Hey guys I hope you enjoyed the video! If you did please subscribe to the channel! Join our Data Science Discord Here: discord.com/invite/F7dxbvHUhg If you want to watch a full course on Machine Learning check out Datacamp: datacamp.pxf.io/XYD7Qg Want to solve Python data interview questions: stratascratch.com/?via=ryan I'm also open to freelance data projects. Hit me up at ryannolandata@gmail.com *Both Datacamp and Stratascratch are affiliate links.
Dude you just made the whole concept so easy to understand, i've been trying to understand exactly what was required of me for hours. Keep up the great work ❣❣❣❣❣❣
as someone who is new to AI/ML, maybe some more clear terminology defined would be helpful. A lot of resources call what you describe as 'Normalizing' as 'Scaling'. And what you call 'standardization' is referred to as 'Normalizing'. Just a little confusing but great video actually showing the difference between the 2.
Could you also explain how the choice of feature_range affects the output processing please? Trying to understand in which case it should be (0,5) and when it should be (0,10), and how you then interpret the output, for example? Also, I am wondering: you are applying scalers to the whole dataset, but what if you have a regression type task (predicting an actual number)? If you apply scalers to all columns then your targets also change
Very good video! I learned a lot. If I was to ask for more, it would be to fill in WHY normalize or standardized. You mention some about “getting your numbers in order.” Add to that there are reasons for visualization tools, comparison analysis, and whatever else. I have some ideas why, but I’m guessing as a Pandas user you have encountered many more. Thank you for sharing.
Hey guys I hope you enjoyed the video! If you did please subscribe to the channel!
Join our Data Science Discord Here: discord.com/invite/F7dxbvHUhg
If you want to watch a full course on Machine Learning check out Datacamp: datacamp.pxf.io/XYD7Qg
Want to solve Python data interview questions: stratascratch.com/?via=ryan
I'm also open to freelance data projects. Hit me up at ryannolandata@gmail.com
*Both Datacamp and Stratascratch are affiliate links.
underrated channel great video
Thanks
Dude you just made the whole concept so easy to understand, i've been trying to understand exactly what was required of me for hours. Keep up the great work ❣❣❣❣❣❣
you teach very well than other channels but i don't know why pepoles are not spend time on your channel really helpfull man
Thanks it’ll happen over time
thank you good bless you i think after all these video i'll understand so well the machine learning
No problem. Make sure to join our discord
learned a lot from this. excellent teaching🙌
as someone who is new to AI/ML, maybe some more clear terminology defined would be helpful. A lot of resources call what you describe as 'Normalizing' as 'Scaling'. And what you call 'standardization' is referred to as 'Normalizing'. Just a little confusing but great video actually showing the difference between the 2.
Should I do polynomial and/or log transformations before normalizing or after?
is there any back transformation taht needs to be done afterwards?
Helpful . Thank you so much
No problem
Would it make sense to do a kruskal-wallis significance test for scaled indices that have been scaled 0-1 with min-max? Thank you ❤
Actually just released that video a few weeks ago. Finishing up a stats playlist
Excellent brother !
Thank you!
Great video!
Thanks!
Thanks so much
Could you also explain how the choice of feature_range affects the output processing please? Trying to understand in which case it should be (0,5) and when it should be (0,10), and how you then interpret the output, for example? Also, I am wondering: you are applying scalers to the whole dataset, but what if you have a regression type task (predicting an actual number)? If you apply scalers to all columns then your targets also change
Very good video! I learned a lot. If I was to ask for more, it would be to fill in WHY normalize or standardized. You mention some about “getting your numbers in order.” Add to that there are reasons for visualization tools, comparison analysis, and whatever else. I have some ideas why, but I’m guessing as a Pandas user you have encountered many more.
Thank you for sharing.
No problem and I may make a statistics course video in the future. Just waiting on my job to apply more skills
👏👏👏
can you please post the jupyter notebook containing code , it will be very healpful
Will be on my website soon, I’m moving the code from the vids into articles
Is there an easy way to get the column names? I have almost 100.
df.loc[ : , [ ' Col1 ' , ' Col2 ' , ' ColN ' ]]
if from index use df.iloc[]