Euclidean Manhattan and Cosine Distance | Euclidean distance vs Cosine similarity

[@yogalakshmiramesh8895]

Lovely explanation. Awesome!!!! thanks a lot

[@IRFANSAMS]

Your videos are super awesome for some one who is doing self study on ML

[@UnfoldDataScience]

Thanks alot, please share with others too.

[@sandeepchandra5033]

Very good Explanation sir.
We need more videos like this...!!!.
Thanks for your efforts & sharing knowledge 🙏
Note:- could you please explain if we 2 data sets csv files like train csv file 1459 rows and 81 columns and 1460 rows 80 columns after merged model implementation. supervised techniques and finally want ID column and salaried to csv how do work could please explain sir

[@UnfoldDataScience]

Thanks, will check your problem.

[@shubhamchoudhary5461]

Very good Explanation sir.
We need more videos like this...!!!.
Thanks for your efforts & sharing knowledge 🙏

[@UnfoldDataScience]

So nice of you Shubham.

[@soheilaahmadi4807]

amazing like always. Thank you so much

[@pramodyadav4422]

Every video is clearing the doubt.

[@UnfoldDataScience]

Thanks again Pramod. Your comments mean a lot.

[@pramodyadav4422]

@@UnfoldDataScience do you have any plan to teach online someone ? Like mini batch type ??

[@anilshinde8025]

Great. Nice explaination

[@suleymanerim2119]

Thanks, good clarificiation

[@UnfoldDataScience]

Glad it was helpful!

[@swethanandyala]

if it is Amans video first like and then watch!!!.... thank you for sharing your knowledge

[@UnfoldDataScience]

Thanks Swetha, means a lot

[@santoshr1708]

Thank you sir. This was really helpful. I have a request. Please a detailed video on Iris dataset using k - means clustering. It will help me as well as everyone a lot. I didn't find any good video on Iris dataset. So, it would be great if you make one on this.

[@UnfoldDataScience]

K means videos are already there, please see this complete playlist
th-cam.com/video/LCpihhKcJQs/w-d-xo.html

[@santoshr1708]

@@UnfoldDataScience Watched the video but I was saying to make a project video on iris dataset.

[@yaseraziz7275]

MR.Aman
can we implimen hamming distance as this
string1 = "pythonwwww"
string2 = "pytho"
cout = 0
if len(string1) != len(string2):
diff = abs(len(string1) - len(string2))
if len(string1) > len(string2):
string2 += diff * ' '
for i in range(len(string1)):
if string1[i] != string2[i]:
cout += 1
cout
to deal with not equl strings ???

[@dhlpaket2247]

You are amazing
Thank you

[@UnfoldDataScience]

Welcome.

[@RamanKumar-ss2ro]

Thanks for the video.

[@UnfoldDataScience]

Thanks a lot.

[@RanjitSingh-rq1qx]

Sir, you are to good. I had many doubts in my mind before watching this video. But now not anyone ☺️. And one more thing sir please arrange the white board little bigger 😊🥰😍

[@UnfoldDataScience]

Thanks Ranjit . Yes new digital board is in use now

[@61_shivangbhardwaj46]

Sir you r best
How easy explanation it is!
Thnx sir😊

[@UnfoldDataScience]

Thanks a lot.

[@ANSHULJAINist]

Amazing explanation.

[@prabhakarm5819]

simply & easy way understading all your concepts.Can you createNLP playlist also. So useful.Your way of teaching i feel so light. Super.

[@UnfoldDataScience]

Thanks a lot Prabhakar. Please find NLP playlist here:
th-cam.com/video/cs049uQWbpg/w-d-xo.html

[@sandipansarkar9211]

finished watching

[@rishigupta2342]

Good explanation!!!

[@UnfoldDataScience]

Thanks

[@radhakrishnanananthan1585]

Nice Video Aman Sir. Can you please share videos on SVD and PCA.

[@UnfoldDataScience]

As soon as possible. Thanks for suggestion.

[@balapranav5364]

Grower distance please

[@UnfoldDataScience]

Sure.

[@ajaykushwaha-je6mw]

Requesting you to kindly show the same using Python code.

[@UnfoldDataScience]

Will Do Ajay.

[@nikhilgupta4859]

Not impressed sir with this video, this video is nothing more than hundreds of videos available online.
We see your videos for something extra.
People are searching for the case where we decide which distance should be used based on data.

[@UnfoldDataScience]

Thanks Nikhil for the feedback. I will take care of it. Stay safe. tc

[@sandipansarkar9211]

finished watching