Error at 15:32 Multiple linear regression: Y = B0 + B1*x1 + B2*x2 + ... + BP*xp + E Polynomial linear regression: Y = B0 + B1*x^1 + B2*x^2 + ... + BP*x^p + E
@kasyapvelampalli2811
Жыл бұрын
Right.. I was confused here too! Because linear regression must always have deg=1 as opposed to what has been taught in the lect, eq. cannot have a polynomial degree of 'p'
@ram-pc4wk
Жыл бұрын
no , linearity is based on coeffiecents x terms in this case, not directly the x terms
@narmadaa2106
8 ай бұрын
Yes u r right It's polynomial regression
@narmadaa2106
8 ай бұрын
If the degree of x is more than1 it represents non linearity
@mayanksj
6 жыл бұрын
Machine Learning by Prof. Sudeshna Sarkar Basics 1. Foundations of Machine Learning (kzitem.info/news/bejne/o4iDuWeKamN0l6w) 2. Different Types of Learning (kzitem.info/news/bejne/po2jqZ-Mn4KffW0) 3. Hypothesis Space and Inductive Bias (kzitem.info/news/bejne/xY-DqauuoJ5kqaA) 4. Evaluation and Cross-Validation (kzitem.info/news/bejne/z495p3xum2dyhGU) 5. Linear Regression (kzitem.info/news/bejne/mYaAmGiJq4OirG0) 6. Introduction to Decision Trees (kzitem.info/news/bejne/p6uAvICpk4ucqHo) 7. Learning Decision Trees (kzitem.info/news/bejne/mImJp3VnfHdpgZw) 8. Overfitting (kzitem.info/news/bejne/2myJ1nVokKeqp20) 9. Python Exercise on Decision Tree and Linear Regression (kzitem.info/news/bejne/zX94tn2ee2Jjkm0) Recommendations and Similarity 10. k-Nearest Neighbours (kzitem.info/news/bejne/sYSd0qmdqKF7iIY) 11. Feature Selection (kzitem.info/news/bejne/rIqwvoqki56fqmk ) 12. Feature Extraction (kzitem.info/news/bejne/p62YvnyPcX10iKw) 13. Collaborative Filtering (kzitem.info/news/bejne/s4yAvGyMgJNijY4) 14. Python Exercise on kNN and PCA (kzitem.info/news/bejne/lWZ4nnhviImGmWU) Bayes 16. Baiyesian Learning (kzitem.info/news/bejne/pmmimGqYjZalq34) 17. Naive Bayes (kzitem.info/news/bejne/lo150aZ6gmmHdqg) 18. Bayesian Network (kzitem.info/news/bejne/lW5mx5Noo4SVfmU) 19. Python Exercise on Naive Bayes (kzitem.info/news/bejne/uaGLlm2sfmdnhpw) Logistics Regession and SVM 20. Logistics Regression (kzitem.info/news/bejne/pHtmmXluaamThXo) 21. Introduction to Support Vector Machine (kzitem.info/news/bejne/yJ-asJaBapmJoHY) 22. The Dual Formation (kzitem.info/news/bejne/uoWp2I2ianyDpZg) 23. SVM Maximum Margin with Noise (kzitem.info/news/bejne/uIKe3J6mqHWBnI4) 24. Nonlinear SVM and Kernel Function (kzitem.info/news/bejne/qJl5rWSGiYhnlpw) 25. SVM Solution to the Dual Problem (kzitem.info/news/bejne/u2Z52o14iYRmpnY) 26. Python Exercise on SVM (kzitem.info/news/bejne/2G1ul4xqcHeknW0) Neural Networks 27. Introduction to Neural Networks (kzitem.info/news/bejne/232H0JyVg4OLanY) 28. Multilayer Neural Network (kzitem.info/news/bejne/ya6mra53m1-hrJg) 29. Neural Network and Backpropagation Algorithm (kzitem.info/news/bejne/tWyNsn2YiKCcqYY) 30. Deep Neural Network (kzitem.info/news/bejne/0YKG2Gikg5OVZ3Y) 31. Python Exercise on Neural Networks (kzitem.info/news/bejne/zIqYv2ZmsZ6jlXY) Computational Learning Theory 32. Introduction to Computational Learning Theory (kzitem.info/news/bejne/mZ6An4pvZphjfW0) 33. Sample Complexity: Finite Hypothesis Space (kzitem.info/news/bejne/z6Nqyo2PiV-Efag) 34. VC Dimension (kzitem.info/news/bejne/sYyezoCBqJaCapg) 35. Introduction to Ensembles (kzitem.info/news/bejne/z5uisGepr6xhkqQ) 36. Bagging and Boosting (kzitem.info/news/bejne/roh6nGuNoImgoXY) Clustering 37. Introduction to Clustering (kzitem.info/news/bejne/pK2gsoGMbmSlrX4) 38. Kmeans Clustering (kzitem.info/news/bejne/0p2Vs2dtkHl8em0) 39. Agglomerative Clustering (kzitem.info/news/bejne/r3mproaDpHaDeGk) 40. Python Exercise on means Clustering (kzitem.info/news/bejne/0qlt3HmJbWiDpG0) Tutorial I (kzitem.info/news/bejne/1nyvynpjoF9yfag) Tutorial II (kzitem.info/news/bejne/rmx-yn-Irmh-pZg ) Tutorial III (kzitem.info/news/bejne/tp9pzmuuqHdedIY) Tutorial IV (kzitem.info/news/bejne/lKNtu36BsYdeh20) Tutorial VI (kzitem.info/news/bejne/w2mM02iwqXmUfWk) Solution to Assignment 1 (kzitem.info/news/bejne/0qeip5mfpmKjfo4)
@RohitKumar-jh1km
7 жыл бұрын
You people explain those things in details which doesn't require explanation. And those things which do require explanation you skip them as if they even doesn't need expatiation.
@dipanjanbiswas4924
6 жыл бұрын
they copied from andrew NG's lectures
@akhandbha
6 жыл бұрын
How do you know ?
@shivaniamehta9851
4 жыл бұрын
You can see this for clarification. medium.com/@nicolabernini_63880/ml-what-is-the-difference-between-gradient-descent-and-stochastic-gradient-descent-be79ab450ef0
@subashchandrapakhrin3537
4 жыл бұрын
@@dipanjanbiswas4924 Is the Anderw NG father of ML ??? or the people who write the papers
@dipanjanbiswas4924
4 жыл бұрын
@@subashchandrapakhrin3537 you can say that
@siddharthGupta632
6 жыл бұрын
Why you have written polynomial regression equation in place of multiple linear regression. This seems a bad lecture. Not expected from IIT
@sunny10528
4 жыл бұрын
Yes I too got stuck at this point in the lecture and started doubting my own knowledge
@Creative_arts_center
2 жыл бұрын
The best professor in machine learning.i like her teaching.i followed her from 2010 onwards.i had collected her lectures through CDs from 2010.i like her very much
@roseb2105
5 жыл бұрын
maybe i am missing somthing here but are these lessons meant to be a review or just an overview of what will be taught? beacuse its hard to understand this if learning this the first time without much examples?
@avinashdwivedi2015
2 жыл бұрын
i was good at Linear regression and after watching this lecture.. i forgot everything about Regression. lol ironic
@ebenezerr9152
2 жыл бұрын
wtf lol
@sunderrajan6172
7 жыл бұрын
Kind of confusing lecture - switching from single variable regression example to multi-variable. All explanation is in a rush. I was hoping that the examples are well explained. Having 1/2 in the equation, is it for half theta? I heard this is not important. When you compare Stanford or MIT Online lectures, lots of improvements needed.
@shirshak6738
5 жыл бұрын
for examples see tutorials
@wolfisraging
6 жыл бұрын
Worst explanation of gradient descent in the world
@sauravprasad1996
7 жыл бұрын
directly skipped to LMS algo without explaining "how to learn the parameters " clearly ..! poor explanations !
@ritikraushan7392
2 жыл бұрын
Kuch samajh me nahi aaya
@varadavinay719
2 жыл бұрын
lol same with me
@shudeshna66
7 жыл бұрын
Having 1/2 as a multiplicative factor does not change the solution as what minimizes z also minimizes 1/2 z. 1/2 is usually added so that the derivative formula has a constant coefficient of 1.
@saurabhchoudhary4572
6 жыл бұрын
Ma'am please review your lectures before publishing, poor explanation and incorrect equation for multi linear regression.
@vaibhavagrawal1856
5 жыл бұрын
You are getting confused between Multivariate Linear Regression and Polynomial linear regression. The equation given here is not wrong. It is just a special case for multivariate, where every feature is a function of the 1st parameter. This is called as polynomial linear regression.
@itsdurgeshray
16 күн бұрын
CORRECTION at 16:24 equation shall not have exponential degrees in power as it is for Polynomial Regression.
@roseb2105
5 жыл бұрын
im very confused. and lost with these lectures
@HA-bj5ck
Жыл бұрын
Very well explained...........This is GOLD ❤
@jivanmainali1742
3 жыл бұрын
Why objective function is 1/2 of sum square error. If we have n data set it should be average so I guess it is 1/n of sum of square error
@AkashCherukuri
3 жыл бұрын
It's for making the mathematics easier since you would have to differentiate the function later. (1/2 gets canceled with the 2 which you get from differentiation, making equations and stuff a lot cleaner.)
@getfitwithakhil
6 жыл бұрын
Mam, you rushed towards the end of the lecture. The theory is more important as we have computers to do most of the calculations.
@anushamathur2019
3 жыл бұрын
polynomial regression is a form of regression analysis in which the relationship between the independent variable x and the dependent variable y is modelled as an nth degree polynomial in x. y = b0+b1x+b2x^2+... and you are calling that as multiple independent variables which relates to multiple linear regression not polynomial
@ashwinprasad5180
3 жыл бұрын
This is indeed a mistake , I presume. it should be y = b0 + b1x1 + b2x2 + ... + bpxp instead of rising to the power.
@shreyxsh5054
3 жыл бұрын
@@ashwinprasad5180 yes! i took me 1 day. i rhought IIK KGP teachers must be right .... then i found MIT Andre ng one and now all is sorted.. thanks
@viral_baba
6 жыл бұрын
Hello Prof the equations written on the blackboard are of polynomial regression but the slides contain equations of multivariate regression is it a mistake if it is please mention it in the annotation. if anyone knows the answer to my query respond to me freely. Thanks
@shivangidubey7062
3 жыл бұрын
It was......she wrote polynomial equation.
@rishabhpansari9963
5 жыл бұрын
I think LMS is least mean square
@mrm371
2 ай бұрын
Scope
@rajasekharareddy6246
6 жыл бұрын
To understand this video , I think people must know the linear algebra . Then only they can understand this concept.
@AkashCherukuri
3 жыл бұрын
The name is *Linear* Regression my man
@koppuprasanthkumar9211
4 жыл бұрын
is this called an NPTL ....worst of time whatever the concepts need extra time you just skip them like anything.......i wont watch NPTL from now onwards ........why u r doing these NPTL certifications i don't know........and overall the title is not at all justified......we don't know how to learn the straight line using linear regression ...........don't watch and waste your time........find anyother resources.......
@onataghoghoatikpe5989
4 жыл бұрын
I am enjoying your courses. thanks
@JMD_coding
3 жыл бұрын
Sir/mam after completion of this course can i get any certificate Please Reply me
@lampfall7915
2 жыл бұрын
She is wonderful teacher, respect to you
@debarpitosinha1162
Жыл бұрын
Error in multiple linear regression formula formula should be x=b1+b2x2+b3x3+...............+bpxp
@chandureddim4327
Ай бұрын
Anybody can help me why we need to assume that errors are independent to each other or mean as zero & has some standard deviation ? and as normally distributed ? please
@SwaroopSinghDeval
7 жыл бұрын
Equation of multi-variable liner regression is wrong.
@solarstryker
7 жыл бұрын
Swaroop Singh Deval yeah I think she misinterpreted the sub's as the powers
@premshankar5967
6 жыл бұрын
yes exactly
@vaibhavagrawal1856
5 жыл бұрын
You are getting confused between Multivariate Linear Regression and Polynomial linear regression. The equation given here is not wrong. It is just a special case for multivariate, where every feature is a function of the 1st parameter. This is called as polynomial linear regression.
@saptarshisanyal4869
3 жыл бұрын
Sorry to say this maam , but this is wrong explanation of gradient descent algorithm and cost function. This tutorial was good till 25 minutes and after that it was total confusion.
@Tchknow879
Жыл бұрын
mam you teaches awesome but one thing that i suggest you can you improve your black board ,improve your camera so we can see clearly
@TheUnblameable22
4 жыл бұрын
Surprised to see.. writing from the chit still making the basic equation itself wrong. The multiple linear regression is wrongly written. Assumptions are just copied and not explained.
@rohitranjan5218
3 жыл бұрын
How is she explaining the non linear equation as linear one, the equation should be linear and she has end up with non linear one. the numbers suffix notation has been written as power. 16- 19th minutes of the video.
@tolifeandlearning3919
2 жыл бұрын
Great lecture
@jamesmathew8291
Жыл бұрын
Excellently covered the topic. Which textbook reference ma'am
@wreckedinsect5069
3 жыл бұрын
my professor lectured fucking 3h and i understood nothing but linear is straight.. here in half and hour i am really ready for the exam, thanks
@Man0fSteell
7 жыл бұрын
There are KZitem channels that provide better lectures or explanations in a simplified form than these IIT professors . Too bad our Indian quality of education/teaching (or whatever you wanna call) needs to improve a lot!! :(
@sujitfulse8846
7 жыл бұрын
please suggest me some good youtube channels .
@priyanshsharma3362
5 жыл бұрын
@@sujitfulse8846 t series
@saptarshisengupta5073
4 жыл бұрын
@@sujitfulse8846 @brazzers
@JMD_coding
3 жыл бұрын
Shall we get any certificate after completion all video's
@shashu1999
6 жыл бұрын
Copied J(theta) formula from Andrew Ng's module and didnt update the variables
@harisankar6104
4 жыл бұрын
andrew ng from coursera?
@myfuzzyrugs7585
3 жыл бұрын
@@harisankar6104 yes
@navedahmad5851
7 жыл бұрын
proper explanation should be provided, the teacher is just rushing without explaining the concepts, this is not good.
@sachinsd4663
6 жыл бұрын
28:12 wtf was that?It sounded alien like and hilarious 😂
@mitrabb4812
6 жыл бұрын
bro i was searching for this comment lmao !!!!
@sachinsd4663
6 жыл бұрын
@@mitrabb4812dude I am glad someone noticed that shit.It is insane.
@mitrabb4812
6 жыл бұрын
yea man big LOL
@santoshkumargoutam4791
3 жыл бұрын
Mam :- Excellence concept clarification
@SubhamCreative.613kviews
6 жыл бұрын
nptel teach us very badly.........
@ishankulkarni3542
5 жыл бұрын
Nahi samaj mei aa Raha hai.....jo PPT mei hai use hi phir se explain kar Rahi hai madam
@sandeepkushwaha9790
6 жыл бұрын
Now more confuse explanation is not good can any one share Good videos for linear regression with gradient descent
@a.yashwanth
6 жыл бұрын
coursera's machine learning by stanford is good.
@JMD_coding
3 жыл бұрын
Mam can i get certificate
@mahipalmahato7648
Жыл бұрын
7:25
@sujitfulse8846
7 жыл бұрын
please explain the concept completely do not leave them in-between.
@SHIVAMGUPTA-wb5mw
4 жыл бұрын
We started with question to find the parameter but never discussed on that....
@ashwinprasad5180
3 жыл бұрын
That is what the algorithm called gradient descent does, which she wrote at the end. It finds the parameters such that it reduces the loss function
@hiraksenroy691
6 жыл бұрын
Easy to interpret for statistics background..
@tararawat2955
7 жыл бұрын
Things are not being clearly explained. Its really unclear or confusing...atleast that example must be taken completely to understand the concept
@manyamittal6767
6 жыл бұрын
Maybe split this lecture into two. It got really rushed at the end.
@ankursaxena4942
4 жыл бұрын
Nice Video How to use #Linear_Regression in #Machine_Learning
@harisankar6104
4 жыл бұрын
bro please help me, at 16:09 of this video she tells the equation of Y with square terms for p independent variable like in a polynomial regression but at the last section she tells for multi variables the equation is a linear function just like one for a multi variable regression, is this p independent variables are not multi variables?
@abhyunnati8589
Жыл бұрын
Superb
@harshitsingh480
5 жыл бұрын
lms stands for least m,ean square not least minimum slope
@harshitsingh480
5 жыл бұрын
Sorry for comma in between its mean not m,ean
@SandeepSharmaRhythmNGroove
6 жыл бұрын
not good explanation at all.
@s_sasmal
6 жыл бұрын
Can't imagine that Kids are preparing from there 8th standard to get into the IIT and after getting into the IIT they will get this kind of lecture.
@suddhasheel
7 жыл бұрын
Sorry to say this! But poor explanations by IIT standards. LMS, Batch gradient descent, and Stochastic descent would require more explanation.
@madsudan9227
6 жыл бұрын
gives a brief overview ,Thanks for your efforts
@harisankar6104
4 жыл бұрын
please help me, at 16:09 of this video she tells the equation of Y with square terms for p independent variable like in a polynomial regression but at the last section she tells for multi variables the equation is a linear function just like one for a multi variable regression, is this p independent variables are not multi variables?
@pankajkumarbarman765
Жыл бұрын
great lecture ma'am . Thank you so much and happy teacher day, Pronam niben.
@theperson66
7 ай бұрын
The best professor!! I love your classes, thank you for your hard work.
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