankhan306]

Thank You So Much Sir For Simplest Explanation and Excellent ways of Explanation. I Wrote DAA & CD after Seeing and Learning from You lecture videos. ❤❤❤😮😮😮😮#Thanks5MinEngg

[@AK_sheikh933]

Sir You are ray of hopes...God Bless you...Thanks so much..

[@officialvenu_]

Your way of expressing the content is too👍 good sir.....every point gets clear....

[@ruchisoni5060]

Great sir... This video is very helpful and in simple language rather than other videos..

[@PAVANKUMAR-fu7pb]

Jabardasth explanation bhaiya..very helpful

[@mandymadman]

Better than any college professor.

[@tanjidhossain4348]

You are a saver brother..
thanks a lot for your all videos.
Really well explained.

[@aadilkhan9725]

What a great course sir, very easy explanation. U r awesome sir. 👍👍

[@rockyetchison3380]

Thanks 🙏 so much Sir!
Just saw this before exam & my doubts are somewhat cleared. 😌

[@nomankhawaja3864]

Sir u style of explanation is awesome.. however do u have any videos regarding putting these algorithms in coding .. pls do guide me it will be highly appreciated. Great respect from Karachi Pakistan

[@ABHISHEKTIWARI-zq2jj]

I have a doubt if for example the probability of P(Fruit/Banana) and P(Fruit/Others ) are equal then which probability we will consider

[@veerabehal151]

sir sari examples classification ki ...nd its tooo gud..bt can u gve examples of same classifier based on continous data as label..or regression based predictive modelling

[@harshrajput9982]

You're Doing great job . Do you think row totals in the table are incorrect

[@aakash8010]

Thanks sir, secured 15% with ur help😋

[@himalpant862]

I really love the way you explained sir ... god bless you (From Nepal)

[@abhi13091985]

Will you not apply laplace soothing when frequency of P(L|O) is missing.

[@deepikasen256]

Thankyou Sir♥

[@nks3209]

very nice and simple and to the point Example

[@shikhargupta7552]

Exactly what you need a night before the exam.
Good work.

[@anishjain8096]

Bhai yeh kis machine learning kis course me ha me to cs me hoo wha to sirf programming he c cpp or java me bhai yeh subject kha he

[@tusharsrivastava370]

@@anishjain8096 bhai konse year me h... Mostly CS k 3rd year m hota ML... Mere college me bhi 3rd yr m h... Aur elective hota subject... Hum choice mili thi data mining aur ml me se ek m

[@DHANANJAYGROVER]

in this probability of having particular type of fruit is missing is guess like for orange it will be multiplied by 650/1200 for others it would be 150/1200 like this .....itsnt it

[@geetanjalibhapkar1888]

Sir please upload videos on ...
An analytics project - communicating, operationalizing, creating final deliverables.

[@himanshuraj5249]

Great explanation.Thank you soooooooooo Much महोदय 🙏😊

[@yunus9850390381]

Just for a correction, the total in the column for all fruit needs to be corrected. As per the numbers in the table, we should have 800 Oranges.

[@shashwatsrivastava7380]

Nope, there are also some oranges that are sweet as well as yellow, its like an intersection if you draw a Venn diagram, and hence the total may not exactly be the sum of sweet oranges and yellow oranges.

[@yunus9850390381]

@@shashwatsrivastava7380 agree, then the column name should not be "total".

[@shashankdixit92]

@@yunus9850390381 the column name should be "total" only...
Because it shows total number of that fruit...it's just a simple concept if you see this problem from the set theory concept

[@INNoMATHsforyou]

You are right because the basic assumptions of chaines of conditional probability that is being used here prevents the intersection to happen. Hence the features should be disjoint and the sum should be corrected here.

[@skilz9525]

soja bhai tera paper hogya na

[@amargupta7123]

very nicely explained please keep posting such videos. it helps a lot.

[@sushantkulkarni97]

Do you make videos on Advanced Computer Architecture?

[@ankitjha7132]

sir i think you should have also multiplied p(banana) in p(fruit/banana) . similarly for p(orange) in p(fruit/orange)

[@apoorvbedmutha457]

I think you are supposed to calculate P(Orange/Fruit) instead of P(Fruit/Orange)

[@palak0408]

are Hi SCOE k senior 😂

[@apoorvbedmutha457]

@@palak0408 😂lol hi, never knew juniors knew me

[@palak0408]

@@apoorvbedmutha457 😂

[@kunalh910]

Nah....its P(Fruit/Orange)...cuz its given that Its orange....but here fruit means the fruit which we need to find ...which is yello,long and sweet....

[@mahrukhbaloch7644]

Stay blessed sir bhot zbrdst explanation thi thank you so much

[@vidhinara6403]

Dear Sir,
can you please explain how the row totals 650, 400 & 150 are arrived at.

[@saurabhchaudhary4207]

Just an assumption because although total of yellow orange and sweet orange is greater than 650 ( The point is there can be some yellow orange which can be sweet as well)🙏

[@sufyandahar3139]

who need worry about machine learning algorithm, when you have sir

[@yashmishra12]

Is the total column correct? total number of oranges should be 350+450=800, right?

[@ayeshanisar7345]

Very good explanation,
Thank you sir

[@kannanv8831]

Thank you very much. The explanation simple and elegant.

[@eishasingh-z8p]

sir mere calculation se p(orange|yellow).p(yellow)divided by p(orange)=0.4 aarha hai and agr simply 350*80/120=233.33 aarha hai inka outcome

[@JohnAbraham-zt8hz]

Nice video ❤

[@engrbilalrafiq]

outstanding explanation of every thing

[@itskaaryan7]

)How No. of oranges is 650 , it should be 350(yellow) +450 (sweet)+0(long) = 800

[@aatishy]

bhai very nice and to the point like maths. If you have some video of joint probability & conditional Probability
then plz share the link

[@crazyguy7665]

Concise and up to the point....great job

[@gurmeharsingh2333]

mango bhi ho sakta hai na XD.
thanks for explaining it so easily tho!!

[@shaikfaisalfasi7704]

Your explanation is good

[@Mani2011ist]

My search often goes like, "[Topic Name] 5 Minutes Engineering".

[@shreyadutta1416]

Sir, thank you for giving such easy explanation but please upload the videos on Id3 algorithms, c4.5 algorithm ,cart algorithm , smoothing and diagnostic.please sir kal exam hai.

[@venkateshnambi1576]

Superb bro great way of explaining....

[@AnkitChaurasiyaVlog]

love from lucknow

[@ssr6948]

great

[@ayushgoyal4382]

Sir how we got total of orange, banana and others as 650, 400 and 150. I was calculating manually in every row and the result turns out to be different. I might be wrong please explain.

[@Genz111-o4r]

I'm confused about same

[@QueenCoder]

This was confusing... but u explain good

[@samiranroyy1700]

Thank you so much sir... really very helpful❤❤❤❤

[@amnaamin6578]

Nice and easy explanation

[@karimbaidar2861]

Can you please upload Computational Learning Theory (COLT), PAC and VC Dimensions in Machine Learning.

[@hsd287]

Thanks sir bohot sahi 👍🏻❤

[@pratiksingh9480]

P(Fruit|Orange) , I see that this as well as other probabilities can be directly computed from the table , without using Bayes Theorem. Can you please elaborate the reason.

[@jayantmishra2266]

Yes I want to know about it too

[@youpremium175]

Please make more videos on sem 7 and 8 B.E IT topics- Mumbai university fan💯💯

[@mahaveerprajapat7094]

I did not understand why Bay's theorem is used here to find P(Yellow/Orange). as we find p(Orange/Yellow) = 350/800 like that we can also get p(Yellow/orange) = 350/650.

[@snehalatakaranje8736]

What is difference bet Bayes classifier and Naive Bayes classifier?

[@swapnilraut6046]

Please explained us about c4.5 algorithm also with simple example

[@sahejseth5097]

Great video sir!!

[@muhammadisrar164]

great sir thank u for your video

[@souravsaha7206]

Lots of respect to you sir

[@rishabhmatharoo1216]

great sir u r a great teacher 👍

[@shahabrao8995]

great explaination sir
love from pakistan

[@sunildogra3975]

Thank you for the explaination.

[@abdulnayeem4196]

Aap toh chagaye sirji

[@ayeshanisar7345]

Best teacher

[@i_akam]

Thanks from Kerala 😀😀

[@sneh9817]

KTU Data mining alle? 😂

[@i_akam]

@@sneh9817 haha yaa

[@devaaravind1604]

thank you soo much sir,1m thanks to you sir!!!

[@datascienceworld7041]

Very nice explanation bro 👌

[@desichannel7401]

Awesome explanation 👌👌👌👌🙏

[@wick_bourne]

Great video and thanks sir!!!

[@tahmidkhan4778]

Wonderful tutorial

[@IEyshaFatima]

The guy invests his whole meals' energy into his videos

[@sumithzoting9016]

ek number sie great