i really can't describe how many platforms i went through like statquest, datacamp, geeks for geeks, stanford, udemy ,towards data science and much more but never saw such explaination . this is the first time when i am commenting on a youtube video .your work made me do so .you are seriously a great teacher krish . thanku for sharing your knowledge in such a clearer way.
your channel has brought a huge transition in our lives , especially people who are looking for a career transition. Thank you and your channel for sharing your knowledge and making it easy for us .
I must say Sir you are genius. What an explanation of logistic regression. It's awesome and so simple that anybody can understand. If you watch this video twice you will never face any problem in future to explain logistic regression.
Your explanation is beyond excellence... After a lots of traveling finally I find a outstanding platform to learn data science.. I am pursuing Ph.D in machine learning.. And this tutorials make my PhD interesting...thanks a lot...
Superb explanation on why we should go for Logistic Regression. I don't think we can find this kind of clear explanantion in most of the channels. Thank you for presenting us the concept in easy and beatiful manner.
Superb explanation.Earlier i was always confused about logistic regression and linear regression as I was unable to understand the mathematical intuition behind it.This is mainly asked as interview questions in product based companies I guess. Thanks Krish.
this is much easier and simpler to understand than what was taught in paid course ..thankyou so very much .You made life simpler for non ML background guys like me .
Currently pursuing my MS in Data Science and Business analytics, this is the most simple and clear explanation on Logistic regression!! Good work Krish:)
This is what happens when you really want to teach and its not all about the money. Krish took the time not just to teach the concept but also connect it to reality. Good stuff boss. Hope the quality of your work only increases.
bro this comment has just came straight from my heart even the andrew ng have not explained with that much clarity even though i know you somewhere learn but your explaination is quite well as i have done ML from 2,3 online courses but i will check whole your playlist again only for the deep understanding of math behind it
andrew explained it really well , but he uses more complex language which is harder for us to relate to. I understood andrew really well too after watching his logistic regression videos twice each.
tutorial 34 was clarified my doubts, I am waiting for part 2 & 3 of performance metrics for Classification problem in ML. Please upload the part 2 & 3 of tutorial 34 sir.
Can anyone tell where he stays I have to touch his feet for this incredible talent of teaching maths 🙏🙏🙏🙏 and students who are from primary schools ,don't watch this video and dislike that just bcoz you didn't understand
Hi Krish. Thank you very much for your videos. I just have questions regarding the reasons you mentioned why we cannot use linear regression for classification. 1) outliers : in general outliers make the model inaccurate and we should fix or remove them. 2) what if y>1 or y 1 and and y
What is the problem with the weight intercept on the linear regression line being greater than 1 or less than 0 ? We're classifying only based on one criteria of the intercept being greater than or less than a particular value like 0.5, right?
#doubt 9:10, just like we indirectly took 0.5 as the threshold point for determining obese and not obese, and when we had some data point far from other then we are also considering 0.5 as the threshold point, why cannot we also change or update our threshold point according to the data, like we could have updated our threshold point to 0.3 and again it would work fine?
At 08:41 you mentioned that because of outlier our best fit line has changed and we are not getting desired output so we should not use linear regression. But a person would say if we get rid of such outliers we can still go ahead using Linear regression and ideally we should be removing outliers. So how do we support the fact that we should not use Linear regression?
What if your outlier points are the input and you need to predict the results for it, Please refer the video in this link th-cam.com/video/2TvKZnTHC4M/w-d-xo.html
I think the main problem is we use probability to find whether it is obese or not, when you use the linear regression, you got probability less than zero and greater than 1, which is not possible, hence we can't use linear regression in the case of these type of classification problems. If i am wrong, please correct me. I appreciate your explanations on your videos
Hi Krish, I loved the way you are teaching us about data science concept. I saw the above vedio and have one doubt in it i.e. If we remove the outliers from data, can we use linear regression for classification problem?
I doubt your reasoning... Shouldn't we remove outliers first before fitting any model? If we have outliers even in regression problems, won't the results be bad even for linear regression because the best fit line will be wrong?
i really can't describe how many platforms i went through like statquest, datacamp, geeks for geeks, stanford, udemy ,towards data science and much more but never saw such explaination . this is the first time when i am commenting on a youtube video .your work made me do so .you are seriously a great teacher krish . thanku for sharing your knowledge in such a clearer way.
Yep coming from StatQuest.....
Me too
I think it is even better than 3blue1brown's explanation. Don't you think so?
try applied Ai course
Other sites just discus about the code. I believe a ML engineer should have a good knowledge of some aspect of maths and statistics
your channel has brought a huge transition in our lives , especially people who are looking for a career transition. Thank you and your channel for sharing your knowledge and making it easy for us .
Sir, you are my role model...
Your aim " sharing knowledge " is incredible... #Keep going
You're one of the reason for me to dive in data science community. I learned from your videos and interesting , felt more to learn. Thank you Krish.
what's your position now ? please reply I am a freshie to data science from nit
I must say Sir you are genius. What an explanation of logistic regression. It's awesome and so simple that anybody can understand. If you watch this video twice you will never face any problem in future to explain logistic regression.
Having watched many videos related to difference b/n LinR and LogR, nobody explained in this way. I think this is what makes stand out. Just fabulous!
Saying Thanks is not enough to KRISH Sir...Daily, Your Video make's my Day fullfillment.....Praying God to have grace over you all the time...
I did Data science program with simplilearn and also reffers much yt channels bt ur the guru we need. much simple language and clearer concept thnx
Your explanation is beyond excellence... After a lots of traveling finally I find a outstanding platform to learn data science.. I am pursuing Ph.D in machine learning.. And this tutorials make my PhD interesting...thanks a lot...
Superb explanation on why we should go for Logistic Regression. I don't think we can find this kind of clear explanantion in most of the channels. Thank you for presenting us the concept in easy and beatiful manner.
Superb explanation.Earlier i was always confused about logistic regression and linear regression as I was unable to understand the mathematical intuition behind it.This is mainly asked as interview questions in product based companies I guess. Thanks Krish.
Amazing Krish.. thanks for such effort to explain it so easily. You are our Andrew ng
this is much easier and simpler to understand than what was taught in paid course ..thankyou so very much .You made life simpler for non ML background guys like me .
You are amazing ... was having a long run doubt of why to use logistic regression ... fantastic explanation. Great Work .. Much Appreciated.
This is the best explanation I've seen about this topic. Thank you very much, Sir.
Wow sir, abhi pura upgrad ka module padha but know chahiye tha logistic ab samagh aya
🤩🤩🤩
Currently pursuing my MS in Data Science and Business analytics, this is the most simple and clear explanation on Logistic regression!! Good work Krish:)
This is what happens when you really want to teach and its not all about the money.
Krish took the time not just to teach the concept but also connect it to reality.
Good stuff boss.
Hope the quality of your work only increases.
I love these videos. You make this puzzling stuff seem so accessible.
Simple concept but the explanation you gave was quite powerful so that we will remember the core concept... Thanks Krish 👍
Sir you are the best teacher from youtube
May Almighty bless you
one of the few videos that I've watched in 1.25x and not 2x. On point
explanied very clearly and in short, keeping going. all the best sir.
Brother take my gratitude. Huge respect for you.
You are a really great teacher. Thank you for sharing this.
Best ever explanation thanku so much for your effort👏👏👏
Superb content & direct to the point of discussion without wasting much time.
Thanks krish,
you are great sir ,your lecture help me lot to clear the concept in ML ..
Sir I love when you say the word-------> "UNDERSTAND" :p :p
bro this comment has just came straight from my heart even the andrew ng have not explained with that much clarity even though i know you somewhere learn but your explaination is quite well as i have done ML from 2,3 online courses but i will check whole your playlist again only for the deep understanding of math behind it
andrew explained it really well , but he uses more complex language which is harder for us to relate to. I understood andrew really well too after watching his logistic regression videos twice each.
bro every youtuber learns from andrew ng
Krish bro you are the best and your contents are magic over the internet!!
tutorial 34 was clarified my doubts, I am waiting for part 2 & 3 of performance metrics for Classification problem in ML. Please upload the part 2 & 3 of tutorial 34 sir.
Thank you bro please continue it helps a alot
Awesome explanation Sir, m waiting ur video on daily basis, sir plz upload maths behind every algorithm, which is helpful for us
its really wow!! thank you so much sir for this beautiful explanation.
Great stuff keep it up 🤗🤗🤗
Krish sir, you are the best....
one of the best tutorials.I just love it
great teacher ever❤
Excellent lecture sir, i am going to watch the entire playlist. Keep up your good work. All the very best sir.
Mind blowing explanation guruji . Happy teachers day
Awesome explanation sir. Mind blowing
Lectures of krish & Andrew ng course on ml is a nice combo..!! 😁
Thanks for sharing this Very Very Very useful knowledge.
Very useful video. Thanks Krish.
Awesome sir👌... superb explanation 🙏... please continue to upload videos sir.
Thank u so much sir... Very good info 👍👍
Brilliantly explained
Very nice explanation! Definitely helped my intuition.
Best explanation, better than my prof😂
Very clear explanation! Looking forward to more videos!
Can anyone tell where he stays I have to touch his feet for this incredible talent of teaching maths 🙏🙏🙏🙏
and students who are from primary schools ,don't watch this video and dislike that just bcoz you didn't understand
Hi Krish. Thank you very much for your videos. I just have questions regarding the reasons you mentioned why we cannot use linear regression for classification. 1) outliers : in general outliers make the model inaccurate and we should fix or remove them. 2) what if y>1 or y 1 and and y
I was searching the comment section for answers to this question, but no one even questioned this
It is actually lies between 0 and 1 for most of times or may be refers to error while implementation
Your explanation is superb
simple and effective explaining .
Thanks for sharing your another great tutorial
Best explanation so far!
amazing stuff
Great explanation
You are an amazing person. I hope you know that.
Easy yet effective.
Very good vedeo, well explained...
finally i understood the concept behind it :)
thank you soo much sir
Great lecture, thankyou so much for everything
listen between 3:05 to 3:15
with deep learning (deep hearing) there is fun
Thanks sir , again it is very simple to learn everyone
Thank you for such good explanation.
Best lecture ever
What is the problem with the weight intercept on the linear regression line being greater than 1 or less than 0 ? We're classifying only based on one criteria of the intercept being greater than or less than a particular value like 0.5, right?
Comprehensive explanation, Part 2 and 3 please...
Check the complete ML playlist
Why use logistic instead of linear for binary classification ?-The reason has been well explained.Thanks
Very good explanation thank you so much
intro music is good. great content.
Sir your lecture series has made my learning simpler. Thank you. If possible, please build a playlist of different neural networks.Best Regards
#doubt
9:10, just like we indirectly took 0.5 as the threshold point for determining obese and not obese, and when we had some data point far from other then we are also considering 0.5 as the threshold point, why cannot we also change or update our threshold point according to the data, like we could have updated our threshold point to 0.3 and again it would work fine?
Very Well Explained.
Great lecture
incredible effort
Thanks a lot for your amazing videos 💜
Great job
At 08:41 you mentioned that because of outlier our best fit line has changed and we are not getting desired output so we should not use linear regression. But a person would say if we get rid of such outliers we can still go ahead using Linear regression and ideally we should be removing outliers. So how do we support the fact that we should not use Linear regression?
He said there are two reasons: the outliers and the outputs>1 or outputs 1 or outputs
@@ouryly1541 Greater than 1 means it is even greater than 0.5 .. same with negative...
Not sure of ur explanation
What if your outlier points are the input and you need to predict the results for it, Please refer the video in this link
th-cam.com/video/2TvKZnTHC4M/w-d-xo.html
nice video
Thanks Krish
This person is Gem
YOU ARE THE BEST
Thank you sir ♥️
Great video. Can you also help me with the difference between Multiclass and Multilabel classification?
well explained ..
Thanks
I have a doubt,
We are not passing a raw data to train a model, Anyhow we do preprocessing, from That we can remove outliers.
I think the main problem is we use probability to find whether it is obese or not, when you use the linear regression, you got probability less than zero and greater than 1, which is not possible, hence we can't use linear regression in the case of these type of classification problems. If i am wrong, please correct me. I appreciate your explanations on your videos
Thank you 😊
Hi Krish,
I loved the way you are teaching us about data science concept. I saw the above vedio and have one doubt in it i.e. If we remove the outliers from data, can we use linear regression for classification problem?
At 6:55 to 7:10 you have highlighted point as negative why? Probability cannot be below 0, I could not understand that particular point
great explanation.
I doubt your reasoning...
Shouldn't we remove outliers first before fitting any model?
If we have outliers even in regression problems, won't the results be bad even for linear regression because the best fit line will be wrong?
Just wow!!
Thank you so much sir
Thank you
very good