For notes👉 github.com/ranjiGT/ML-latex-amendments

What is great about this particular video is these concepts are explained well in many places like scattered dots , you connected the dots to paint the whole picture . an example for gradient descent included - very helpful .

You just helped me understand hundreds of web pages that talked about topics with no order. Thank you

You are such a great teacher. Concepts are clearly explained beginning with the basics and slowly easing into the most advanced level. Thank you

My god you are perfect I think your work should reach more audience your best and clear than the renowned ML yputubers. Applause Ranji

The best and clear explanation I've ever listened about Gradient Descent. Keep up the good work!🙌

this might be the most underrated explanation on youtube

Hi, Your video is helpful for beginners to understand the concept. One suggestion: In the very beginning of the video when you write the equation of your predicted line remember to mark it as y(cap) = mx(i) + c. It is not y(i) which is the actual data point.

its a very good description, The way you teach is humble and appreciatable.

When my ML teacher teaching this , I felt I am learning some rocket science ,but you are teaching it felt very easy , thank you Sir😊

I was unable to understand this topic tried many videos but this was the most useful video thankss

This is what I pay my internet bill for! Thanks a lot!

Your hard work made the concept very easy to grasp. Kudos.......

Everything is so easy on this channel, great work Man!

Hey thank you so much for this content since I started studying regression using your videos , I became huge fan of yours

Truest your the best. You solve my long time machine learning challenge.

I am a beginner and as a beginner I was struggling understand the gradient decent concept. I have seen many videos on gradient decent however all of them skipped explaining the derivative part however you explained it very well both (total and partial) with solution . Thanks!

Man, you've won my heart, you kept it so simple, best way of explaining Gradient Descent. Can you please help me in using learning rate in the equation and number of steps used in gradient descent with an example.

Sir thank you very much, this has been so helpful since my course will only get tougher from here onwards and u helped me understand the basics

Great explanation with an example. This is the way to explain such concepts.

perfect video for core concept understanding , amazing.. I love the explanation.. thankyou so much

As far as I know, gradient descent doesn't talk about solving 'm' and 'c' directly by putting them into two equations like the way you did it here..Because sometime the expression that we get after differentiation becomes so complex especially with logistic regression, random forest and other complex model in deep learning that solving 'm' and 'c' directly becomes extremely tricky and time complexity becomes very high...So Gradient descent talks about solving 'm' and 'c' by some trial and test method. Starting with some dummy value of 'm' and 'c' and putting those values in the equation of differentiation and check if the value of differentiation(say D0) for those 'm' and 'c' becomes 0 or close to 0. If not, then subtract that differentiation value(D0) from the old value of 'm' and 'c' and get a new 'm' and 'c' and again check if the value of differentiation (say D1) for those new 'm' and 'c' comes close to 0. Continue like this till you find that old 'm' - value of differentiation is very very close to 0. That 'm' becomes your actual 'm' and similarly the same thing to be done for 'c'.

Very good explanation. It would've been good if you could've explained the usage of learning rate usage to find a minimum point.

Very good explanation of gradient descent

Hi @ranjiraj , at 21:37 u have given a wrong explanation in the partial derivative w.r.t C... d/dC (-C) will be -1 ,then why are you treating it as constant whereas in d/dc mx should be taken as constant.

simple and useful lecture.. thanks

Thanks for the clear explanation sir

Really amazing, thank you so much sir, keep rocking

Well explained bro ❤ just bring another video for statistics and linear algebra 🎉

explanation is simply awesome.....

very nicely explained Bro

will the sign ( direction ) for calculating m and b at last change, from addition to subtraction if we take "y-pred - y" instead of "y - y-pred" lke you have done in cost function ? i saw at few articles where this was mentioned but it was not clear.

killer explanation !! amazingly amazing !! thank you bro

Thank you so much, I got a clear picture of the topic now.

you earned a subscriber my man

sir u explained so well

Thankyou sir , Make more videos on machine learning concepts .

Thank you! Wish me luck on exam about it!

excellent video

great explainnation

Great video man. Loved it.

Thanks for this tutorial sir..made it very easy and simple.

one of the best

Best explanation sir

Thanks a lot sir! It was really helpful. Excellent explaination.

your videos are really nice, good content and presentation...keep it up sir.

really nice approch to teach thank you sirji

Absolutely the best explanation

i have this video over and over again, it the most satisfying video i have seen, in as much as gradient decent is concer, but i have questions, 1 what happen to the 2 tha became the multiple of the function as chain rules implies, then what happen to the no in the cost function. i know its mean squared errored thing. in my small assumption either of the values cant be thrown away just like that mathematically. please help with explanation.

Thanks so much for the video. It helped me a lot.

Thank you so much sir for such a perfect explanation....🙏🙏👏👏👏

Sir linear regression is not used for classification as you said in satrting of video while explaining

between the timestamp 21:39 to 21:45 you told that partial derivative of y(i)-mx-c with respect to c is 0 so only take minus sign which is wrong it will be -1 because of here c is not constant.

Lovely explanation

can you please tell me why the curve is much sharp when you draw the graph with respect to j and c ? please tell

At 20.37, you discarded n/2. I would like to know why?

Thanks bro.

That is a explanation. I have one question, where would the learning rate be actually used in computation. Like in your numerical example. We found the outputs and calculated corresponding m and c. How does the learning rate is catered. Secondly, when we multiply learning rate with derivative, what does it gives us ?

thank you so much sir

The explanation of mathematical formula is absolutely fantastic. The explanation was about with single feature. But if we have multiple feature, what to be changed in the equation? Can you please let us know that. Thanks very much, and we will love to see this kind of videos shortly.

Hi one question here: First derivative: xsquare =2x Second derivative = 2 ( replaced in same location) Third derivative = 0 While applying same in mean square error formula First derivative= I understood square to 2/n(•••) Second derivative: with respective to slope It should be 2/n I=1 to n (yi -xi -c) here I applied second derivative replacing. Since -mxi converts to -xi. But In ur explanation instead of replacing, you brought second derivative to starting as below: 2/n I= 1 to n -xi(yi-mxi-c) In the same way for intercept. One more At the end , what happened to 2/n? Please correct me if I am wrong.

Thanks Buddy :)

Nicely explained.

Well explained

Hi Ranji Sir, i have a doubt. In 17:00 you have mentioned that we need to take derivative because we have two variables. And mentioned variables as x and c. But I think you were supposed to say m and c. later in 17:27 you mentioning about two parameters m and c. Please verify whether it correct or not. If i have pointed out wrong please apologize me.

Thanks ...very well explained

why do we set the derivative equal to 0? i mean the gradient at the minima might not be equal to zero for all curves

Amazing lecture. x^n, will not have a 3rd order derivate to be 0, it will be n+1 order derivate.

great explaination

keep up the good work !

Sir my maths is quite weak i want to start my career in data science i know that i can make my math strong but how should i start to learn maths for data science

can you please explain me why we are squaring the step at 4.36 . everything is clear to me except this one squaring step I cant able to understand..

What should i do if i am to apply learning rate of some value?

thank you!!!

What type is it batch gradient?

dear sir, I still cannot connected what is the purpose we do m = m - lambda * dJ/dm and c = c - lambda * dJ/dC

Hi Ranji sir, I wanted to ask that if there are if our line is of the form M1*(feature1) + M2*(feature2).... Mn*(feature n) + c, do we have to follow same steps and calculate dJ/dm for all M1, M2...Mn?

Excellent...

Why do we need partial derivative when we have the total derivative?

So gradient descent is our cost function to calculate the error

J = 1/(2*m) * sum (h(x)-y)^2. being h(x) the hipotesis and y the accurate value... at 3:37 you got them mixed up right? damn man.. no wonder people get confused

Linear regression for classfication at 0:20???

Thanks you so much but I have small clarification regarding differentiate. why we are differentiate with respect to m , c & why we should not differentiate with respect to x to find out the value of y..

hi, sir do you have the python code using tensor flow or do you have any recordings of ML using TF

can this method work for an equation with multiple slopes?

How did the 2/n gone from equation when dJ/dm and dj/dc was assigbed to 0

bro please ans this question why we are taking summation of c in one equation and not in other ---> one is 5C why

Thank you so much..quick question, when/how do we use the learning rate in this regard?

is it a sweet, in the middle of a hyperplane?

thank you sir

Thanks Bro :)

🙂👍 nice like that

why at the end multiply the 2 nd equation with 5

thanks a lot..... :)

Which level of maths is required 11th and 12th or degree level mathematics ?

sir explane krne ke baad thoda side ho jaya kijiye screenshot lena rehta hai

Sir can you recommend a book for machine learning with mathematical background please

Sorry bro, Not clear what you saying..

havoc explanation

Doesn't he look like Bharati's (a comedian on Kapil Sharma's show) husband?😅