00:02 Naive Bayes Classifier is a classification technique 01:07 Understanding the definition of the Naive Bayes classifier 02:22 Naive Bayes Classifier is used for classification in machine learning. 03:29 Understanding the calculation process in Naive Bayes classifier 04:22 Using multiplication to calculate the probability of events 05:04 Naive Bayes classifier uses probability to make predictions. 06:47 Naive Bayes Classifier with Example 07:29 Naive Bayes Classifier is used for investment classification.
Mam we have exam on 24th JNTUH....please try to cover topics from other units also...atleast complete 3rd unit....we'll have chances to pass atleast then....thank you so much for covering all the topics from last two units☺
Thank you ma'am it was a very well explanation of naive Bayes... could you please share the lecture on brute force Bayesian algorithm and good links regarding these lectures
computational learning theory--introduction,probably learning an approximately correct hypothesis......sample complexicity for finite and infinite hypothesis space......the mistake bound modei of learning instance based learning--------introduction,k-nearest neighbour algorithm....locally weighted regression....radical basic fn,cases based reasoning,remarks on lazy and eager learning plz make videos on this topics...plz.
Mam In exam we can write any example or they will give table? because in spectrum and most of cases example depends on enjoy sports but ,You explained fruit example and some youtubers explained other examples please answer my question
In 6:00 you’ve made a mistake. The probability of fruit being orange is not 0. However, if you said the probability of orange being sweet AND yellow AND long, then the answer is 0.
UNIT - III Bayesian learning - Introduction, Bayes theorem, Bayes theorem and concept learning, Maximum Likelihood and least squared error hypotheses, maximum likelihood hypotheses for predicting probabilities, minimum description length principle, Bayes optimal classifier, Gibs algorithm, Naïve Bayes classifier, an example: learning to classify text, Bayesian belief networks, the EM algorithm. Computational learning theory - Introduction, probably learning an approximately correct hypothesis, sample complexity for finite hypothesis space, sample complexity for infinite hypothesis spaces, the mistake bound model of learning. Instance-Based Learning- Introduction, k-nearest neighbour algorithm, locally weighted regression, radial basis functions, case-based reasoning, remarks on lazy and eager learning.
thankyu soo much for your notes.....well explained...and the rest of the topics left in this unit make videos on-- bayesian belief networks,the EM algorithm.
This is a best presentation but i face one difficulty that is madam speaking speed is fast. and i am face difficulty to understand a complete sentense. may be my processor is old one and madam processor is very fast as well as modern
This is because there can be oranges which can be both yellow & sweet...i.e. each fruit can have multiple features (yellow, sweet & long)associated with it.
didi i cleared accenture cognitive test and also my both the codes were executed successfully on 16 august when should i expect communication assessment and interview ?? plz help 🙏🙏🙏😕
No bro , it may be intersection orange can be both sweat and yellow , so due to that , total manipulate, so that they told total no of oranges (fruits) at each end column
00:02 Naive Bayes Classifier is a classification technique
01:07 Understanding the definition of the Naive Bayes classifier
02:22 Naive Bayes Classifier is used for classification in machine learning.
03:29 Understanding the calculation process in Naive Bayes classifier
04:22 Using multiplication to calculate the probability of events
05:04 Naive Bayes classifier uses probability to make predictions.
06:47 Naive Bayes Classifier with Example
07:29 Naive Bayes Classifier is used for investment classification.
Thanks mam...for absolutely explain....i got so deeply 🙏💕
Thank you very much mam
Easy to understand your teaching 😊👍
A😅😅
I think I'm watching first youtuber videos where I don't need to speed up the video, it's naturally too fast & quite interesting. :)
Mam we have exam on 24th JNTUH....please try to cover topics from other units also...atleast complete 3rd unit....we'll have chances to pass atleast then....thank you so much for covering all the topics from last two units☺
haa s
Oh oh divya 😄😄
@@maheshadhimulam3074
Mahesh anna topper
did you pass the exam or do you still dance?
Mee class lo teachers chappara papa
You are Genius! Love from Ireland. I am doing MSC in AI here and your tutorials help me get clear concepts.
Thanks alot ma'am ....May god bless you 🌹❤️
Thank you ma'am it was a very well explanation of naive Bayes... could you please share the lecture on brute force Bayesian algorithm and good links regarding these lectures
computational learning theory--introduction,probably learning an approximately correct hypothesis......sample complexicity for finite and infinite hypothesis space......the mistake bound modei of learning
instance based learning--------introduction,k-nearest neighbour algorithm....locally weighted regression....radical basic fn,cases based reasoning,remarks on lazy and eager learning
plz make videos on this topics...plz.
Excuse me Ma'm U deserve more views and likes according to your quality of content.
I wish that 2024 will bring more views 😊😊😊😊😊😊😊😊
Thank you so much mam its very helpful and clear explanation. Please cover the other topics in 4,5 units also madam
Mam I have ml sem exam on 24th of this month ...I request to make info covering atleast 3 chapter .. thankyou ❤️🙏
This concept is of data mining and warehouse ❤
I don't know who are you but thanks a lot you saved my 5marks
Mam, then what is the difference between bayes optimal classifier and naive bayes classifier?
Thank you for your efforts
You have such a beautiful voice. ❤️
You saved my day!!
Mam In exam we can write any example or they will give table? because in spectrum and most of cases example depends on enjoy sports but ,You explained fruit example and some youtubers explained other examples please answer my question
In 6:00 you’ve made a mistake. The probability of fruit being orange is not 0. However, if you said the probability of orange being sweet AND yellow AND long, then the answer is 0.
Well explained thank you mam
all glories to u saved ma lyf
mam in exam we have to calculate for yellow/banana
What is the difference between naive Bayes and Bayes optimal and Bayes theoremmm......all are looking same please post crct videos ...
Mam I have exam on 3rd sep...so please do more vedios as soon as possible 😌
UNIT - III
Bayesian learning - Introduction, Bayes theorem, Bayes theorem and concept learning, Maximum
Likelihood and least squared error hypotheses, maximum likelihood hypotheses for predicting
probabilities, minimum description length principle, Bayes optimal classifier, Gibs algorithm, Naïve
Bayes classifier, an example: learning to classify text, Bayesian belief networks, the EM algorithm.
Computational learning theory - Introduction, probably learning an approximately correct hypothesis,
sample complexity for finite hypothesis space, sample complexity for infinite hypothesis spaces, the
mistake bound model of learning.
Instance-Based Learning- Introduction, k-nearest neighbour algorithm, locally weighted regression,
radial basis functions, case-based reasoning, remarks on lazy and eager learning.
thankyu soo much for your notes.....well explained...and the rest of the topics left in this unit make videos on-- bayesian belief networks,the EM algorithm.
great!
Mam plz upload more all topic videos atleast upto 4th unit I have exam on agus 24th jntuh
This is a best presentation but i face one difficulty that is madam speaking speed is fast. and i am face difficulty to understand a complete sentense. may be my processor is old one and madam processor is very fast as well as modern
what resources do you use for learning??
Mam can you please explain about generalised linear models
Mam in this example why you are considering denominator
Nice 👍
its completely understood mam ... i have small doubt y are the total oranges are 650 i did not understand that
This is because there can be oranges which can be both yellow & sweet...i.e. each fruit can have multiple features (yellow, sweet & long)associated with it.
Good afternoon madam
I have exams
Jntuh
Pls post all topices about the
Machine learning
Thanks mam for the video
Mam but in data set we have more no of data items,here u just taken 3-4 data items.For a data set how should we do that 👉👈
Shouldn't we multiply by P(Orange) while calculating P(Fruit = Orange)? and divide by the product of prob of Yellow, Sweet and Long?
I am not understand how the total value is 650 but what you are told is totally confused
Because in the classes yellow, sweet, long they are repeated
So for total we should take a random whole number????
Mam can u explain SVM IN MACHINE LEARNING
Is naive base classifier and naive base models are same..shall i write in exam
Future mam
Why the total is not same for rows and columns mam
Mam make playlist on embedded Systems mam
In the fruit example the first row is total 800 na
Can u pls provide the implementation part...i mean how to implement it in code
And mam please share your notes also
They are very clear and easy to understand
Tq mam
Plz do some more vedios of continuation topics of ML
ty & ily
Hii mam we have different topics in second module in 6th sem can u teach us
Total of long is wrong it is 85
Mam please cover upto unit 5
On 24 th we have ML exam
JNTUH
thank you so much
Thanj you mam
Please teach again with another example
Mam I can't understand how you calculated total number of fruits
Please respond
What about examples that is Bayesian belief network
Thanks
Thank you
You're welcome
Mam, the total orange fruits is 800,but you wrote 650,how?
Noo total is 650 only she explained it in the video in the beginning.
Tqs madam
your voice iz choooooooo chweeeeeeeet uWu😍😍😍😍
Thanks madam
How can total no. Of oranges are less than no.of yellow oranges+no.if sweet oranges??
Because an orange which is yellow can be sweet as well. That's the property of independence of the attributes
Analytical learning plz
Our ML exam is on 22nd ... We are JNTUH students Make all the units as fast as possible... Please help us
On 24th august *
why dont u complete the full problem
Machine learning exam on 24th Aug
Jntuh
didi i cleared accenture cognitive test and also my both the codes were executed successfully on 16 august when should i expect communication assessment and interview ?? plz help 🙏🙏🙏😕
Please give me update about final remainder accenture mail
Mam exam on 24 so please complete ml video series atleast on/before 24 aug mam
I’ll try
350+450=800 how it's come 650 meam please
Tell me that logic
Mam we have exam on jan 2022
Mam this is unfair you put the thumbnail of your image but in video , no facevideo of yours 🙁
You are here for the face ?😂
@@shaikaftabahmed9666 maybe😅
@@shaikaftabahmed9666bro got click baited😂
Pass tho karna bhaiii
Kyu nahi ho rhi pdai
i was a great explanation but the total of above example is wrong , the total fruits will be 2050
No bro you calculate coloum wise
Done
Mera kal exam hai 7 jan 2023 ko
can you please look after your voice its bit too low while playing the video
We want notes sister can u provide plz😢
Hole total ia also wrong
No bro , it may be intersection orange can be both sweat and yellow , so due to that , total manipulate, so that they told total no of oranges (fruits) at each end column
Long one
Oka mukka ardam kalyy asalu laste 3 line topic 😢
do videos on compiler design ......
Go and listen akka education 4u😂😂😂😂
why u always take the same example of the 5-min engineering videos?
i am getting irritated from this subject
akka assal hyderabad lo akkaduntav akka vachi prase chestha
Create your own content instead of copying from others
you have taken the example in wrong way mam and you took the total also wrong you didnt even explain y should take that values plz explain it clearly
Y r u in 2x
5 min engineering ke video ka copy kiya hai
maam it is absolutely unnecessary to do all this formulation
MADAM TOTAL GALAT AARA HAI,ITNE CHAANTE MAARUNGA ITNE CHAANTE MAARUNGA
ichipadesavv!!!!!!!!!!
ganda intro, please change
if you are not comfortable in english then tere is no shame in explaining in hindi in my opinion
I guess she doesn't know Hindi,her mother tongue is Telugu...
Video ki starting m ye cartoon vala music kyu lga rkha... Aadha mood to isko sunker hi khraab hojata
copied from 5 minute engineering🤣🤣