You have put lots of effort to create complete series of proximity measures...I'm able to understand proximity measures clearly only because of these series...thank you so much dear sir...
I watched all the 6 videos and i also want to know about mixted attibute type.sir, if you can please make a video quickly.🥺your teaching style is very good.🤗i like it.good luck sir.☺️
Thanks Isuri for your nice wording. On your demand, I made video on Mixed Attribute ruclips.net/video/aVUMDYPXLxM/видео.html . Please share your comment. Thanks again !!
Please follow data table in video. p1 values are 0 & 2 and p3 values are 3 and 1. Hence, d(p3,p1) = square_root [(3-0)2 + (1-2)2] = √10 = 3.2. Kindly let me know if you want more information. Please feel free to contact me. Your doubt might be very useful for others one. Keep watching. Thanks Saikrishna !!
Hi Sadath, if you see carefully p1 and p2 values are given like (0,2) and (2,0). You can map these value as (x1,y1) and (x2,y2). Hence x1=0, x2=2, y1=2 and y2=0. Now if you use given formula you will get 2.8. Let me if you do need more explanation on it. Thanks.
H i Sir, I have some doubt in this 1) why distance of all these 3 methods are different , if data are same so distance should be same 2) where these distance methods will be use in data science?
Answer 1: Euclidean distance formula can be used to calculate the distance between two data points in a plane. We use Manhattan Distance if we need to calculate the distance between two data points in a grid like path. Answer 2: The distance metric helps machine learning algorithms to recognise similarities between the contents. Many supervised and unsupervised machine algorithm based on distance measure metrics to know the input data pattern in order to make any similarity decision like face recognition, recommendation system. Kindly let me know if you need more clarification on it. Thanks Mukesh. Keep watching and learning.
Thank you for your kind comment and encouragement! Your support means a lot to me and I'm grateful to have you as a viewer. I'll do my best to continue creating content that you find helpful and enjoyable.
Thank you for your feedback. It was our initial time when we had started this channel. Now we have improved and correct camera problem in our latest videos, and still trying to improve. Kindly keep sharing your feedback to improve this channel. Keep Learning !!
You have put lots of effort to create complete series of proximity measures...I'm able to understand proximity measures clearly only because of these series...thank you so much dear sir...
Thank you. Respect from Afghanistan. 🙏🙏
Thank you Parwiz for all kind words. Good to know this Proximities tutorial video was useful for you. Keep Learning !!
you are better than my doctor, thank you so much
This Playlist is very helpful. Keep making such useful videos sir ! Thank you !
Happy to hear Jainam, this Proximity Measures videos Tutorial series helped you. Keep Learning and thank you for nice words!! @binodsumanacademy
Great work Binod Sir. this really helps.
Thank you Biju, happy to know this Proximity Measures Tutorial Video helped to learn. Keep Learning !!
Your explanation is great
Excellent Set of videos from your academy sir!....
Thanks a lot Sir, for sharing your knowledge and also your explanation is crystal clear on the topic
you are the best, this has helped me a lot thank you
Thank you Jay for good words. Happy to know this Proximity Tutorial videos helped you. Keep Learning !!
I watched all the 6 videos and i also want to know about mixted attibute type.sir, if you can please make a video quickly.🥺your teaching style is very good.🤗i like it.good luck sir.☺️
Thanks Isuri for your nice wording. On your demand, I made video on Mixed Attribute ruclips.net/video/aVUMDYPXLxM/видео.html . Please share your comment. Thanks again !!
Thank you sir.I’ll share it.🤗
Euclidean distance should be stated as sqrt((x2-x1)^2+(y2-y1)^2)
Great tutorial
Thanks a lot
Thank you Fahad !!
Thank you very much for the clear explanation sir
Thank you Ayesha !!
Nice sir good thank you so much ❤
How did you calculate d(p3, p1) in euclidean, Manhattan, supermum? What values should take while applying through formula?
Please follow data table in video. p1 values are 0 & 2 and p3 values are 3 and 1. Hence, d(p3,p1) = square_root [(3-0)2 + (1-2)2] = √10 = 3.2. Kindly let me know if you want more information. Please feel free to contact me. Your doubt might be very useful for others one. Keep watching. Thanks Saikrishna !!
Great Video 👍👍
You have the formula written x1-y1^2 for euclidean but you're doing x1-x2 + y1-y2
Hi Sadath, if you see carefully p1 and p2 values are given like (0,2) and (2,0). You can map these value as (x1,y1) and (x2,y2). Hence x1=0, x2=2, y1=2 and y2=0. Now if you use given formula you will get 2.8. Let me if you do need more explanation on it. Thanks.
@@binodsuman it needs more explanation if you please!
Thanks for such nice information..is it possible to make video's for Proximity Measures for Categorical, mixed attributes and cosine similarity??
Thank you. I will make video on your suggestion. 👍
Sir I am interested in categorical data please share your videos if now you have made in that direction. Thanking you in anticipation.
Thank you sir ❤
Good to know this Proximity Measures Tutorial video you liked. Keep Learning !!
Could you do it for mixed attributes and cosine similarity
Thanks Srikanth. Happy to know, this video useful for you. Sure, I will make video in mixed attribute and cosine.
H i Sir, I have some doubt in this 1) why distance of all these 3 methods are different , if data are same so distance should be same
2) where these distance methods will be use in data science?
Answer 1: Euclidean distance formula can be used to calculate the distance between two data points in a plane. We use Manhattan Distance if we need to calculate the distance between two data points in a grid like path.
Answer 2: The distance metric helps machine learning algorithms to recognise similarities between the contents. Many supervised and unsupervised machine algorithm based on distance measure metrics to know the input data pattern in order to make any similarity decision like face recognition, recommendation system.
Kindly let me know if you need more clarification on it. Thanks Mukesh. Keep watching and learning.
great tutorials
Thank you.
on supremum should p2p3 be max ( |2 -0|, |3-1|) == max (2,2) with answer 2 ? or am I missing something ?
Thank you 👌🏼
kya yeh dissimilarity distance hain kya
Hats off sir
Thank you for your kind comment and encouragement! Your support means a lot to me and I'm grateful to have you as a viewer. I'll do my best to continue creating content that you find helpful and enjoyable.
sir,don't have any video for attribute mixed type
I will upload video on mixed attribute soon. Thanks Isuri !!
thank uuu sir !!
thanks sir!
Thank you Karan !!
Thanku sir
thanks~
Finally found indian Binod(sir)😉
Thank you Shanid 😀
Happy to see this Proximity Measures Tutorial video helped you. Keep Learning !!
Binod
Sir, please develop some self confidence, you have mistaken x1,x2 as x1,y1.
worst camera man in the history.
Thank you for your feedback. It was our initial time when we had started this channel. Now we have improved and correct camera problem in our latest videos, and still trying to improve. Kindly keep sharing your feedback to improve this channel. Keep Learning !!
Thank you👍🏾