I've never said this earlier but I think, As NV Sir was for JEE preparation of mine, Striver is in DSA, u r an absolute life saver in Data Science. Can't thank u enough, the way to make us visualise is awe inspiring.
Wow ! Different concept, ' Word of Wisdom ' , interesting. Thanku so much for new additional information. Initially, I decided to skip due to some new thing , but it is explained in a simple n beautiful manner by giving an example. Thanku so much to campus x channel.
I just wanted to take a moment to express my sincere gratitude for your incredible teaching. Your dedication, passion, and expertise make every lesson engaging and enlightening. You've not only imparted knowledge but also inspired us to think critically and strive for excellence. Thank you for your unwavering support and commitment to our success. Your impact on our education is truly invaluable!
Thanku so much for introducing new term 'Wisdom of crowd' . Wherever there is mentor or wonderful teacher like u , every student will really perform well. God bless u ! I was thinking what if there wouldn't have been campus x channel & teacher like u , Machine learning would have been difficult since noone taught this subject that deeply n thoroughly. I wouldn't have think about data science & machine learning as a wonderful , easy & interesting subject as ur amazing teaching style make it so.
After going through a lot of channels.. Recently got stuck at your OOPS video... And now referring all videos. Really... Very beautiful explanation of all topics...🙌 Thanks for this KT
22:18 I have a doubt, is the performance of the model dependent on the dataset? becasue I was wondering if we can find out which model works best then why use model that does not give right output most of the time instead we can have just top 3 or 4 models and pass it to the KNN
If we consider 6 to 7 models then each model will predict a particular set of test data correct as per its algorithm but will wont be able to perdict everything and if the test data keeps changing surely each algorithms performance wont be same so giving weights in a way helps to cover all the cases that a test set could give and in a way would give good performance on a consistent basis😀 Maybe this helps
Nitish, when I work with few datasets, I get some dounts. Few get clarified in the internet, but few needs assistance. If I join CampusX, where there be a provision where I can ask few questions and get relevant answers from you? Appreciate your response.
I've never said this earlier but I think, As NV Sir was for JEE preparation of mine, Striver is in DSA, u r an absolute life saver in Data Science.
Can't thank u enough, the way to make us visualise is awe inspiring.
jee alakh pandey
@@indrajitpatil7533 he couldn't make u crack jee advanced
Wow ! Different concept, ' Word of Wisdom ' , interesting. Thanku so much for new additional information. Initially, I decided to skip due to some new thing , but it is explained in a simple n beautiful manner by giving an example. Thanku so much to campus x channel.
I just wanted to take a moment to express my sincere gratitude for your incredible teaching. Your dedication, passion, and expertise make every lesson engaging and enlightening. You've not only imparted knowledge but also inspired us to think critically and strive for excellence. Thank you for your unwavering support and commitment to our success. Your impact on our education is truly invaluable!
Thanku so much for introducing new term 'Wisdom of crowd' . Wherever there is mentor or wonderful teacher like u , every student will really perform well. God bless u ! I was thinking what if there wouldn't have been campus x channel & teacher like u , Machine learning would have been difficult since noone taught this subject that deeply n thoroughly. I wouldn't have think about data science & machine learning as a wonderful , easy & interesting subject as ur amazing teaching style make it so.
After going through a lot of channels.. Recently got stuck at your OOPS video... And now referring all videos.
Really... Very beautiful explanation of all topics...🙌
Thanks for this KT
Super explanation
I am learning machine learning from various institutions but you explanation is very good
Dil se shukria bhai. You are the best teacher.
He can teach Machine Learning Even to a Baby !!!
True,
I am 17 year old and learning Machine learning from him.
He is really a great teacher
You are Greatest Sir in Data Science Filed and Your Teaching is Awesome Sir
I m doing cfa level 2 and believe me u r great and recommend to everyone who want improve concept. Thanks bro God bless u
Too good yaar..me jis coaching se data science study kr rha hu ek bhi teacher aapki level ka nhi h...
He is a nice teacher and teach things will❤
You put huge effort in each video. hats off to your teaching. Keep rocking..
Man, what should I say ! Your explanations are just awesome. Thanks a lot.
bhai kya explanation hai mind blowing.
Thanks for teaching and doing for us free at cost❤
can't have better explanation than this
where did you take dataset ..can you provide me the source
Best Explanation.Thank You Very Much for making this so easy.
Thank You Sir.
We can use same model with different parameters also right??
22:18 I have a doubt, is the performance of the model dependent on the dataset? becasue I was wondering if we can find out which model works best then why use model that does not give right output most of the time instead we can have just top 3 or 4 models and pass it to the KNN
If we consider 6 to 7 models then each model will predict a particular set of test data correct as per its algorithm but will wont be able to perdict everything and if the test data keeps changing surely each algorithms performance wont be same so giving weights in a way helps to cover all the cases that a test set could give and in a way would give good performance on a consistent basis😀 Maybe this helps
as always clearly explained. Thanks alot
Crystal clear ,amazing
Excellent more clear and effective
Great Explanation in simpler words.
what a teaching method sir. Kudos ...!!!!. godgifted skills of teaching..!!+
Intuitive and clear. Thanks!
explained in the best way
Thank you so much sir🙏🙏🙏
Amazing lecture Sir 😍♥
We are grateful to you 🙏thankyou so much.🙏🙏
Amazing teaching level..
what happens in case of even no. of base model?
Hello Sir ,
I have a doubt .
So the final estimator is never trained on the dataset ?? Its trained only on the responses of the models before it ?
great video
Sir, The content is great. Could you please share your notes as well. which you have created
Amazing sir mind bluing thank you sir
Great explanation sir.Thank you so much.
nice vedio all thing is understood sir
Amazing explanation, but if i may say, can we have a video in all English for people who don't understand hindi? :)
Thank u so much sir for making explanation so easy
wonderful teaching sir
Great explanation sir, 🙏🙏
looking nice in this hair cut😎😎
awesome explanation, thanks a lot.
clear and deep explanation sir
Great sir👍
Thank you sir
Thank you
very useful! thanks a lot!!
Thank you so much.
finished watching
Awesome
Nice 👍
Thank you sir
You are magic
Sir time series ki series banao na plz
Gem video
Nitish, when I work with few datasets, I get some dounts. Few get clarified in the internet, but few needs assistance. If I join CampusX, where there be a provision where I can ask few questions and get relevant answers from you? Appreciate your response.
Yes. .. You can get answers
Can we say?
Bias Is a training Error
While Variance is a testing error
BEST BEST BEST
Use yellow color on black background
TopG
Plz update new video in Machine Learning 100 days , no videos after 62th day. See u soon.
Thx
Will start from Tuesday. Got little busy. Sorry about that
Nitish Sir >>>>>> Mukesh Ambani
No
ये जो पब्लिक है ये सब जानती है पब्लिक है
it's called ensemble not onsomble...your pronunciation is wrong
m.ruclips.net/video/3Y5KANETpjo/видео.html
Half knowledge can sometimes cause you embarrassment 😅
His pronunciation is right lil bro, go search for the pronunciation first yourself 😂
finished watching
Thank you sir