7 days session Supermacy Krish sir. These are the best things on your channel, meaning in these live sessions we get whole review of the things and get to learn new things, which we hadn't during the first time we learnt elsewhere.
In Feature engineering ,please cover scaling ,encoding ,outlier treatment,missing value treatment ,feature selection and extraction in great detail not only basics ,please spend atelast 1 hour for each session
Great sessions. They have been really helpful, but kindly don't erase the content. While I understand how it works today by watching your videos, it's the notes that I'd refer to months down the line. And if the content is erased from the notes it will only create confusion.
I have completed this entire playlist, and yeah it was great. I have one question , is this it for ML . Obviously i am learning different concepts and terms as and when they come by reading detailed editorials on google and also i have to do the practical part. Can i consider ML to be done here ? Or do i need to follow some other materials.
Is the 7 day live session knowledge in ML enough for me, to move on to deep learning or do I have to learn more topics. And if I can learn DL, is there any place which covers all topics so that i dont need to search and in what order should I do.
hii krish sir , I have finished your statistics course recently Now I am following this seven days ml playlist, can you provide the materials of all 7 days because on website I am not able to see the material so can you ping pdf in comment section of all seven days or you can generated drive link too.it would be very helpful
Sir please take deep learning sessions instead of deep learning because many random people vote in community session even the comments from past videos requested deep learning
7 days session Supermacy Krish sir. These are the best things on your channel, meaning in these live sessions we get whole review of the things and get to learn new things, which we hadn't during the first time we learnt elsewhere.
hello Anwesh ! I just want to ask are these 7 videos enough for Classical ML ??
I have to give data science engineer technical test in few days
Best Teacher ever! Thank you so much for sharing these videos.
Thank you, sir. Finished Stats and Machine Learning. Now onto time-series and deep learning
please bring back mock interviews, they were very useful for us
In Feature engineering ,please cover scaling ,encoding ,outlier treatment,missing value treatment ,feature selection and extraction in great detail not only basics ,please spend atelast 1 hour for each session
Thank you Sir . Amazing session please continue ❤️
Can we get Unsupervised feature selection techniques, e.g. minimising number of features for a clustering algorithm!!
Want to write a book at some point? Im watching your intro to deep learning, you are so good at educating
Great sessions. They have been really helpful, but kindly don't erase the content. While I understand how it works today by watching your videos, it's the notes that I'd refer to months down the line. And if the content is erased from the notes it will only create confusion.
completed stats and ml playlist very informative thanks sir
I have completed this entire playlist, and yeah it was great. I have one question , is this it for ML . Obviously i am learning different concepts and terms as and when they come by reading detailed editorials on google and also i have to do the practical part. Can i consider ML to be done here ? Or do i need to follow some other materials.
It was awesome 🔥
Thank you for interview friendly content sir
Thank you so much.
Awesome sessions. Thank you.....
Thankyou sir for the very much informative session.
Thank You So Much Krish Sir....
finished watching
Great info
set speed 1.5x, Happy Learning👍
I had to do 0.75 X to understand 8t clearly. 😒😒😒
Thanks Krish. Can you make a video on how you use scrible ink software along with the setup.
Continue this seven day series sir
Amazing session Krish
Thank you sir for this amazing session
Please explain how it works in case if Multiclass classification
Thank you krish
Hello Krish Sir in Similarity Weight was it Sum of squares of weights or Square of total sum of weights.
Is the 7 day live session knowledge in ML enough for me, to move on to deep learning or do I have to learn more topics. And if I can learn DL, is there any place which covers all topics so that i dont need to search and in what order should I do.
I guess we need to take summation of squares for residuals instead of square of summation for the numerator of similarity weight.
Please cover light GBM,cat boost method as well also PCA,SVD,polynomial regression
for calculating the similarity weights in the denominator part why he is minus ???ie
0.5*(1-(-0.5)) + 0.5*(1-0.5) + 0.5*(1-0.5)
Hi sir, is possible to get notes that what you written in the session?. Lemme know how can i get it?. Thank you.
hii krish sir , I have finished your statistics course recently Now I am following this seven days ml playlist, can you provide the materials of all 7 days because on website I am not able to see the material so can you ping pdf in comment section of all seven days or you can generated drive link too.it would be very helpful
MEE TOO I AM ALSO UNABLE TO FIND DID YOU GET THE NOTES??
yes
on which playlist we can find fuzzy logic and genetic algo sir??
Hi Krish, when will next community session start and on what topic ?
@Krish What is he headphone model you're using??
sir can you please do a vedio on HTML file creation for prediction
Where can i get this document
Similarity weight formula doesn't look right..it should be (sum of all residual)^2. First bracket should be before summation sign in numerator..
36:28 what’s the predicted /output label given to DT2
wow
32:34,we take output nto similarity score I belive
Sir please take deep learning sessions instead of deep learning because many random people vote in community session even the comments from past videos requested deep learning
GG
Sir please do a live sessions on deep learning
σ(x) = 1/(1+e^-x)
why do you repeat one thing many, time, thats not a good way, it is very irritating, lol
That's not for scholarly person like you bro. 😀😀😀
Additional SVM blog : medium.com/sfu-cspmp/a-practical-guide-to-support-vector-machines-svm-ccd6a4d4dd04