omg!!!! tomarrow is my paper Don’t have words to say thank you. I have taken oneneuron platform and I must say you and Sudhanshu sir are the best. Thank you so much for this initiative. ❤❤😇😇
Model 1- Overfitting Model 2- Underfitting Model 3- General fitting,,, THANKS SIR, FOR MAKING THIS PLAYLIST, hm ek institute se DATA SCIENCE ka course ker rhe hai (total charge 1.8 L), but hmko wha se utna samjh me nhi aaya hai jitna ki aapke video dekh ker samjh me aa rha hai... Thanks Sir
Don’t have words to say thank you. I have taken oneneuron platform and I must say you and Sudhanshu sir are the best. Thank you so much for this initiative. ❤️❤️😇😇
omg you explained it so good like i wish i had you as my professor in my class . You truly inspire me to learn and grow making machine learning so easy for me . Thank you
I wanted to thank you for your excellent explanation . Your teaching method made it so much easier for me to grasp the concept. I truly appreciate the effort you put into making the lessons engaging and understandable. I depend on you to transition my career into data science. 😍❣
First model is Low Bias + High Varience (Overfitting) Second model is High Bias + High Variance also (underfitting) Third model is Low Bias + Low Varaince. (Generalized)
Excercise 9:55 / 12:17 Model 1-> Low Bias, High Variance (Overfitting) Model 2-> High Bias, High Variance(Underfitting) Model 3 -> Low Bias , Low Variance (Generalized Model)
underfitting when training error is high, gives scenario of high bias. Bias is always realted to training data and variance is realted to test data. underfitting is when training error is high. Low bias when training data error is low. Low bias comes with low variance while high bias can come with both high and low variance. Low bias gives generalised model. OVerfitting problem occurs when best fit line passes through all the data points so the training error is zero. So this is a condition of low bias but high variance. So overfitting is associated with low bias and high variance because training error is zero but test data error will be high.
Addicted to all your videos, can't stop watching, I keep repeating all your videos, I can't my self believe that I am knowledgeable... iam not fan of any movie heros....but huge fan of you 👏
OMG!!! Was it that easy? I found this very difficult before watching your video. thnk u so much ....
Many videos I have seen on UA-cam. But your teaching is much understandable than others.
omg!!!! tomarrow is my paper Don’t have words to say thank you. I have taken oneneuron platform and I must say you and Sudhanshu sir are the best. Thank you so much for this initiative. ❤❤😇😇
Model 1- Overfitting
Model 2- Underfitting
Model 3- General fitting,,,
THANKS SIR, FOR MAKING THIS PLAYLIST, hm ek institute se DATA SCIENCE ka course ker rhe hai (total charge 1.8 L), but hmko wha se utna samjh me nhi aaya hai jitna ki aapke video dekh ker samjh me aa rha hai... Thanks Sir
Me 2.67 lac ka course kar raha hu wha nahi aya 😂
Don’t have words to say thank you. I have taken oneneuron platform and I must say you and Sudhanshu sir are the best. Thank you so much for this initiative. ❤️❤️😇😇
hum but the price is 😥
@@__________________________6910 you won’t find cheaper course than oneneuron.
omg you explained it so good like i wish i had you as my professor in my class . You truly inspire me to learn and grow making machine learning so easy for me . Thank you
Krish sir, Thank you very much for making this video. Love you sir. you are among the top great teachers of DS
.
Wow..thanks a lot sir ,please continue this series.
I wanted to thank you for your excellent explanation . Your teaching method made it so much easier for me to grasp the concept. I truly appreciate the effort you put into making the lessons engaging and understandable. I depend on you to transition my career into data science. 😍❣
You are the best teacher found 🙏🙏 Thankyou for such a simple explanation sir.
First model is Low Bias + High Varience (Overfitting)
Second model is High Bias + High Variance also (underfitting)
Third model is Low Bias + Low Varaince. (Generalized)
Truely no words to thanks mind-blowing, great ❤❤❤
Excercise 9:55 / 12:17
Model 1-> Low Bias, High Variance (Overfitting)
Model 2-> High Bias, High Variance(Underfitting)
Model 3 -> Low Bias , Low Variance (Generalized Model)
Sir, for this kind of content. Beautifully Explained
underfitting when training error is high, gives scenario of high bias. Bias is always realted to training data and variance is realted to test data. underfitting is when training error is high. Low bias when training data error is low. Low bias comes with low variance while high bias can come with both high and low variance. Low bias gives generalised model. OVerfitting problem occurs when best fit line passes through all the data points so the training error is zero. So this is a condition of low bias but high variance. So overfitting is associated with low bias and high variance because training error is zero but test data error will be high.
thank you so much ♥ ♥
Nice explanation bro thank you❤
very nice, bhot acha padhaya sir ne tysm
Best explanation, thanks alot
Addicted to all your videos, can't stop watching, I keep repeating all your videos, I can't my self believe that I am knowledgeable... iam not fan of any movie heros....but huge fan of you 👏
Thankyou for wonderful video sir🙏
best teacher
Please make a video on ML Career path. How to get into a job.
Still i am not able to understand how in the case of overfitting test error will be higher ?
Explained very well😁
it was so helpful
thank you sir❤
explained beautifully.
nice video sir
sir linear regression me jo best fit line to straight line bnti hai ... y = mx + b....to ye tedi line best fit kese ho skti hai?
Was finding this. ♥
Timestamp:9:55 >>>> Model1: Overfitting, Model2: Underfitting, Model3: Generalized model
Thanku so much
you made ml easy to love
Overfitting
Underfitting
Generalised model
Masterpiece
Sir muze samaz nahi araha hai ki linear regression me ovrfitting kaise ho sakta hai ... after all it is a line!
Sor, what if in regression model in the 3rd graph, if we have training error almost 0 and variance is also low ??
It is a generalized model
@@krishnaikhindi thanks sir
thanks sir
10:44 wrong underfitting (high bias + low variance)
Why low variance bro?? Test data error is high na so high variance..
11:42
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What language is he speaking?
Hindi
Hinglish