Perfect one! Without any theory explanation. Majority of us (i mean people who are looking for such an example) are familiar with theory but it is hard to find direct implementetion of it.
usually I don't comment even on good channels but your way of delivering the content is really good, the way I like: No repetition of words, maintaining a steady flow, no use of touch words, no use of filler words.(usually other UA-camrs do this trick to increase the length of the video for engaging the audience for longer time which build a habit of coming back to the channel for more content).
This explanation is very good. I have seen other videos too. Just one suggestion please dont use "particular" in every line that to twice or thrice. It sometime breaks the flow of listening and too irritating.
sir in another video u say that we have to consider value greater than threashlod value but in this video u are saying that we have to consider value less than threashold could u please give me clarity about this ASAP
In first example we calculated the distance, there we need to select the minimum distance In second example we have used similarity Matrix, hence we need to use highest similarity value
Perfect one! Without any theory explanation. Majority of us (i mean people who are looking for such an example) are familiar with theory but it is hard to find direct implementetion of it.
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usually I don't comment even on good channels but your way of delivering the content is really good, the way I like: No repetition of words, maintaining a steady flow, no use of touch words, no use of filler words.(usually other UA-camrs do this trick to increase the length of the video for engaging the audience for longer time which build a habit of coming back to the channel for more content).
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tomrw is my exam thank you sirr well explained my professor took hours to explain this 😄😄
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Nice slide and explanation. You just easily overview the topic within 11 minutes. Thanks ❤
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Great video with a nice example, detailed calculation and clear explanation!
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amazing channel with exceptional explanation. always look for your videos when im searching a topic on youtube
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Thank you very much!!! It is very clear and concise explanation
You are welcome!
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very good explanation and slides, Thanks
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Thank you very much, it's really interesting and understood easily for your exolanation about DBScan with the real case
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You are blowing my brain ❤❤❤ amazing explanation
Thank you so much
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You are the man bro, amazing job
Glad it helped
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Great Teaching.
Thank you
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Excellent explanation sir !!
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Can u explain graph how to draw it such as points
Thank you so much sir❤
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This explanation is very good. I have seen other videos too. Just one suggestion please dont use "particular" in every line that to twice or thrice. It sometime breaks the flow of listening and too irritating.
Ok
Excellent explanation, thank you so much!
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amazinggggggggggggggggggggg
thankyou sir
Well explained 👍
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If the minpts and e is not given what can we do sir. can we determine with heuristic method
sir could you please explain that......... how would you plot the graph points
from 0:10 ro 0:40 the points shown numerically here are just plotted on graph
@@skarthikeya5285 u cleared my doubt TQ
Tq
@@skarthikeya5285Tq
if two clusters merge together, do they become a single cluster or remain different clusters??
A small doubt, now the point 11 belongs to which cluster??
Super explanation sir
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Thank you sir
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Thank you a lot.
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Great explanation sir
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Sir, graph me centre points 2,5,11 or inke nearest points ki plotting randomly kri hai????
no, it is done according to the coordinates data given in the question. Like - P1:(3,7), P5:(7,3)
what to do if the distance is equal to epsilon?
ammamaa bhagwan ho mero lagi yo sir :)
excellent lesson, i will have an exam in a few days where I cannot code, where can I find similar practice questions?
Numerical examples..?
@@MaheshHuddar yes, both supervised and unsupervised, not in python
sir in another video u say that we have to consider value greater than threashlod value but in this video u are saying that we have to consider value less than threashold could u please give me clarity about this ASAP
In first example we calculated the distance, there we need to select the minimum distance
In second example we have used similarity Matrix, hence we need to use highest similarity value
But here point 2 and 11 belongs to two different clusters? how can we explain that?
can the clusters overlap ?
Sir ye slides aapkp kha sey milee, cause ye same slided hamari ma'am bhe copy ki hain. can you please tell me
❤
amazing sir🤩
Thanks a lot 😊
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thank you
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Thank you so much!
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Sir can we use Manhattan distance formula ?
Yes
You can use any distance metric
@@MaheshHuddar Thank You Sir For Your Quick Response !!
How to form clusters for points with more than two coordinates?
👍
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Can we use manhattan formula insted of euclidean
Yes you can use any distance metric
How the graph is pointed can anyone explain
First all given points (x-axis,y-axis) in the question were plotted on graph. Then clustered according to the core points
Please provide the pdf
CLARANS
👏👏👏
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may I have your file ppt of this video?
🙏🏼🫡
Thank you so much sir❤🙏
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Thank you
Thank you so much!
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