Mam, I am extremely happy to have your video just before my exam. I completely understood and got the real concept behind the candidate elimination algorithm. Thank you so much.
This video helps me more i listen and i got it. my exam with in few hours easy and clear explaination and it helps to learn quickly thanks a lot mam thanks 🙏🙏🌹🌹
In case you have told that yes means positive and no means negative if outputs are other than yes no means what need to considered as positive and negative
Ma'am tomorrow there is ml exam for 3-2 cse students of jntuh r.18 regulation and may be that's the reason for sudden increase of your subscribers from 10.5k to 11k 😂😂
Hi mam I have understood the concept THANKS TO YOU. Mam can you please tell according to the JNTUH R18 Machine Learning Syllabus 3rd year 1st semister for AIML students. Our exam dates are at the end of the Feb.
@@TroubleFreevideos UNIT-I: Introduction to Pattern Recognition Importance of pattern recognition, Features, Feature Vectors and Classifiers, Supervised, Unsupervised and Semi Supervised Learning.Classifiers based on Baye's Decision Theory Baye's decision theory, Discriminant Functions and decision surfaces, Bayesian classification for Normal Distributions, Estimation of Unknown probability density functions, The Nearest Neighbor Rule. UNIT-II: Linear Classifiers Linear Discriminant functions and Decision Hyperplanes. The perceptron Algorithm, Least Squares Method- Mean Square Error Estimation, Stochastic Approximation and the LMS Algorithm, Sum of Error Squares Estimation Least Squares Method; Mean Square Estimation Revisited-Mean Square Error Regression: Support Vector Machine- Separable classes. Nonseparable classes UNIT-III: Non Linear Classifiers The XOR problem. The two layer perceptron. Three layer perceptrons, The Back propagation Algorithm. The cost function choice, choice of the network size, A simulation example, Networks with weight sharing, generalized linear classifiers, polynomial classifiers, Radial basis Function Networks. UNIT-IV: Feature Selection & Generation Feature Selection-Pre processing. The peaking phenomenon, Feature selection based on statistical hypothesis testing, ROC curve, class separability measures, feature subset selection; Feature Generation -Basis Vectors and Images, The KL Transform, The Singular Value Decomposition, Independent Component Analysis, Non negative Matrix Factorization, Regional features, Features for shape and size characterization. UNIT-V: Clustering Introduction, Types of Features, Definitions of Clustering. Proximity Measures- Proximity Measures between Two Points, Proximity Functions between a Point and a Set, Proximity Functions between Two Sets: Categories of Clustering Algorithms, Sequential Clustering Algorithms, A Modification of BSAS, A Two-Threshold Sequential Scheme, Refinement Stages Text Book: 1. Sergios Theodoridis, Konstantinos Koutroumbas, Pattern Recognition, Academic Press, Second Edition, 2009. Reference Books: 1. Richard Duda, Peter E Hart, David G Stork, Pattern Classification, John Wiley & Sons, Second Edition, 2001. 2. Christopher M.Bishop, Pattern Recognition and Machine Learning, Springer Publications, 2006.
U explained 100%better than my professors
Mam, I am extremely happy to have your video just before my exam. I completely understood and got the real concept behind the candidate elimination algorithm. Thank you so much.
😂😅
can one write how ma'am has written for VTU exams?
Its probably the best and easiest explaination i came across after browsing for like 2 -2.5 hrs. Thank you so much
I have a ml exam tomorrow this video is very useful for us thanks for videos😊
can one write how ma'am has written for VTU exams?
I have exams from tomorrow. Thanks for your vedios 🔥🔥
You’re welcome all the best for your exam
@@TroubleFreevideos Madam naku chepandi all the best
@@TroubleFreevideos tomorrow exam
@@youtubecomment1228 😆 🤣
I too have my internals senior😂
I have exams on 27th , your explanation makes me confident , keep up the good work.
finally someone who wants their subscibers the understand the concept
Thank u soo much mam I had got confused in this algorithm u have explained it soo well that I couldn't forget it at all❤❤❤❤❤❤
I watched a lots of video about this topic. But this is the best. Thanks a lot for the effort❤
This video helps me more i listen and i got it. my exam with in few hours easy and clear explaination and it helps to learn quickly thanks a lot mam thanks 🙏🙏🌹🌹
You’re welcome and all the best
Your channel deserves the name
Your are the best teacher with in one day i have covered my syllabus thank you very much
Thank u so much
It is really helping a lot
Lots of love from bengaluru
LITERALLY, THE BEST ONE!
you are the most efficient online teacher that i have thank you so much for akijng my life simple
love you 3000
Thank you mam very nice explanation. I was trying this concept in many channels but i am unable to understand. This video helped me a lot.
Thank you so much mam!
Your explanation is damn good ❤
Soo easy to understand😊
In case you have told that yes means positive and no means negative if outputs are other than yes no means what need to considered as positive and negative
Your videos of machine learning are excellent. I thoroughly understood concepts.
way of teaching is mind blowing seriously.....👌
generally i don't comment Ma'am, but couldn't stop myself 😊😊, u r very nice and sweet, liked your to the point videos... ty!
Thank you so much mam!
Your explanation is excellent good😌
It is easy to understand mee mam🤗
One of the finest video i never saw before❤
Mam...I loved your explanation.Helped me a lot.Stay blessed :)
i have a machine learing exam Tomorrow your video was excellent tq for the clear explanation mam
can one write how ma'am has written for VTU exams?
Mazak aa gaya😃 great explanation
Like whyyyy didn't my lecturer see this video before teaching us🤦🏻♀️
I regret not watching ur videos from the beginning of sem.
Your voice is soo sweet 😊😊
Thanks a lot 😊
Very nice explanation ma'am. Understood the concepts well. Please try to make videos on all concepts of AIML (18CS71).
Just superb explanation 💥💥
yes mam your videos helps me lot during my exam preparation .I request you do videos on FIOT and STM as well .
Hi i have exam tomorrow machine learning i.e is 12 August i just got to check the channel right now
Best explaination ever ❤❤❤❤❤❤❤❤❤❤❤❤❤❤❤❤🙌🙌🙌🙌🙌🙌👌👌👌👌👍👍👍👍👍👍
exam: tomorrow , college: Manipal
Great explanation... Nd ur fluency of speaking english next level ✨
Thanks for the video, it really helped to understand this algorithm better.
Every thing is perfect , But love your handwriting :)
No mam, its wrong
If the instance is positive we should move from general to specific and if it is negative we should move from specific to general
Very Clear Explanation Mam,
Thank You.
Great video.....Life changing !!!!!
Great Thankyou Very Much 😊 ! It was best 🌟❕
Amma advanced congratulations for achieving 100 k sub
Thank you so much for your clear explanation 😊🥰
All vtu engineering students is having ml exam on Thursday including me.. Plz make a content of expected questions for the exam
Nice explanation.
Thanks a lot for your videos ..maam
Nd we have our xam on 24th of this month
My ML exam is on March 6th... please do more videos madam which will more helpful to me madam
Syallabus:- jntuh r18(3-1)
thank you mam without you iam nothing😘
Ma'am tomorrow there is ml exam for 3-2 cse students of jntuh r.18 regulation and may be that's the reason for sudden increase of your subscribers from 10.5k to 11k 😂😂
😂😂
Mam make more videos on candidate elimination algorithm for better understanding..
Excellent mam teaching ML for us thats a great opportunity for us
Very nice explanation ma'am. understood all the concepts well. please try to make videos on computer networks.
very useful video , thank you very much
Good explanation video mam♥️
very good to expalin madam it is easy going to understood
Thank you madam
waao.. good teaching
Excellent explanation ✨✨✨
Tq so much! I could easily understand it through ur video
You’re welcome
What if the first hypothesis is a negative one? I have nothing to compare to previously for changing generic to specific
Tomorrow is my exam , Please Make the Video for that. 😢😢😢😢
Graphic Era
Thank you so much ma'am ...this helped a lot ❤️
Thank you ma'am for such a nice video
wow...excellent explanation
Thank you
Mam please try to upload our syllabus mam (ml)
Please make a video about confusion Matrix in machine learning..
I'm trouble free now, okay done👍
Nice explanation ma’am thank you
Mam do a video on difference between finds and candidate elimination
Superb explanation
Hi mam I have understood the concept THANKS TO YOU. Mam can you please tell according to the JNTUH R18 Machine Learning Syllabus 3rd year 1st semister for AIML students. Our exam dates are at the end of the Feb.
Thank you so much ma'am
helped alot
My pleasure 😊
Mam can I get the notes which u used to explain, it's very neat and clear mam, thank you
Thank you mam.. ❤❤❤
man this was so helpful
At last when u compare s3 and S4, there is a change in sky "rainy" and "sunny" but u didn't change it in S4 mam. Can u explain it clearly mam
Thank you mam..24th i have exam
Sister please do software testing methodology I have semester next week
Tqsm mam good understanding mam
Mam can you make a video for the example 'Japanese car economy' for candidate elimination
you have compared half of G (for -ve example) with S3 and half from the original table, which is correct?
Your right brother
Ma'am for negative classification it has to be opposite values... it's slightly wrong, please check
Madam please do videos on subject pattern recognition
Can you share the syllabus. My contact details are in description
@@TroubleFreevideos
UNIT-I: Introduction to Pattern Recognition
Importance of pattern recognition, Features, Feature Vectors and Classifiers, Supervised, Unsupervised and Semi Supervised Learning.Classifiers based on Baye's Decision Theory Baye's decision theory, Discriminant Functions and decision surfaces, Bayesian classification for Normal Distributions, Estimation of Unknown probability density functions, The Nearest Neighbor Rule.
UNIT-II: Linear Classifiers
Linear Discriminant functions and Decision Hyperplanes. The perceptron Algorithm, Least Squares Method- Mean Square Error Estimation, Stochastic Approximation and the LMS Algorithm, Sum of Error Squares Estimation Least Squares Method; Mean Square Estimation Revisited-Mean Square Error Regression: Support Vector Machine- Separable classes. Nonseparable classes
UNIT-III: Non Linear Classifiers
The XOR problem. The two layer perceptron. Three layer perceptrons, The Back propagation Algorithm. The cost function choice, choice of the network size, A simulation example, Networks with weight sharing, generalized linear classifiers, polynomial classifiers, Radial basis Function Networks.
UNIT-IV: Feature Selection & Generation
Feature Selection-Pre processing. The peaking phenomenon, Feature selection based on statistical hypothesis testing, ROC curve, class separability measures, feature subset selection; Feature Generation -Basis Vectors and Images, The KL Transform, The Singular Value Decomposition, Independent Component Analysis, Non negative Matrix Factorization, Regional features, Features for shape and size characterization.
UNIT-V: Clustering
Introduction, Types of Features, Definitions of Clustering. Proximity Measures- Proximity Measures between Two Points, Proximity Functions between a Point and a Set, Proximity Functions between Two Sets: Categories of Clustering Algorithms, Sequential Clustering Algorithms, A Modification of BSAS, A Two-Threshold Sequential Scheme, Refinement Stages
Text Book:
1. Sergios Theodoridis, Konstantinos Koutroumbas, Pattern Recognition, Academic
Press, Second Edition, 2009.
Reference Books:
1. Richard Duda, Peter E Hart, David G Stork, Pattern Classification, John Wiley & Sons, Second Edition, 2001.
2. Christopher M.Bishop, Pattern Recognition and Machine Learning, Springer Publications, 2006.
Can you post videos for data science using python....
Thank you so much❤ for helping us🥰
We are having semister from July 10th , please help us with all topics mam.....
i appreciate ur help so much☺
Thank u so much...I have exam now in 1 hr I'll luckily pass now😂
Very nice explanation
YOUR explanation was supeb and where can i get notes
Nice mam........thank You So Much mam
Mam we exam on Wednesday please make vedios of important questions please mam
Madam can you Suggest important topics unit wise of ML.. Though I have my exam day after tomorrow 😔....plzz don't ignore madm....😢😢😢❤❤❤❤❤❤😊😊😊😊😊😊😊
I am from kit-coimbatore college. Tomorrow I have exams. Please make a video to how easily pass the machine learning paper.
kal mera exam hai,, kal chiye mere ko video simulated annealing,,,
ro ra hu mai sun rhi ho na
😂😆😆
But where is the version space
Super explanation mam
Mam can please make a last moment preparation video for design and analysis of algorithm(VTU University) my exam is near by
mam plz provide the notes l so that it I'll be easy for us to read through the notes that you are teaching....😊😊
vvvvvvvvvvvvv goooooooood
Thank you..Well explained..
You’re welcome