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 .

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.

Amazing Krish.. thanks for such effort to explain it so easily. You are our Andrew ng

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...

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!

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.

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.

Currently pursuing my MS in Data Science and Business analytics, this is the most simple and clear explanation on Logistic regression!! Good work Krish:)

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...

This is the best explanation I've seen about this topic. Thank you very much, Sir.

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 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

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.

Sir I love when you say the word-------> "UNDERSTAND" :p :p

I love these videos. You make this puzzling stuff seem so accessible.

Brother take my gratitude. Huge respect for you.

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 .

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

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

You are amazing ... was having a long run doubt of why to use logistic regression ... fantastic explanation. Great Work .. Much Appreciated.

Simple concept but the explanation you gave was quite powerful so that we will remember the core concept... Thanks Krish 👍

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.

one of the few videos that I've watched in 1.25x and not 2x. On point

You are a really great teacher. Thank you for sharing this.

Wow sir, abhi pura upgrad ka module padha but know chahiye tha logistic ab samagh aya 🤩🤩🤩

Sir you are the best teacher from youtube May Almighty bless you

Awesome explanation Sir, m waiting ur video on daily basis, sir plz upload maths behind every algorithm, which is helpful for us

Thank you bro please continue it helps a alot

Best ever explanation thanku so much for your effort👏👏👏

explanied very clearly and in short, keeping going. all the best sir.

Superb content & direct to the point of discussion without wasting much time. Thanks krish,

Krish bro you are the best and your contents are magic over the internet!!

you are great sir ,your lecture help me lot to clear the concept in ML ..

Krish sir, you are the best....

great teacher ever❤

Excellent lecture sir, i am going to watch the entire playlist. Keep up your good work. All the very best sir.

listen between 3:05 to 3:15 with deep learning (deep hearing) there is fun

one of the best tutorials.I just love it

Lectures of krish & Andrew ng course on ml is a nice combo..!! 😁

Brilliantly explained

Thank u so much sir... Very good info 👍👍

Great Explanation sir, Thank You

its really wow!! thank you so much sir for this beautiful explanation.

Thanks for sharing this Very Very Very useful knowledge.

Very useful video. Thanks Krish.

Easy yet effective.

Best explanation, better than my prof😂

Mind blowing explanation guruji . Happy teachers day

finally i understood the concept behind it :) thank you soo much sir

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?

Great lecture, thankyou so much for everything

Very good vedeo, well explained...

Awesome explanation sir. Mind blowing

Thanks for sharing your another great tutorial

You are an amazing person. I hope you know that.

Best lecture ever

Very nice explanation! Definitely helped my intuition.

Awesome sir👌... superb explanation 🙏... please continue to upload videos sir.

Sir your lecture series has made my learning simpler. Thank you. If possible, please build a playlist of different neural networks.Best Regards

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

Very clear explanation! Looking forward to more videos!

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?

Great stuff keep it up 🤗🤗🤗

Comprehensive explanation, Part 2 and 3 please...

Best explanation so far!

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?

Thanks sir , again it is very simple to learn everyone

YOU ARE THE BEST

amazing stuff

Your explanation is superb

simple and effective explaining .

Why use logistic instead of linear for binary classification ?-The reason has been well explained.Thanks

Great explanation

Thank you for such good explanation.

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

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?

Great video. Can you also help me with the difference between Multiclass and Multilabel classification?

sir please upload SVM intuition

Hi Krish, this is well explained. However, why are we even considering 'adding' an outlier to existing model, which would change the threshold? If we just predict using 0.5 threshold for the new sample point without including it in model, it will give correct answer. By the logic explained in the video, every model will change the hyper plane on addition of new data points. Please let me know what f I am missing something.

Thanks

Very good explanation thank you so much

This person is Gem

Very Well Explained.

Thank you sir ♥️

great explanation

Thanks a lot for your amazing videos 💜

Great lecture

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.

Hey bro, can you help me out with from sklearn.linear_model import LinearRegression? I get an error message from using. This is the error message I'm getting. from R and python.ModuleNotFoundError Traceback (most recent call last) in () ----> 1 from sklearn.Linear_model import LinearRegression 2 regressor = LinearRegression() 3 regressor.fit(X_train, y_train) ModuleNotFoundError: No module named 'sklearn.Linear_model' Thanks bro.

#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?

Thank you so much sir

Great job

intro music is good. great content.

Thank you so much

Thanks Krish

well explained ..

great work

Thank you 😊