Thank you so much , I have completed my 4-1 Machine learning exam today, I followed only your videos for the exam, your content is amazing mam. Helped me 100%. Thank you once again🥺🥶
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.
Mam i request you to explain one question that is, Discuss how multilayer network learns using a gradient descent algorithm, what is solution for this question is gradient descent using delta rule
at 5:12 wont it be h(di) rather than h(xi) we havent defined xi, when we change di in place of x, so should μ be changed to h(di). correct me if wrong!....
There is some misinformation in the video Maximum likelihood estimation = argmax(D/h) and not h/d . good playlist but please include real life examples while solving
Mam can we know which text book are you following, because there is only one textbook mentioned in our jntuh syallbus and content is little bit different from yours
subject is introduction to data science my college is jntuh sem exams starts on march 4th pls make a playlist on introduction to data science r18 regulation jntuh syllabus
Best teaching skills ever , specially for all categories students,🎊
Thank you so much , I have completed my 4-1 Machine learning exam today, I followed only your videos for the exam, your content is amazing mam. Helped me 100%. Thank you once again🥺🥶
Wonder ful explanation really worth watching ur video till end... without skipping..am in final year!!
u are explaining ML very effectively i always try to understand i am not data minning side so feeling difficult
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.
we appreciate your efforts ma'am
all i can say is thanks a lot for saving my sem exam mam !! thank you sooo muchhhh
Excellent explanation mam...can't thankyou enough
Thank you mam.. For Helping a poor students❤
Thank you for making our life simple❤
Mam please complete video series soon and also post important questions of ml mam please
Very good teaching my dear sister.All the best for your bright future 😊
Thank you very much for your Content !
Very clearly explained, thank you so much, u have helped me a lot😊
YOU ARE GOD❤
appreciate your efforts mam thank you so much for nice explanation and presentation
Really great job 👍
TQ u so muchuuu❤
Understood perfectly
thank you ma'am . love your teaching 😍
Thankyou so much mam really your teaching is extraordinary and easily understood
You defined p(d/h) is p(di/h)
But when we look into hmap it is p(h/d) right
Very helpful thanks a lot❤️
Very informative
Please make a video on Maximum likelihood hypothesis for predicting probabilities. Exams are approaching. Please make it fast.
Mam
Software testing methodologies exam on aug 26 pls make videos on stm subject ......
Ur explanation is super mam🤗
please make videos before 26 aug.
Thank you so much..it was very helpful
Mam i request you to explain one question that is, Discuss how multilayer network learns using a gradient descent algorithm, what is solution for this question is gradient descent using delta rule
Mam give the explanation for this particular question
@@shafanashahreen7296 then how, what should do now
Thank you very much mam❤
It will be helpful to your and channel and our students also
Thank you much akka it's useful for me
Ciet may
Hello could you please
Explain the maximum likelihood hypothesis for predicting the probabilities in machine learning
Yes please
at 5:12 wont it be h(di) rather than h(xi) we havent defined xi, when we change di in place of x, so should μ be changed to h(di). correct me if wrong!....
Ma'am please make videos for Predictive Analysis according to JNTUH syllabus.
Please make a video on the MAP Hypotheses and Consistent Learners
Useful
Please make a video on cryptography
There is some misinformation in the video Maximum likelihood estimation = argmax(D/h) and not h/d . good playlist but please include real life examples while solving
Text book name please
Hlo mam, please make a video on maximum likelihood hypothesis for predicting probabilities..
Reply me as soon as possible..
Deep learning subject
very nice
Mam can u explain ids jntuh my exams at March 4th plz help
Maam u have written the hmax formula wrong it's p(D/h) not p(h/D)
Nice mam good explaination
Mam can we know which text book are you following, because there is only one textbook mentioned in our jntuh syallbus and content is little bit different from yours
Mam please answer me back.
subject is introduction to data science my college is jntuh sem exams starts on march 4th pls make a playlist on introduction to data science r18 regulation jntuh syllabus
deep learnning
Can u explain maximum likelihood hypothesis for predicting probabilities we have 3-2 exams on July 10 th onwords
Marri Laxman reddy government collage Dundigal
after this topic there is another topic MAXIMUM LIKELIHOOD HYPOTHESES FOR PREDICTING
PROBABILITIES but the video is not there
Can u explain Naive bayes algorithm mam we have exam on Monday
thanks akka
Good morning mam.Maximum likelihood hypothesis for predicting probabilities topic video not available mam.if posiible explain that topic
Is this answer for relationship of least square method and mimum likelihood
mam can you make videos on software testing methodologies, syllabus as par JNTUH, exam on 26 august prepare videos before 26 plese.
@1:48 hMAP is same as hML but it is different ...I would like to know how these two are same.
maximum likelyhood and maximum posterior are not same!!!, why bother assigning different names if they are same?
Mam please do DAA Videos
How I am asking how how did you saying so fastly I am just wondering and also I am so jealous about your speed😢
mam explain computaional learning theory concepts mam.
Good akka
Maam here your Hmap = argmax(p(D/h) but you are writing wrong value i think
Mam do we have problems in Maximum likelihood
July 26
Sri indu college
Sphoorthy Engineering College need all machine learning syallabus of jntuh
Mam compiler design
Jntuchceh
Exam on 6th of September
Mam compiler design videos
Jntuhceh r18 regulation
Exam date 6th September
Explain the knn algorithm mam pls
I think this is not the correct explanation of least squared error method in last minute of the video.
Software testing teach us
Can u upload the videos as fast as possible as there is less time for our exams
Yes ma'am
hMAP is different from hML.
Compiler design
Mam ...Maximum likelihood hypotheses for predicting probabilities
Please explain this topic...
do network programming of jntuh
ML University of Hyderabad
iam starting at 6.56am and my exam is at 9.45am--(13-01-2025)
We nees videos of STM
Mam pls upload the next videos
mam where i get this notes
Mam we need cse r18 btech 3rd year network programming subject it's very urgent mam please can you make this
information retrieval system
Aaayennnnn???
Mothaniki exam Ipoindi
august 16th 2024
hech
🤣🤣🤣
Please pronounce "H" correctly... its irritating to hear wrong pronunciation of "H" all the time...!