He is not insulting he just tell truth... in 1-2 hour lacture they read english terms and not show exact meaning they dont have batter teaching skills. but this youtube channel sir have.... @@bnnbnnn4517
Agreed with you.. I'm doing MCA in data science from Symbiosis International University.. and this explanation is way far better.. He is Mr. Bombastic ... Amazing explanation..
I must say "Sir' that your way of teaching is exactly same as mine or better.... you seriously don't waste even single minute of student by giving way too many examples or too many definations but clear the Funda behind everything in very well in least time... I watch your videos on 2x speed as your voice is also very clear and understandable.... really thanks a lot for these videos...
Through this tutorial video, I Understood SVM 1. SVM? - Using for Classification and Regression problems 2. Choosing the best classification line? - Have very large margin (D+ + D-) 3. Drawing Hyerplane through nearest Support Vector Point 4. The scenario for Linear base classification 5. The scenario for Non-Linear base classification 6. Non-Linear 1 dim data converted into 2 dim and if not then same converted into 3 dem through the kernel function until it is not been classified correctly Thanks a lot "5 minutes Engineering" and appreciated your effort
I have studied in class and watched number of videos to get clear understanding of SVM but couldn't find any , and I left SVM earlier but , in these 2 videos , I can only say that , "PERFECT" , what a way of clearing the concept in 7-8 min. Thanks a lot and more power to you.
Bhai i was literally roaming here and there to understand this concept and then i caught up this vedio and bravo u just did the thing its damn clear now thanks bro
Whenever u say to share your videos sir....i'm like nah i won't because this is the secret of mine to maintain the rank in my class. hahhahha.....bdw thank you so much sir for your brilliant explaination.
No wonder ,excellent explanation I really admire your videos ,you have in short explained the need of svm,the use of hyperplane and most importantly the way to classify the non linear data using Kernels.If possible could you please explain the mathematics behind the svm in your upcoming videos
Yes this is very good vedio and got good information from this vedio, but i need one help, there are many kernal functions available in SVM, but how to decide which kernel function to use ? kindly advise. Thanks
Can you explain , what actually the kernel function does ? What logic does it use to create the third feature ? Ex : if the features in 2D were F1 and F2 and say the kernel function computed the third function F3=F2**2 , then we can plot the same data points in 3D. So my question is : You talked about What inputs and output are for Kernel function , Can you please also explain what it does in the background.Thanks
Can you showcase a practical example by taking 1D and 2D (actual numerical data) feature space that how they are converted into 2D and 3D feature space. It's very complicated to understand by just seeing how it exactly getting converted. Thanks in advance.
Bro, you're 1000 times better than the over-educated professors.
Thanks
did you understand hindi george ?
Dont insult other professors man if you don't understand
He is not insulting he just tell truth... in 1-2 hour lacture they read english terms and not show exact meaning they dont have batter teaching skills. but this youtube channel sir have.... @@bnnbnnn4517
I am doing M.Tech from BITS Pilani and I tell you, you are 10 times better than the professor there.
😂😂👏👏
😀👍😀👍😀
😆😆😆
bhai aj kal khy kar rahe ho? kohi job lagi hai apki ya nhi?
Agreed with you.. I'm doing MCA in data science from Symbiosis International University.. and this explanation is way far better..
He is Mr. Bombastic ... Amazing explanation..
I must say "Sir' that your way of teaching is exactly same as mine or better.... you seriously don't waste even single minute of student by giving way too many examples or too many definations but clear the Funda behind everything in very well in least time... I watch your videos on 2x speed as your voice is also very clear and understandable.... really thanks a lot for these videos...
Bhai 1.75x pe hi dekha kr...dimaag ka bhosda hota hai 2x se
@@sudarshanmhaisdhune1039 baat toh sahi kahi hai waise... but yaar time thoda sa bach bhi jata hai on cost of mind
Through this tutorial video, I Understood SVM
1. SVM? - Using for Classification and Regression problems
2. Choosing the best classification line? - Have very large margin (D+ + D-)
3. Drawing Hyerplane through nearest Support Vector Point
4. The scenario for Linear base classification
5. The scenario for Non-Linear base classification
6. Non-Linear 1 dim data converted into 2 dim and if not then same converted into 3 dem through the kernel function until it is not been classified correctly
Thanks a lot "5 minutes Engineering" and appreciated your effort
Thanks to 5min engineering and you also...
Sir ,3D to 2D me convert kar sakte hai with kernel??
I gave my sessional and scored very low due to inefficient explanation of an IIT Madras professor, you cleared the whole picture. Thanks a lot!
with your videos ive scored such good marks in my bachelors thankyou so much!!!
Hlo mam ✌🏻
Despo@@mrnavi188
Gjb.. Kmal h yar.. Kernal ka concept aaj clear hua h achhe se.. Thanku bhai..
I have studied in class and watched number of videos to get clear understanding of SVM but couldn't find any , and I left SVM earlier but , in these 2 videos , I can only say that , "PERFECT" , what a way of clearing the concept in 7-8 min.
Thanks a lot and more power to you.
This man is legend, thank you for explaining such complicated concepts with such a ease 👏👏
Bhai i was literally roaming here and there to understand this concept and then i caught up this vedio and bravo u just did the thing its damn clear now thanks bro
Thank you Sir...Aapka hee videos dekh k apna concept clear kr rhe..THANK YOU VERY MUCH
your videos make me understood which could not happened with 2 hours video...thank you
There is no better way to explain this concept than the way u did.
Bohot aayege. Bohot jayege. Par app jaisa koi nhi aayega sir. You are the best.
I have watched a lot of videos, but you are the one who hitted all the concepts clearly.... 🎆🎆🎆
Today, I saw your video first time , you teaching style is very unique..
simply superb, everyone can understand easily
Ur teaching style is outstanding ,only point to point answering 🎉
Sir you are the best teacher 🥰I love the you explained everything thing in just a single line .... You are amazing... 😍🥰
I cannot tell you how perfectly you explain. I have watched max of your videos. I strongly recommend your videos
Prabhu aap ka padhane ka tarika 🙇
Excellent!
Too much knowledge without wasting any data!!
Kernel takes low-dimensional feature space and converts it into high-dimensional feature space
For a long time, I was waiting for such type of tutorial I found it here. Thanks and highly appreciated Brother.
5 minute engineering, start explaining these examples with data set also in just 5 minutes , phir dekhna pro students ho jayenge❤
bhai theory with practical really very necessary.only theory ....
Ur teaching is better than 10 books
Whenever u say to share your videos sir....i'm like nah i won't because this is the secret of mine to maintain the rank in my class. hahhahha.....bdw thank you so much sir for your brilliant explaination.
10000000000000000000000 better then a video of length 30 minutes :)
Awesome explanation sir. Love from IIT KGP.
Bro iss baar kernel word nhi btaya jese support vector me vector kya h btaya tha? Wese kernel samajh aa gya low to high dimension conversions👍
Happy teachers day sir❤😊 today is my exam and i will be able to attend the question because of this video, thank you ❤❤❤
6 Minute Video IN SHORT: Convert Your Data points in 2D to 3D plane using Kernel Trick..... BHAI THORA SHORT PRAHAY KRO , BAAKI GOOD HA
bro kiya bat ha itna easy way me esa concept maza agya
Aaj ka video vaakahi kamaal ka hone waala hai
You are amazing mann...you are real gem 💎
sir you are best💯💯💯💯💯you made it so easy to understand
bhai bhai kya banda h tu jabbr bhai keep going on bro.........
Thanks, Dear so much for such helpful information sharing in short time, really we all are support you from core of heart. Thanks once again
Best and simplest explaination of this concept...thanks alot sir
No wonder ,excellent explanation I really admire your videos ,you have in short explained the need of svm,the use of hyperplane and most importantly the way to classify the non linear data using Kernels.If possible could you please explain the mathematics behind the svm in your upcoming videos
What a great explanation sir.
Love and encourage from pakistan
You are better than my teacher :)
this guy is underrated
crystal clear explanation sir
A very difficult concept explained well!
Excellent sir ur idea is really easy to understand.Tq so much sir
THanks for clearer explanation
the most superb explained video sir,,,,FAB
🔥🔥🔥🔥🔥🔥🔥🔥🔥share kar diya bhai
Really Appreciate your efforts. Kindly Make a video in Gaussian kernel and linear kernel. My exam is on 20-5-2019
Bhai aap kon se exam ki bat kar rhae ho
Thank You so much, you made it very easy for me
Yes this is very good vedio and got good information from this vedio, but i need one help, there are many kernal functions available in SVM, but how to decide which kernel function to use ?
kindly advise.
Thanks
Great. Please try to include SVM cost function.
fantastic mja aa gya
Behtareen bro Behtreen, love from 🇵🇰
Super video! I applauded for CHF 2.00 👏
Bhot badhiya explaination!! got it in a single watch@@
Thanks this helped me lot only in 6.26 min
U r teaching is osm
make so so easy...
thank you sir,
hats's off sir,thanks for the knowledge
Awesome explaination sir😍😍😍
thanks a lot for this video!
Very nice . Thank you so much
Can you explain , what actually the kernel function does ? What logic does it use to create the third feature ?
Ex : if the features in 2D were F1 and F2 and say the kernel function computed the third function F3=F2**2 , then we can plot the same data points in 3D.
So my question is : You talked about What inputs and output are for Kernel function , Can you please also explain what it does in the background.Thanks
Kernel trick- Enables SVM to classify non-linear separable data by mapping it to a higher dimensional space.
Again, brilliant explanation. But How do we apply this on any data? Let’s say IRIS data.
yes if applied on any data-set like IRIS than it would be more beneficial. a kind request to 5 minutes engineering.
Agree
Please make videos on last 3 units of HCI...
Nice explained in a video... It would be good if you provide notes , materials also.
Simply awesome!!!!
Can you showcase a practical example by taking 1D and 2D (actual numerical data) feature space that how they are converted into 2D and 3D feature space. It's very complicated to understand by just seeing how it exactly getting converted. Thanks in advance.
Superb sir 🎉👌👌🎉👌👌
nice rocket 0:25
It is really helpfull thank you so much
love you , thank you , bless you
salute to u sir..easy explanation..
khub valo laglo
Amazing explanation
Superb easily understand
Sir app bhi jaye sandeep maheswari milne maja Aayega sir appko dekh kr
Plz make this type of videos of another cloud computing topic
Hiii
@@techbuddyrahul bhai yaha ni
gr8 ......................................
Thanks sir it's very helpful ❤❤
Also, please make some videos on Random Forest classification.
sir you are just awesome
nice explanation . thankyou
1:58 nice dance step😅
No words to say 🙏🙏🙏🙏🙏🙏
Awesome Sir... Thanks
Big fan sir 🙏🏻
Great videos . Please include coding too
Great tutorial!
Thanks for this video,it's really helpful
Excellent!
Thank you 🙏 sir🎉
Sir can you make a video on calculation of weight and bias in svm
Sir plz upload d video of all kernels
Good explanation bro👏
Good explanation. Had one request. Can you pls explain this concept considering a data set. And could you pls explain the math concept behind this.