Great video. I've had classes for both machine learning and deep learning. I've passed the classes with good grades,but this series has really helped me deepen my understanding over data analysis in general. Great job on your videos,I'm very thankful!
this is marvelous..... i am searching through your channel right now to explore more... i am a beginner in machine learning and i believe your tutoring approach is giving big hope
Good tutorial. Simple and straight forward. Maybe a plot of the regression line and the original data points would have been nice for a visual. Seaborn does this but I don't think it gives the equation for the line??
You are right, Seaborn displays the linear regression line by default but it does not display the equation. Of course you can always get the slope and intercept values using linregress from stats in scipy package (or linear_model in sklearn like I showed in the video).
Hi Sreeni, why didn't you train_test_split in testing and training the model? Is it ok to train the model directly without splitting the data set. please explain to me when it is necessary to used train_test_split
this lecture is not up to the level as compared to other lectures @sreeni please explain with more examples when you write the code because at some point in time it feels like what is the use of this code and why it is written.
It is unfortunate that you do not find the explanation to be clear. Any suggestions on what is missing so I can fix it the next time I cover this topic?
Thank you! I tried to find a very beginner-friendly tutorial, and this is the best one I've found!
Great to hear!
Great video. I've had classes for both machine learning and deep learning. I've passed the classes with good grades,but this series has really helped me deepen my understanding over data analysis in general. Great job on your videos,I'm very thankful!
this is not very good is AMAZING. thanks sir I learned a lot with this tutorials.
Thanks for share the code also.. keep it up
this is marvelous..... i am searching through your channel right now to explore more... i am a beginner in machine learning and i believe your tutoring approach is giving big hope
Welcome aboard!
The R² value is good, and the residual plot (using seaborn) shows the randomness of the residue, implying the fit was good
Very good video, Thank you so much... Good work
One of the best expatiations. 👏👏👏👏👏👏👏👏
Thank you sreeni
thanks
Thank you sir, learned a lot as a novice student
Glad to hear that
You are the best.
Great content!
Good tutorial. Simple and straight forward. Maybe a plot of the regression line and the original data points would have been nice for a visual. Seaborn does this but I don't think it gives the equation for the line??
You are right, Seaborn displays the linear regression line by default but it does not display the equation. Of course you can always get the slope and intercept values using linregress from stats in scipy package (or linear_model in sklearn like I showed in the video).
in case anyone gets "module not found" for sklearn - open conda prompt and run "conda install -c anaconda scikit-learn"
Very nice lecture sir
Thanks for liking
thanks alot for the Video. Where can we find the file you were using?
thanks, it was helpful
Glad to hear that!
Hi Sreeni, why didn't you train_test_split in testing and training the model?
Is it ok to train the model directly without splitting the data set.
please explain to me when it is necessary to used train_test_split
I do not do train and test split for linear regression as the fitting provides MSE metric that reflects the accuracy of the fit.
Why do you predict 2.3?
this lecture is not up to the level as compared to other lectures @sreeni please explain with more examples when you write the code because at some point in time it feels like what is the use of this code and why it is written.
It is unfortunate that you do not find the explanation to be clear. Any suggestions on what is missing so I can fix it the next time I cover this topic?
@@DigitalSreeni after watching the 6th time this lecture now I understand the whole concept which is covered in this lecture.