I was searching for one resource where I can learn about machine learning.. after watching this video I found one great resource... Explanation was good.. but explain while writing code, don't skip to that writing part of coding, . Please Regularly come with great content like this..All the very best to this channel
Okay... Thank You very much for your feedback. I will not skip the writing of codes from my next videos... And sure, i am coming up with content regularly
it would be great if dataset used here would be available for absolute beginners, it is one of the easiest explanation to understand cost function and why gradient decent algorithm is being used to minimize the cost. A lot of minute details related to numpy & algorithms is being given here which help a novice to feel like champ.
It’s a great explanation. In Machine Learning, I still wonder what can we get from the Linear Regression analysis. Could you explain us what information or advantages from LR analysis?
It gives a sense of how predictive certain trends might be. Lineair regression is a way of predicting response variables to explanatory variables. If you know how much Y change for an n amount of X, then you can predict for example the earnings for next year based on how much you advertise etc.
good job. I have seen simple and multi-linear regression videos,. one question, why is that your implementation of both models not working with learning rates 0.01 and 0.001? [ I think it is the most commonly used values in regression]
Thank You very much for your question. Your question made me learn something new as well. Here is my answer : We can initialize theta with any random values, but these values must be closer to zero. Or in other words, modules of theta should be small enough, so that its close to zero ( |theta| ~ 0 ) If |theta| is too big, then | y_pred | becomes too big, and (y_pred - Y)^2 ( in cost function) becomes almost equal to y_pred^2 only, i.e, (y_pred - Y)^2= y_pred^2. Thus the model will not learn. There is no exact way of knowing how small |theta| should be to train our model well. So when we initialize theta to zeros, it adapts itself in such a way that y_pred becomes similar to Y, by minimizing the difference (y_pred - Y)^2. That's why we initialize theta with zeros and not with any other value, because it can lead our model to not train at all. I hope it helps !
Hi Hana... I won't be able to make that video, coz it is not directly related to Machine Learning. You can refer to this link - www.geeksforgeeks.org/decorators-in-python/ I find geeksforgeeks articles very intuitive and easy to understand. Hope it helps.
Hi... I have taken partial derivatives that you are talking about. You can refer to my gradient descent video on linear regression. In that video, I have derived an expression after computing partial derivatives and I am using that expression here.
@@varshafegade4688 I have taken derivative w.r.t to both of theta 0 and theta 1. The equation has theta matrix, which has both theta 1 and theta 0. And thus the derivative is also taken for both theta 1 and theta 0, but is represented in 1 single equation by using matrix mutlplication.
Sure, I’m trying to do a linear regression on forecasting revenue based on what we spend. So, I’d like to understand at what point does spend stops affecting the revenue? Would you know how to obtain that from this regression analysis?
@@sumaiyahashim3535 Sure.. I will need to to look at the problem statement. Can you mail me your contact details (whatsapp number with country code or any social media handle ) along with more information/resources/links on the problem you are trying to solve , at my mail - codeboosterjp@gmail.com ? I will let you know, if I can help you in any way further from there.
Hi, it was provided in the pdf notes. But here is the link again: github.com/Jaimin09/Beginners-Machine-Learning-Explained-Simply/blob/master/Assets/data.txt
Thanks for your question. Writing the complete model helps us to understand how machine learning models work behind the hood. This way, we can later develop new complex models that are not available directly to import. There are many complex deep learning models in Computer Vision and Natural Language Processing. We need to understand these basic models to understand those complicated models. But yea, when we just want to use these simple regression models, then we have option to directly import and use them. And I will also make videos on how to do so, in future.
I was searching for one resource where I can learn about machine learning.. after watching this video I found one great resource...
Explanation was good.. but explain while writing code, don't skip to that writing part of coding, .
Please Regularly come with great content like this..All the very best to this channel
Okay... Thank You very much for your feedback. I will not skip the writing of codes from my next videos...
And sure, i am coming up with content regularly
I've been searching for exactly this implementation. Your the only one who implemented this Thanks it was really helpful.
Thank You so much ! Its really good to hear that... I am glad that it was helpful to you 😇
In X why are we having the first column as ones?
Tried multiple vids for understanding this concept... finally understood after this vid...
Great work...
Subscribed
when we do using gradient descent ,we consider partial derivative with respect to theta 0 and theta 1
Thanks for the short video. However, I needed some other videos and searching as a beginner.
it would be great if dataset used here would be available for absolute beginners, it is one of the easiest explanation to understand cost function and why gradient decent algorithm is being used to minimize the cost.
A lot of minute details related to numpy & algorithms is being given here which help a novice to feel like champ.
Thank you so much. Highly appreciate how you observed the details. Glad it was useful 🙂
Why do u run written code fast ??? Some people may find it difficult to understand
Amm... I only fast forward the code that is not related to the concept I am teaching. I will consider this comment from next time onwards though
hi bro u have done an excellent video please do more all the best
Hi Praveen! Thank You so much! This really means alot to me !!
Thank you so much for the amazing stuff. Looking forward for your more videos.
Sure Malik ! Next video coming tomorrow
One thing about the code
Please add resize on Y only after adding X = np.vstack((np.ones((X.size, )), X)).T (In the beginning phase of the code)
Frankel, u should have 1M subs, bro!!
Hahaha… thank you so much 😄 Happy to hear this!
It’s a great explanation. In Machine Learning, I still wonder what can we get from the Linear Regression analysis. Could you explain us what information or advantages from LR analysis?
It gives a sense of how predictive certain trends might be. Lineair regression is a way of predicting response variables to explanatory variables. If you know how much Y change for an n amount of X, then you can predict for example the earnings for next year based on how much you advertise etc.
Hi, thanks for a nice tutorial. One thing I did not understood is, why you are taking 1's in the X vairable as a first column ?
well explained JP i understood now thanks.
Happy to help 🙂🙂
Nicely done bro! Simply Explained indeed! I would love to host you on my Channel sometime :)
Hi Kunaal, you content and channel is great! I would love to colab with you too!
Nicely explained sir💙💙
Thank You so much Rahul ! Means alot to me !!
This is great… thanks
Very Good videos, thank you!
Thank You.. I appreciate your comment !
Why were you appending 1 when choosing random number to show the approximate prices?
good job. I have seen simple and multi-linear regression videos,. one question, why is that your implementation of both models not working with learning rates 0.01 and 0.001?
[ I think it is the most commonly used values in regression]
thank you so much for the explanation sir but i'm confused why theta is taken zero what if we take any other values
Thank You very much for your question. Your question made me learn something new as well.
Here is my answer :
We can initialize theta with any random values, but these values must be closer to zero. Or in other words, modules of theta should be small enough, so that its close to zero ( |theta| ~ 0 )
If |theta| is too big, then | y_pred | becomes too big, and (y_pred - Y)^2 ( in cost function) becomes almost equal to y_pred^2 only, i.e, (y_pred - Y)^2= y_pred^2. Thus the model will not learn.
There is no exact way of knowing how small |theta| should be to train our model well.
So when we initialize theta to zeros, it adapts itself in such a way that y_pred becomes similar to Y, by minimizing the difference (y_pred - Y)^2. That's why we initialize theta with zeros and not with any other value, because it can lead our model to not train at all.
I hope it helps !
@@CodingLane thank you so much for the answer
bro , awsm it izzz😁😁
Thanks Shubham !
Can you make a video about Python Decorators in complete detail and their uses in real-life applications?
Hi Hana... I won't be able to make that video, coz it is not directly related to Machine Learning.
You can refer to this link - www.geeksforgeeks.org/decorators-in-python/ I find geeksforgeeks articles very intuitive and easy to understand. Hope it helps.
@@CodingLane I will check it out 👍
Please do some math videos
For sure Sakib ! More mathematical explaination of other Machine Learning models are coming !!
@@CodingLane i am excited😙😙
how to create that data set
Hi, you can create a dummy dataset by just writing the data in a .txt or more preferably .csv file.
why you have not taken partial derivative w.r.t0 theta 0 and theta 1 and update both
Hi... I have taken partial derivatives that you are talking about. You can refer to my gradient descent video on linear regression. In that video, I have derived an expression after computing partial derivatives and I am using that expression here.
@@CodingLane taken only one derivative not other
@@varshafegade4688 I have taken derivative w.r.t to both of theta 0 and theta 1. The equation has theta matrix, which has both theta 1 and theta 0. And thus the derivative is also taken for both theta 1 and theta 0, but is represented in 1 single equation by using matrix mutlplication.
@@CodingLane Thank you ,now I understood
why d_theta doesn't have np.sum
The summation is implicit in matrix multiplication
How can we obtain a diminishing return from this analysis?
Hi Sumaiya,
Sorry, but I didn’t get your question. Can you please elaborate?
Sure, I’m trying to do a linear regression on forecasting revenue based on what we spend. So, I’d like to understand at what point does spend stops affecting the revenue? Would you know how to obtain that from this regression analysis?
@@sumaiyahashim3535 Sure.. I will need to to look at the problem statement.
Can you mail me your contact details (whatsapp number with country code or any social media handle ) along with more information/resources/links on the problem you are trying to solve , at my mail - codeboosterjp@gmail.com ?
I will let you know, if I can help you in any way further from there.
Please add video on KNN
Thank you for the suggestion. I will make a video on it.
Everything is good but if you don't explain the code side by side, Its not that profitable as it should be.
Thanks for the suggestion Divyam! From the next videos, I will be explaining code side by aide, and only skip the redundant explanations
can you please provide the dataset.txt file
Hi, it was provided in the pdf notes. But here is the link again: github.com/Jaimin09/Beginners-Machine-Learning-Explained-Simply/blob/master/Assets/data.txt
Why you write all this code??why you not just import regression model
Thanks for your question. Writing the complete model helps us to understand how machine learning models work behind the hood. This way, we can later develop new complex models that are not available directly to import.
There are many complex deep learning models in Computer Vision and Natural Language Processing. We need to understand these basic models to understand those complicated models.
But yea, when we just want to use these simple regression models, then we have option to directly import and use them. And I will also make videos on how to do so, in future.
🙌🙌🙌
Thanks alot Shreyash !! 🙌🏻
d_theta = 1/m * np.sum(np.dot(x.T, y_pred - y))