These ML vdos uploaded 3 yr ago. Still students, lectures and the people who are going give interviews they are also watching till now. Best explanation i have ever seen. No any promotion n not taking too much time to explain. Best content. Please don't stop ma'am we need u n even next generation too need u. So please never stop teaching. I have exam in November n I'll definitely get good marks in my exam its my belief becoz I'm watching your teaching videos. ❤❤❤
You are young teacher but you are teaching better than old teachers everything relevant or in short with details and after seeing your content we are able to solve questions so it is enough but keep it up
Ma'am ur are doing great job !!. Ur explaination complicated questions into simplified way. By ur explaination we are getting overall picture about the topic.
Very well explained again....Thanks for dealing the topics in most appropriate manner !!!! Good luck... Do we will have AI related topics also in near future ?
Thankyou so much mam you are a JEM and backbone for all graduators here.... thankyou so much take care mam you have a beautiful voice which makes us attract to listen.And i want you to explain a topic called " Networking Basics and Product Knowledge " can you mam?
I’m having exams on 18 March 2024. Graphic Era Hill University , Dehradun India. The subject is Artificial Intelligence (on topic of Introduction to Machine Learning Approaches- Decision Tree Learning)
Thank you so much mam , mam please continue with JNTUH syllabus series mam, very useful, we might have exams from 16th mam . very clear and understandable... waiting for next videos ..
Well jntuh exams are on 24 .... So it's ok to have videos made by 23 rd night mam... So don't stress mam... Thank you for your time in explaining us....
Module I: Python programming Machine Learning (ML) [10 Periods] Introduction to Python: Python, expression, variables, assignment statements, functions, built in function, strings, modules, lists, making choice( Boolean. if, storing conditional statements), repetition(loops, while, counted loops, user input loops, control loops, style notes). File processing one record per line, records with multiple fields, positional data, multiline records, looking ahead, writing files), sets and dictionaries(sets, dictionaries, inverting a dictionary), Algorithms with suitable example. Construction of functions, methods. Graphical user interfaces, databases and applications. Introduction - Well-posed learning problems, designing a learning system, Perspectives and issues in MI. Concept Learning - Introduction, Concept Learning task, Concept learning as search, Find- S: Finding a maximally specific hypothesis, Version spaces and candidate elimination algorithm, Remarks on version spaces and Candidate elimination, Inductive bias. Module II: Decision Tree Learning and ANN [09 Periods] Decision Tree learning - Introduction, Decision Tree representation, Appropriate Problems, Decision Tree learning algorithm, Hypothesis Space Search, Inductive bias, Issues. Artificial Neural Networks Introduction, Neural network representation, Problems for Neural Network Learning, Perceptions, Multilayer networks and Back Propagation algorithm. Remarks on back propagation algorithm, Evaluation Hypotheses, Motivation, Estimation hypothesis accuracy. Sampling theory, General approach for deriving confidence intervals, Difference in error of two hypotheses, Module III: Bayesian learning and Instance based Learning [10 Periods] A: Bayesian learning Introduction and concept learning, Maximum Likelihood and Least Squared Error Hypotheses, Maximum likelihood hypotheses for predicting probabilities Minimum description length principle. B: Instance-based Learning-K-Nearest Neighbor Learning, Locally Weighted Regression, Radial Basis Functions, Case-Based Reasoning, Lazy and Eager Learning, Genetic Algorithm: Motivation, Hypothesis Space Search, Genetic Programming. Models of Evolution and Learning, Parallelizing Genetic Algorithms Module IV: Rules and Analytical Learning [09 Periods] Learning Sets of Rules - Introduction, Sequential Covering Algorithms, Learning Rule Sets Learning First Order Rules, Learning Sets of First Order Rules: FOIL, Induction as Inverted Deduction, Inverting Resolution. Analytical Learning - Introduction, Learning with Perfect Domain Theories: Prolog-EBG Remarks on Explanation-Based Learning, Explanation-Based Learning of Search Control Knowledge Module V: Learning Techniques [10 Periods] Combining Inductive and Analytical Learning - Motivation. Inductive-Analytical Approaches to Learning, Using Prior Knowledge to initialize Hypothesis, Using PriorKnowledge to alter Search Objective, Using Prior Knowledge to Augment Search Operators. Reinforcement Learning Introduction, Leaming Task. Q Learning. Non-Deterministic Rewards and Actions. Temporal Difference Learning, Generalizing from Examples, Relationship to Dynamic Programming Mam this is ML syllabus please prepare some videos on missing topics mam please my end exams are from 12-12-2023
Your videos are very good and more understandable. But your explanation is so fast . Explain little bit slowly. Your videos are more helpfull to engeneering students . Thank You so much for doing this videos. Good luck for Your sucess .
Very well explained mam. Please complete the chapters asap we have the semister exams on 3rd August and 21st of July we are going with our mid term exams. So kindly upload the videos soon
Can you please make videos of Machine Learning and Natural language processing syllabus for M.tech (Osmania University Hyderabad, Course: Artificial Intelligence and Machine Learning)
Very good lectures mam Thank you Mam kindly upload the lectures as soon as possible so we can prepare of semester exm. And also firstly upload the topics which are more important in external semester exam .
yes even I think the same. IG=1-Entropy. That's what I have read everywhere. And also the formula that ma'am has told for IG is actually the formula for Entropy. I am really confused.
mam, i have mid- exam on 14th sep on machine learning, iam from vardhaman collge of engineering,Hyderabad.could u pls give me any tips for exam how to write
such a patience you have in doing this type of videos for students..definitely this will lead you to a great sucess.
These ML vdos uploaded 3 yr ago. Still students, lectures and the people who are going give interviews they are also watching till now. Best explanation i have ever seen. No any promotion n not taking too much time to explain. Best content. Please don't stop ma'am we need u n even next generation too need u. So please never stop teaching. I have exam in November n I'll definitely get good marks in my exam its my belief becoz I'm watching your teaching videos. ❤❤❤
You are young teacher but you are teaching better than old teachers everything relevant or in short with details and after seeing your content we are able to solve questions so it is enough but keep it up
i cannot thankyou enough..you justdeal every single topic with appropriate manner hoping to see more and more videos from you ma'am
I have my Knowledge Discovery in Database course exam next Monday at FAU Erlangen Germany. Your videos are just lifesavers.
2:16 vdo starts
akka nuvvu chaala baga explain chestunnav thanks 👌👌
A clg bro
Tomorrow is my ML exam
Pdf unte send chey bro tomorrow my exam ml with python
Day after tomorrow @@Hbdhdhej
Ma'am ur are doing great job !!.
Ur explaination complicated questions into simplified way.
By ur explaination we are getting overall picture about the topic.
mashaallah excellent mam
nice explaination.....
thnk u so much for this video our course instructor just included this topic in the final exam without even teaching us
you are the only teacher who make me pass
Excellent command over the topic and the way of explanation is really there is no words to say madam.
Mam my exam are in Feb-6 -2023
It have only 8 days for learning
I am studying MCA my 1st exm is
Machine learning with python
you are the best teacher ❤
Watching this video 1hr before my exam…😀
Thanks for teaching us I am a failure.!
Thank you so much mam your explanation is easy to understand keep doing more videos
Very well explained again....Thanks for dealing the topics in most appropriate manner !!!! Good luck... Do we will have AI related topics also in near future ?
Your helped me a lot. I can't explain how much helped in my exam
I understand your previous videos very well but this video I can't understand better
mam really great explanation im very clear about the topic now, I'm from Karimnagar,Telangana
Good & clear explanation
Thank you maam
Contents were reaching us but we are not supposed to write in exam as per your explanation adding more info
Thankyou so much mam you are a JEM and backbone for all graduators here.... thankyou so much take care mam you have a beautiful voice which makes us attract to listen.And i want you to explain a topic called " Networking Basics and Product Knowledge
" can you mam?
Thank you so much mam your giving valuable source preparation for examination thank you mam
You're a life saver
Nice lecture and make more videos on machine learning subject Deeply...
tq so much man nice explanation ABOUT ML .
please continue with JNTUH syllabus
I’m having exams on 18 March 2024. Graphic Era Hill University , Dehradun India. The subject is Artificial Intelligence (on topic of Introduction to Machine Learning Approaches- Decision Tree Learning)
Good video mam keep continuing 🙂
Thank you
CART (Classification and regression tree ) vedio pls mam
Thank you madam❤❤
PB Sidhardha College...my exam data is 27 th March tq so much mam
Thank you so much mam , mam please continue with JNTUH syllabus series mam, very useful, we might have exams from 16th mam . very clear and understandable... waiting for next videos ..
Yes sure I’ll do by then
thank you could you please do videos on back-end development like django and also do videos on oops concept in python
Well jntuh exams are on 24 .... So it's ok to have videos made by 23 rd night mam... So don't stress mam... Thank you for your time in explaining us....
In 15 min exam is going to start...🥲
Watching it a night before my exam, thanks!
Module I: Python programming Machine Learning (ML)
[10 Periods]
Introduction to Python:
Python, expression, variables, assignment statements, functions, built in
function, strings, modules, lists, making choice( Boolean. if, storing conditional statements), repetition(loops, while, counted loops, user input loops, control loops, style notes). File processing one record per line, records with multiple fields, positional data, multiline records, looking ahead, writing files), sets and dictionaries(sets, dictionaries, inverting a dictionary), Algorithms with suitable example. Construction of functions, methods.
Graphical user interfaces, databases and applications.
Introduction - Well-posed learning problems, designing a learning system, Perspectives and issues in MI.
Concept Learning - Introduction, Concept Learning task, Concept learning as search, Find- S: Finding a maximally specific
hypothesis, Version spaces and candidate elimination algorithm, Remarks on version spaces and Candidate elimination, Inductive
bias.
Module II: Decision Tree Learning and ANN
[09 Periods]
Decision Tree learning - Introduction, Decision Tree representation, Appropriate Problems, Decision Tree learning algorithm,
Hypothesis Space Search, Inductive bias, Issues.
Artificial Neural Networks Introduction, Neural network representation, Problems for Neural Network Learning, Perceptions, Multilayer networks and Back Propagation algorithm. Remarks on back propagation algorithm, Evaluation Hypotheses,
Motivation, Estimation hypothesis accuracy. Sampling theory, General approach for deriving confidence intervals, Difference in error of two hypotheses,
Module III: Bayesian learning and Instance based Learning
[10 Periods]
A: Bayesian learning Introduction and concept learning, Maximum Likelihood and Least Squared Error Hypotheses, Maximum
likelihood hypotheses for predicting probabilities Minimum description length principle. B: Instance-based Learning-K-Nearest Neighbor Learning, Locally Weighted Regression, Radial Basis Functions, Case-Based
Reasoning, Lazy and Eager Learning, Genetic Algorithm: Motivation, Hypothesis Space Search, Genetic Programming. Models of
Evolution and Learning, Parallelizing Genetic Algorithms
Module IV: Rules and Analytical Learning
[09 Periods]
Learning Sets of Rules - Introduction, Sequential Covering Algorithms, Learning Rule Sets Learning First Order Rules, Learning
Sets of First Order Rules: FOIL, Induction as Inverted Deduction, Inverting Resolution. Analytical Learning - Introduction, Learning with Perfect Domain Theories: Prolog-EBG Remarks on Explanation-Based
Learning, Explanation-Based Learning of Search Control Knowledge
Module V: Learning Techniques
[10 Periods]
Combining Inductive and Analytical Learning - Motivation. Inductive-Analytical Approaches to Learning, Using Prior
Knowledge to initialize Hypothesis, Using PriorKnowledge to alter Search Objective, Using Prior Knowledge to Augment Search
Operators. Reinforcement Learning Introduction, Leaming Task. Q Learning. Non-Deterministic Rewards and Actions. Temporal
Difference Learning, Generalizing from Examples, Relationship to Dynamic Programming
Mam this is ML syllabus please prepare some videos on missing topics mam please my end exams are from 12-12-2023
Madam please
Explain tommorow machine learning exam
How does rules extract from Decision tree
Good Explanation 👍👍
Your videos are very good and more understandable. But your explanation is so fast . Explain little bit slowly. Your videos are more helpfull to engeneering students . Thank You so much for doing this videos. Good luck for Your sucess .
Very well explained mam. Please complete the chapters asap we have the semister exams on 3rd August and 21st of July we are going with our mid term exams. So kindly upload the videos soon
can you explain about list then eliminate algorithm
We have exams from August 16th mam so, plz post videos as soon as possible mam... Wating for next videos...
Yes I’ll do by then
Forecasting numerical values with regression question video please sister
I understood very clearly
Your videos are great
Helpful video ❤
mam please provide important questions with answers for 4-1 subjects of cse please mam or you can keep the doc link in comment section mam
You are an excellent teacher
Superb mam
Clear explanation
Supb teaching
awesome
i was able to understand ....... thanks you so much..............
Your voice super mam😊
mam is decision tree topic is same in data mining and machine learning
Awsome explanation..
Can you please explain the same way in python program for this concept
can you please explain how to choose best target atribute?
please reply ... I have my exam very soon
Informative😇, exam on 24th - JNTUH
your class is very good i can understand so tell me
Mam 4-1 r19 nosql lectures upload cheyyandi viliatey
22/10/2022 time:11:30 am ,college: Graphic Era Hill University, Haldwani
mam this topic is same in both date warehousing and machine learning
Ma'am everything is fantastic.... but please reduce the intro sound little bit, its so harsh 🤕
Im yash btech 4th year mvgr college tmrw ML sem exam now I'm here to listen class 😅
Can you please upload videos on concurrent and parallel programming topics
Annamacharya institute of technology and sciences, rajempeta, good
Thank you mam🙌
U did a great job thank u sooooo muchhh ✨
jaldi jaldi banao yaar maza aata hai......
Teek hai yaar 😊
Brilliant explanation
what is mean by algorithm(ID3)
Gud
🥳
Explain LPVLSI Subject
Hello mam I have an mid exam at 06 nov 2003 can u please say hw to understand easily ml in this short tyme
Thank you so much mam ur explanation so good mam
Thank you so much
Can you please make videos of Machine Learning and Natural language processing syllabus for M.tech (Osmania University Hyderabad, Course: Artificial Intelligence and Machine Learning)
Very good lectures mam
Thank you
Mam kindly upload the lectures as soon as possible so we can prepare of semester exm.
And also firstly upload the topics which are more important in external semester
exam .
Sure I’ll upload
Mam tomorrow we have sem exam ML in 24 th soo please tell our syllabus
Supr mam
Mam i wanted one class one conditional independence
Nicely explained helped me a lot ☺️
Thanks and all the best
super
I guess you've switched between entropy and information gain.
yes even I think the same. IG=1-Entropy. That's what I have read everywhere. And also the formula that ma'am has told for IG is actually the formula for Entropy. I am really confused.
Ma'am, please share the link to your notes.
thanks mam
Good going mam...
Content is nice but in book information have total paper but not understandble u r video information is good can u expand more
I did not get you
Can u increse importantion more
mam can you tell me 20th regulation syllabus please ,i had a exam on 27 so please ,this subject has no faculty in our collage so please tell me mam
upload clustering concept videos mam college:rvr&jc college of engineering
caluculate
mam, i have mid- exam on 14th sep on machine learning, iam from vardhaman collge of engineering,Hyderabad.could u pls give me any tips for exam how to write
U need tips even for the mid exams😑😑
@@saisharath1007 ante appudu class lu vinale so
@@amaansmd5611 classes kuda vintara😑
@@saisharath1007 🤣🤣🤣
Lolz bro tomorrow I have sem 🤝✌
Machine Learning on 27-05-2022 Exam Anurag Group of Institute at Ghatkeshar Hyderabad
jntuk r19 exams from nov 14 mam . we want ml notes cse