Chala clear ga explain chesaru bro Im in my PhD little bit confused with the statistical analysis! Chala videos chusanu utube lo but mee explaination tho I got clarity! Keep going with such amazing videos!! All best for you 👍
bro you said in some other video, that we have to make sure no-correlation exists before handover to machine learning model. But in regression correlation exists. What's the difference ?
Sir meeru cheppedhi data science gurinche kadha sir yendhukante nenu CSE lo data science maaku data science anedhi oka subject sir Data science subject lo model development gurinchi adhi indhena sir please reply sir....🙏🙏
Bro, Now i am in final year mechanical engineering, my HOD gives me a project i.e., support vector machine regression used to predict the dimensional features of electrical discharge machining, I didn't understand what to do and how to do please help me annayya
In 5mins 45 secs in top figure if you draw straight line from origin they we some points touching in the scattered plot. Can't we consider as that as linear regression
We can consider, but the main motive should be loss function should be minimum so linear regression consider chesinappudu there will be huge error term. Sp linear regression vadakunda undadam best
R2 Score : "(total variance explained by model) / total variance.” So if it is 100%, the two variables are perfectly correlated, i.e., with no variance at all.
Hii bro... Mitu explain chese vidhanam bagundii kani..ML Ante teliyani vallu chala Mandi vunnaru.. for python, python ante teliyani vallu python nee explain chesi and Dani medha ex. Programs anevi cheptharuu like Jupiter use chesi.. explain chesthe explain chesinatte vuntundii... Oka example question tisukoni malli Dani medha chepthe vere level vuntadii... So miru kuda simple linear regression explain chesi.. Dani medha oka example program lantidi cheyandii edhai Jupiter lanti platform medha.. it's better to understand.. I hope u understand what I'm telling.🙂😊
Bro squared error,mean squared error,root mean square error, absolute error paina oka video chey bro. Values near 1 unte em avutadi and near to 0 unte em avutadi ani.
Bro ne valana naku linear regression ippudu ardam ayindi.. Thank u👌
Chala clear ga explain chesaru bro Im in my PhD little bit confused with the statistical analysis! Chala videos chusanu utube lo but mee explaination tho I got clarity! Keep going with such amazing videos!! All best for you 👍
Very nice bro. even lay man can understand easily.
Video starts at 1:00
bro you said in some other video, that we have to make sure no-correlation exists before handover to machine learning model. But in regression correlation exists. What's the difference ?
Baga cheppv bro...I always encourage u
Ndhuku bro , nerchukoo ani antha neerasangam ga chepthav 🙆
🤣
😂😂
Listen in 1.25 .. then only it works
nice expalanation broo , thank you
Super explanation sir
Annaa...locally weighted linear regression video pettu
Sir meeru cheppedhi data science gurinche kadha sir yendhukante nenu CSE lo data science maaku data science anedhi oka subject sir Data science subject lo model development gurinchi adhi indhena sir please reply sir....🙏🙏
Yes, adi idhe
If any one Assumption fail ayithe yemi cheyali outliers data cleaning chesina tharvatha kuda assumptions fail ayithe yemi cheyali
Bro, Now i am in final year mechanical engineering, my HOD gives me a project i.e., support vector machine regression used to predict the dimensional features of electrical discharge machining, I didn't understand what to do and how to do please help me annayya
Very nice explination brother
Notes kavali bro
Channel name super pettav bro👌
Thank you bro ❤️
Thank you bro
In 5mins 45 secs in top figure if you draw straight line from origin they we some points touching in the scattered plot. Can't we consider as that as linear regression
We can consider, but the main motive should be loss function should be minimum so linear regression consider chesinappudu there will be huge error term. Sp linear regression vadakunda undadam best
R2 Score : "(total variance explained by model) / total variance.” So if it is 100%,
the two variables are perfectly correlated, i.e., with no variance at
all.
We can also say variance of the two features are equal
Hii bro... Mitu explain chese vidhanam bagundii kani..ML Ante teliyani vallu chala Mandi vunnaru.. for python, python ante teliyani vallu python nee explain chesi and Dani medha ex. Programs anevi cheptharuu like Jupiter use chesi.. explain chesthe explain chesinatte vuntundii... Oka example question tisukoni malli Dani medha chepthe vere level vuntadii... So miru kuda simple linear regression explain chesi.. Dani medha oka example program lantidi cheyandii edhai Jupiter lanti platform medha.. it's better to understand.. I hope u understand what I'm telling.🙂😊
Nenu next video chudalee broo... Sorry eppudu chusa next video... It's awesome broo...
How can I contact u
Bro squared error,mean squared error,root mean square error, absolute error paina oka video chey bro. Values near 1 unte em avutadi and near to 0 unte em avutadi ani.
We cannot say 1 daggara unte em avutadi ani because a values feature scale meeda kuda depend avvachu, 0 unte asalu error e lenattu
thanks.
Hi bro can I cal u regarding some classes
Sir continue ml course
How to calculate yi and yp
Yi value is already given, yp value is calculated by using hypothesis function (yp = b0 +b1x1)
👏👏
NICE SUPER EXCELLENT MOTIVATED
Thank you bro