▶ Watch Entire Pandas Playlist ✅ Subscribe To My RUclips Channel: bit.ly/3wDKct8 bit.ly/2IGzvOR ▶ See More At: ✅ Join My Facebook Group: Codemy.com bit.ly/2GFmOBz ▶ Learn to Code at Codemy.com ✅ Buy a Codemy T-Shirt! Take 50% off with coupon code: youtube50 bit.ly/2VC9WUN ▶ Get The Code: bit.ly/3PXPJBt
This is INCREDIBLE!!! I've been reading so many Python tutorials on this and gotten nowhere in understanding. These 3 videos made sense of over 25 hours of reading tutorials. Thank you. I have subscribed and bought the lifetime membership. You rock!!
I watched videos 27 and 28 in this series to see how training/testing works together with linear regression. My online program sometimes requires us to learn certain concepts on our own, so I'm grateful for these. You explained everything clearly, and I was able to apply this to my own computer performance dataset, which is not as pretty, but now I know why! 😀
Hi, thanks for the great tutorial! I was a little confused by your explanation of the intercept_ for LinearRegression at 11:18. How does the sign of the intercept tell you whether the target increases or decreases as the features increase? Wouldn't this information be discerned from the sign of the slope rather than the intercept?
Thank you for detailed tutorial👏. I have a doubt, How can we increase our accuracy from 45 to some higher value? Let's say 60-70%.. How can we do that? If possible please make a video on it. Thank you.
You don't. The data is what the data is. If you run an experiment and get a certain outcome...that's the outcome. You don't change the experiment to get an outcome that you like more...
stupid question (bc I can ask that) - visually the model looks good, but why would R2 come at 45%, that percentage doesn't sound too good for a model. What am I missing? In my mind north of 60% is good...
▶ Watch Entire Pandas Playlist ✅ Subscribe To My RUclips Channel:
bit.ly/3wDKct8 bit.ly/2IGzvOR
▶ See More At: ✅ Join My Facebook Group:
Codemy.com bit.ly/2GFmOBz
▶ Learn to Code at Codemy.com ✅ Buy a Codemy T-Shirt!
Take 50% off with coupon code: youtube50 bit.ly/2VC9WUN
▶ Get The Code:
bit.ly/3PXPJBt
This is INCREDIBLE!!! I've been reading so many Python tutorials on this and gotten nowhere in understanding. These 3 videos made sense of over 25 hours of reading tutorials. Thank you. I have subscribed and bought the lifetime membership. You rock!!
I watched videos 27 and 28 in this series to see how training/testing works together with linear regression. My online program sometimes requires us to learn certain concepts on our own, so I'm grateful for these. You explained everything clearly, and I was able to apply this to my own computer performance dataset, which is not as pretty, but now I know why! 😀
Happy to hear it!
Excellent series! I was able to brush up on my knowledge of Pandas and Linear Regression. Thank you for providing such helpful content.
Glad you enjoyed it!
this is taught so well! You should really do more machine learning videos
Just started a new Machine Learning Monday playlist yesterday.
@@Codemycom will you teach it in python too? I see you have started with R
@@ErenMC_ of course
I really learnt alot. thank you sir
It's my pleasure
Hi, thanks for the great tutorial! I was a little confused by your explanation of the intercept_ for LinearRegression at 11:18. How does the sign of the intercept tell you whether the target increases or decreases as the features increase? Wouldn't this information be discerned from the sign of the slope rather than the intercept?
Thanks for your videoo!!, is great, Greetings from Uruguay!
welcome!
it coming more content for models? , kind regrestion, classification and more ? Greetings !@@Codemycom
13:38 Why have we used y.min,y.max here and why have you written it two times?
Sir please do more videos on machine learning algorithms
We'll see :-) I'm doing a deep learning with pytorch playlist at the moment.
Thank you for detailed tutorial👏. I have a doubt, How can we increase our accuracy from 45 to some higher value? Let's say 60-70%.. How can we do that? If possible please make a video on it. Thank you.
You don't. The data is what the data is. If you run an experiment and get a certain outcome...that's the outcome. You don't change the experiment to get an outcome that you like more...
I have a question, the only way to make the model more accurate is to make the data for training more?
stupid question (bc I can ask that) - visually the model looks good, but why would R2 come at 45%, that percentage doesn't sound too good for a model. What am I missing? In my mind north of 60% is good...
👍
:-)
Sir, would you like to teach at LFC ? My professor kinda dumb...
Ha
Who are here after completing the entire series 🙋♂
🙋♂