Best explanation of cost function, we learned it as masters students and the course couldnt explain it as well.. simply brilliant

I never understood what is a gradient descent and a cost function is until I watch this video 🙏🙏

I have seen many teachers explaining the same concept, but your explainations are next level. Best teacher.

I don't see a link on the top right corner for the implementation as you said in the end.

Why am I not surprised with such a lucid and amazing explanation of cost function, gradient descent,Global minima, learning rate ...may be because watching you making complex things seems easy and normal has been one of my habit. Thank you SIR

How can I not say that you are amazing !! I was struggling to understand the importance of gradient descent and u cleared it to me in the simplest way possible.. Thank you so much sir :)

For those who are confused. The convergence derivative will be dJ/dm.

Best video on youtube to understand the intution and math(surface level) behind Linear regression. Thank you for such great content

The video was really great. But I would like to point out that the derivative that you took for convergence theorem, there instead of (dm/dm) it should be derivative of cost function with respect to m . Also a little suggestion at the end it would have been helpful, if you mentioned what m was, total number of points or the slope of the best fit line. Apart from this the video helped me a lot hope you add a text somewhere in this video to help the others.

Really awesome video , so much better than many famous online portals charging huge amount of money to teach things.

Awesome!! Cleared all doubts seeing this video! Thanks alot Mr. Krish for creating indepth content on such subject!

Thanq so much for all your efforts.... Knowledge, rate of speech and ability to make thing easy are nicest skill that you hold...

A small comment at 17:35. I guess it is Derivative of J(m) over m. In other words, the rate of change of J(m) over a minute change of m. That gives us the slope at instantaneous points, especially for non linear curves when slope is not constant. At each point of "m, J(m)", Gradient descent travels in the opposite direction of slope to find the Global minima, with the smaller learning rate. Please correct me if I am missing something. Thanks for a wonderful video on this concept @Krish, your videos are very helpful to understand the Math intuition behind the concepts, I am a super beneficiary of your videos, Huge respect!!.

The best I've come across on gradient descent and convergence theorem

Really thanks you krish. you just cleared my doubts on cost function and gradient descent. First I saw Andrew Ng class but have few doubts after seeing you video. Now its crystal clear.. Thank You...

Great! Fantastic! Fantabulous! tasting the satisfaction of learning completely - only in your videos!!!!!

This is the best stuff i ever came across on this topic !

Best explanation of Linear Regression🙏🙏🙏.Simply wow🔥🔥

Hi Krish, Thanks for the video. Some queries/clarifications required: 1. We do not take gradient of m wrt m. That will always be 1. We take the gradient of J wrt m 2. If we have already calculated the cost function J at multiple values of m, then why do we need to do gradient descent because we already know the m where J is minimum 3. So we start with an m , calculate grad(J) at that point and update m with m' = m - grad(J)* learn_rate and repeat till we reach some convergence criteria Please let me know if my understanding is correct.

Thankyou for this awesome explanation!

i knew the concept of Linear Regression but didn't know the logic behind it.. the way Line of Regression is chosen. Thanks for this!

Thank you Soo much Krish. No where I could find such a detailed explanation You made my Day!

Please add the indepth math intution of other algorithms like logistic, random forest, support vector and ANN.. Many Thanks for the clearly explained abt linear regression

Thank You Sir, You have explained everything about gradient Descent in the best possible easiest way !!

Such a great explanation of gradient descent and convergence theorem.

This maths is same as coursera machine learning courses Thank you sir for this great content ..

you just made the whole concept clear with this video,you are a great teacher

So beautifully explained...did not find anywhere this kind of clarity....keepnup the good work....

There's a little correction in Convergence Theorem: derivative of J(m) should be there in place of derivative of m in numerator.

Watched this video 3 times back to back .Now its embaded in my mind forever. Thanks Krish , great explanation !!

Best video on theory of linear regression! Thankyou soo much Krish!

Hi Sir, I am from cloud & DevOps background is it make sense to go & learn Ml AI, what path I can follow to become a dataops engineer or devops ml ai engineer.

every line you speak..so much important to understand ths concept......thank u

No one can find easiest explanation of gradient descent on youtube. This video is the exception.

god bless you too sir, explained very well. basics helps to grow high level understanding

Before watching this video I was struggling with the concepts exactly like you were struggling in plotting the gradient descent curve. ☺️Thanks for explaining this beautifully.

I knew that their will be an Indian that can make all the stuffs easy !! Thanks Krish

This is a really good explanation for Linear Regresison, Krish.. looking forward to check out more of your videos..thanks and keep going!!

Implementation part: Multiple linear Regression - kzfaq.info/get/bejne/a9imoc-LvODTqGw.html Simple linear Regression - kzfaq.info/get/bejne/e5Ooo5CEz8yzg40.html

Your explanations are the clearest!!!

Thank you my friend, you are a great teacher!

Sir, you are outstanding. Please keep it up

Hi . Can you please do a video about the architecture of machine learning systems in real world . How does really work in real life .for example how hadop (pig,hive) , spark, flask , Cassandra , tableau are all integrated to create a machine learning architecture. Like an e2e

one of the best explanation so far :)

Thanks so much sir.. you're doing good for the community

sir i can' find the simple regression and multiple regression video as u said and some videos are little jumbled its getting difficult to follow the videos and plz do explain the functionalities of each and every keyword or a inbuilt function when ur explaining the code...ofcourse ur explaining in a very good way but i faced a liitle problem while folllowing that practical implementation of univariate,multivariate,and bivariate analysis(there you have used FACETGRID function)..so will u plz expalin me what is the exact use of facetgrid...?

you are ultimate, got answers to some many questions, video is good.

Value of the video is just undefinable! Thanks a lot :)

Oh my gosh this is awesome tutorial I ever seen God bless you sir🤩🤩

Hi krish, that was an awesome explanation of Gradient Descent. With respect to finding the optimal slope. But in linear regression both slope and the intercept are tweakable parameters, how do we achive the optimal intercept value in linear regression.

my god that was clear as crystal...thanks krish

lovely! love it.

Thank you so much, Krish!

Loved it. Thanks Krish.

Really great sir. I very much thank you sir for this clear explanation

As always Krish very well explained!!

Great Tut sir got things pretty quick with this video ty

Hi Krish, That was an awesome explanation for the maths used for linear regression, especially for the cost function. Can you make a video on 5 assumptions of linear regression and also explain the assumptions in detail.

Thank you! This video was so good!

Thankyou sir...Get to learn so much from you.

Great sir. Love this video

Thank you Krish bhaiya!

Best explanation. Thank you!

Nice Explanation, I like this.

At 14:56, how do we decide how many slope values to try? and how about selecting intercepts in a certain range?..

What about the C (intercept) value? how does the algorithm selects the C value?

Thanks for all great prepared videos, I think you meant (deriv.J(m) / deriv(m)) at 17'.45", is it correct?

Yaar you nailed it man after watching sooo many videos i had some Idea , By Finishing your Video now i m completely clear 😍😍😍😍

Similar to Andrew NG course from coursera kind of revision for me 😊😊

It would be great if you could suggest some best books for python programming?

I had so much difficulty in understanding gradient descent but after this video It's perfectly clear

really great explanation sir 😍

This is super helpful!

Thanks Krish u are helping alot

Finally I understood the perfect answer of gradient descent..

your videos are clear and easy to understand

excellent video u are a champion man

great video sir, so lucid

We would also recommend your videos to our students!

Amazing explanation! I have one question, from where did you study all of this? Some books or the net?

Great...

Excellent!!!!!

never found a better explaination

Hi Krish, how to calculate the intercept value as in this we have initialized it to 0 and we have not calculated at the end. We have calculated only slope of best fit line.

can you do more math intuition s please. These are very helpful. Thanks!

This video is really helpful.

Very nice explanation. Thank you.

Hi, Tried searching links but not able to found could you share the link for better practice. Thanks

good expplanation now clear all queries

Nice tutorial. Thank you

Excellent Explanation

very well explained Thank you.

best one sir thank you so much

Great,but not able to find the link for how to implement in python,plz awaiting for your valuable reply.

This guy was born to teach

where is the link on the top right corner for the implementation as you said in the end?

the only video that made gradient descent so simple that even 2nd grade students woud understand

Very good and detailed explanation

Excellent explanation sir. I have started following your videos for all the ML related topics its very interesting. One doubt = In Gradient Descent, when slope is zero, M value will be considered as the slope of the best file line. I do not understand this. Can you please explain here? Thanks.

amazing man!