[@nandinibalyapally3388]

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

[@mohitpatel7876]

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

[@navjotsingh8372]

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

[@soumikdutta77]

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

[@manikaransingh3234]

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

[@koushikkumar4938]

Implementation part:
Multiple linear Regression - ruclips.net/video/5rvnlZWzox8/видео.html
Simple linear Regression - ruclips.net/video/E-xp-SjfOSY/видео.html

[@RJ-dz6ie]

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 :)

[@shubhamkohli2535]

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

[@tarunsingh-yj9lz]

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

[@anuragmukherjee1878]

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

[@tusharikajoshi8410]

what's J in this? Y values? I'm super confused about this d/dm of m, cz it would be just 1. and m I think is just total number of values. Shouldn't the slope be d/dx of y?

[@mdmynuddin1888]

@@tusharikajoshi8410 it will be the cost or loss (J)

[@mdmynuddin1888]

new(m) = m- d(loss or cost)/dm * Alpha(learning rate.

[@suhasiyer7317]

Super helpful

[@threads25]

I'dont think because it netwons method actually

[@animeshkoley6478]

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

[@anuragbhatt6178]

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

[@shailesh1981able]

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

[@supervickeyy1521]

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!

[@pranitaumarji5224]

Thankyou for this awesome explanation!

[@FaizanKhan-fn6ew]

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

[@skviknesh]

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

[@PRASHANTSHARMA-ev7rr]

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.

[@harshdhamecha5503]

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

[@salmanjaved2816]

Correct 👍

[@xanderkristopher1412]

sorry to be so off topic but does anyone know of a way to get back into an instagram account..?
I was stupid lost my password. I love any tricks you can give me.

[@nanditagautam6310]

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

[@azizahmad1344]

Such a great explanation of gradient descent and convergence theorem.

[@rambaldotra2221]

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

[@dhainik.suthar]

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

[@priyanshusharma2516]

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

[@V2traveller]

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

[@PritishMishra]

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

[@mayureshgawai5951]

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

[@nidhimehta9278]

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

[@pradeepmallampalli6510]

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

[@annapurnaparida7655]

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

[@moulisiramdasu6753]

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...

[@ngarwailau2665]

Your explanations are the clearest!!!

[@kunaltibrewal2881]

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

[@arunsundar489]

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

[@varshadevgankar8242]

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...?

[@magelauditore333]

Sir, you are outstanding. Please keep it up

[@ayurdubey4818]

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.

[@9902152322]

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

[@ankitchauhan6629]

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

[@nikhil1303]

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

[@w3r161]

Thank you my friend, you are a great teacher!

[@shhivram929]

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.

[@pearlbabbar7981]

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

[@shan5612]

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

[@deeptigoyal4342]

one of the best explanation so far :)

[@dan684al]

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

[@ahmedbouchou6893]

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

[@aayushsuman4592]

Thank you so much, Krish!

[@mellowftw]

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

[@tamellasivasubrahmanyam6683]

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

[@pjanjanam]

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!!.

[@vishwashah4109]

Best explanation. Thank you!

[@hardikshah_99]

Implementation Links:
Simple Linear Regression: ruclips.net/video/E-xp-SjfOSY/видео.html
Multiple Linear Regression: ruclips.net/video/5rvnlZWzox8/видео.html

[@nikifoxy69]

Loved it. Thanks Krish.

[@meetbardoliya6645]

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

[@ShiVa-jy5ly]

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

[@vishnuppriya5263]

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

[@shrikantlandage7305]

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

[@SanjeevKumar-dr6qj]

Great sir. Love this video

[@shaktirajsinhzala4588]

Sir,there is no playlist of this series where can I found that? About cdf,pdf...

[@python_by_abhishek]

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.

[@wellwhatdoyakno6251]

lovely! love it.

[@auroshisray9140]

Thank you Krish bhaiya!

[@arrooow9019]

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

[@dheerajnuka6245]

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

[@Neuraldata]

We would also recommend your videos to our students!

[@RanjithKumar-jo7xf]

Nice Explanation, I like this.

[@juozapasjurksa1400]

Thank you! This video was so good!

[@nayanjain3594]

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.

[@mohitpatel7876]

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

[@ruchit9697]

The trials of slope selections go until the cost function reaches the local minima point ....and for intercept there are some random initialization techniques through which a fixed value is set for intercept....

[@ishanthakur3315]

You are taking derivative of cost function w.r.t. m in convergence theorem? Please reply!

[@shchiranth6626]

Great Tut sir got things pretty quick with this video ty

[@priyankachoubey4570]

As always Krish very well explained!!

[@namithapr4966]

Can you please provide the attached video link in this tutorial. I cannot find it

[@rezafarrokhi9871]

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

[@kevinsusan3345]

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

[@muralimohan6974]

Bro, how we update the slope

[@dileepkumar-nd1fo]

Hi Krish,
Where can i find previous video on Linear Regression ?

[@nivitus9037]

Great...

[@cutecreature_san]

your videos are clear and easy to understand

[@cynthialobo1995]

Very nice explanation.
Thank you.

[@akrsrivastava]

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.

[@slowhanduchiha]

Yes this is correct

[@vamsikrishna4107]

I think we have to train the model to reach that min. loss point while performing grad. descent in real life problems.

[@shreyasbs2861]

How to find best Y intercept ?

[@mvcutube]

Nice tutorial. Thank you

[@sahilswaroop1996]

excellent video u are a champion man

[@debrupdey7948]

great video sir, so lucid

[@ajinkyadeshmukh9674]

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.

[@PavanKumar-xg8ye]

Excellent!!!!!

[@avinashgote2770]

good expplanation now clear all queries

[@sharikatv1989]

This is super helpful!

[@rakeshenjapuri3143]

why we are using cost function and gradient sir what is the concluson
? can we apply as well multilinear and logistic regression also?

[@pradnyavk9673]

very well explained Thank you.

[@glamgalmanu]

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

[@Dinesh-uh4gw]

Excellent Explanation

[@Uma7473]

can u upload a video multi-label classification with eg using scikit-learn

[@jaspreetsingh5334]

Thanks Krish u are helping alot

[@nurali2525]

This guy was born to teach

[@satyamrout1400]

sir i think link for practical implementation is not provided can u pls give that link?

[@kushshri05]

Plz try to upload videos on this series in span of 2 days...

[@sampathkumarmanchala2237]

Doubt: Does model doest not select best slope by itself?

[@dhruv1324]

never found a better explaination

[@user-ec9he3nz7f]

really great explanation sir 😍