3 weeks of classes in the university and you summarized everything in 12 min. In a way I finally could understand, ofc. Thank you for that.
@durgaprasadvadlamoodi12713 жыл бұрын
i agree
@wordsexplained75653 жыл бұрын
It's really incredible to think that out there, there are genius people sharing this type of content, and by genius, I mean someone like ritvikmath, that instead of being like the usual majority that hide their lack of knowledge behind a lot of blind nonsense formalism, gift his viewers with a deluge of knowledge like this lecture of today. We're really grateful for your work!
@ResilientFighter3 жыл бұрын
Go Ritvik!
@ankurdubey14412 жыл бұрын
This is one of the best explanation of collaborative filtering on internet. Thank you !
@legendary53205 күн бұрын
holy shit. an insane video, clear, concise, and to the point. Really appreciate this stellar explanation man
@thangtran1452 жыл бұрын
To future people, do not dislike this video. It's extremely helpful! Thank you.
@sabazainab15243 жыл бұрын
The best youtube channel found on the internet. You are so amazing, Sir. I have watched a few other videos of yours and just clicked the subscribe button. You teach very easily and effectively. Thank you so much.
@ritvikmath3 жыл бұрын
Wow, thanks!
@JohnAndrewsKatadhinco5 ай бұрын
An excellent and easy-to-understand explanation. Thanks for breaking it down and sharing some of the challenges with collaborative filtering!
@MsKakashi20122 жыл бұрын
Best person to explain data science concepts in the whole youtube imo
@mr.kkquanini1156 Жыл бұрын
I just l love the approach and your way of delivering, it has really helped me a lot. Thank you
@ResilientFighter Жыл бұрын
Very much needed. This is extremely used in the real world, but not really much teaching in undergraduate.
@borisdrogendijk1888 Жыл бұрын
I have noticed to be a bit late to the party with all of your videos, yet I still wanted to just let you know that you by far explain anything related to machine learning and data science out of all the guys i have stumbled upon, cheers mate!
@ritvikmath Жыл бұрын
Wow, thank you!
@Somish3 Жыл бұрын
This is such a brilliant explanation, I was already pulling my hair trying to understand this concept and you just saved me, thank you! 👍
@neotokyovidАй бұрын
BRILLIANTexplanation! THX!
@adebayoemmanuel91110 ай бұрын
I'm liked this video to Increase the collaborative filtering so that KZbin can recommend more of this video to me.
@mehdicharife233520 күн бұрын
As always, very concise and succinct explanation. Do you have other videos, or some recommendations that can help explain intuitively how matrix factorization fits into this?
@drupad-l4i14 күн бұрын
this was soo helpful , i was taking andrew ng courses on couresera but he didnt explain it as clearly as you were. thank you soo much.
@ritvikmath14 күн бұрын
Glad it was helpful!
@bankruptLucifer Жыл бұрын
Excellent explanation. Precise, clear and easy to follow. Thank you!
@denjand4 ай бұрын
Super easy to understand. Thank you so much for the great explanation!
@mbanta6 ай бұрын
very clear explanation that answered many questions I had from a lecture. Thanks.
@ritvikmath6 ай бұрын
Glad it was helpful!
@elkanaajowi9093Ай бұрын
I now understand why mathematics, in itself, is a field to be studied.
@haibhanu2 жыл бұрын
Super Explanation given to the concept. It really clarifies most of my doubts regarding the topic.. Thank You very much..
@lvmrjb3 жыл бұрын
Watched 2 of ur videos so far that explain the concepts extremely well for a class project I have to do :) Your teaching and content are excellent!!
@ritvikmath3 жыл бұрын
Great to hear!
@venkatnetha83823 жыл бұрын
For a 1200 long pages of question bank on real world scenarios to make you think like a data scientist. please visit: payhip.com/b/ndY6 You can download the sample pages so as to see the quality of the content.
@familienolte15016 ай бұрын
I really liked the explanations. But is this concept superior to other cluster analysis methods such as AHC using euclidean distance for example? I mean euclidean distance is easier to understand that the cosine similarity. And for what do I need the expected rating? Wouldn't it be enough to find the most similar person and look for the highest rated film of that person, which my used person has not watched yet?
For a 1200 long pages of question bank on real world scenarios to make you think like a data scientist. please visit: payhip.com/b/ndY6 You can download the sample pages so as to see the quality of the content.
@just_a_viewer53 ай бұрын
amazingly taught. thank you so much
@Phil-oy2mr3 жыл бұрын
A question on cosine distance- user 1 and 3 were quite opposite on our scale and had a similarly of 0.57, so nearly 0.6. This is not very close to 0, which would indicate a true polar opposite, right? Why were 2 users here not rated near 0? What case would be? Thanks!
@venkatnetha83823 жыл бұрын
For a 1200 long pages of question bank on real world scenarios to make you think like a data scientist. please visit: payhip.com/b/ndY6 You can download the sample pages so as to see the quality of the content.
@LucasElliot-k8p Жыл бұрын
Hello, I have a question please. To get the predicted rating of User 1 for Item 4 (r1,4) Why did you multiply the similaritiy of S12 to the rating given by user 2 and S13 to the rating given by user 3. What do we call that formula or did you come up with it? What's the explanation behind it please? Also, what if the only available rating for item 4 is just the rating of user 2, can we still predict the rating that user 1 will give? Thank you so much, this is a very great video tutorial.
@amipigeon2 жыл бұрын
Thanks for such a clear and concise explaination!
@dragolov3 ай бұрын
Bravo, Maestro!
@Katharina-xt5il3 ай бұрын
Great explanation!
@nataliaminnigalimova7582 Жыл бұрын
Thank you for an amazing, understandable tutorial!
@kat.simplelife Жыл бұрын
Thank you for the precise explanation! It helps me understanding the recommendation system better. I have one question, where I'm just wondering how does latent factor fit into this?
@omerahmaad12 минут бұрын
Very good content thanks
@pranjalmittal Жыл бұрын
Really love the explanation! The video aside, I couldn't help but wonder if collaborative filtering based recommender systems that suggest content to people based on preferences of people similar to them, is one of key reasons why ideological polarity in general population is increasing on issues (such as in politics, but also beyond), because people get classified into a cluster based on similar but not the same interests, and as they see more of content in that cluster, they become even more "similar" or associative to that cluster/group they were originally somewhat but less similar to, assuming consuming content influences and creates bias in people along the lines (vector direction) of the content they consume, which I intuitively think is a fair assumption.
@zixuanchen4506 Жыл бұрын
If U1 and U3 are polar opposite, instead of bring the score up by weighted average, can we double down if scores by U2 and U3 are far apart? something like change 0.99*2+0.57*5 into 0.99*2+0.43*1?
@lilianaaa984 ай бұрын
I really love your video, thank a lot
@stmasanti10 ай бұрын
Geniously explained
@gm49842 ай бұрын
Explained really well! Tyty :D
@karitaalmeida14292 жыл бұрын
Amazing explanation! Thank you very much.
@hallvardjohnsen83022 ай бұрын
Great video.
@ahmadhaziq33582 жыл бұрын
Wow this is very well explained
@sivaneshc1093 жыл бұрын
Amazing explanation!!! Thanks!
@ritvikmath3 жыл бұрын
Of course!
@eliastheod2 жыл бұрын
Excellent. Thank you very much!
@hameddadgour2 жыл бұрын
Great content! Thank you for sharing.
@mikeyt51386 Жыл бұрын
Great video. I just subscribed!
@shakedg29563 жыл бұрын
You are very talented in explaining!
@NDBGamingHD2 ай бұрын
great video!
@shubhampandilwar84483 жыл бұрын
Clearly explained. But I have some questions. Can we use users who liked(also unlike) and watched videos to recommend? How many times he has seen a particular video of a particular genre etc.(Generally, not just Netflix. ex - youtube)
@venkatnetha83823 жыл бұрын
For a 1200 long pages of question bank on real world scenarios to make you think like a data scientist. please visit: payhip.com/b/ndY6 You can download the sample pages so as to see the quality of the content.
@ruslanshchuchkin3 жыл бұрын
man what a great video, thanks a lot!
@ritvikmath3 жыл бұрын
Glad it helped!
@adamtran57472 жыл бұрын
love the content brother. Keep it coming.
@scody3112 ай бұрын
Fantastic!!!!
@rbedson89652 жыл бұрын
is this user based CF or item based CF, as i see the cosine is used for item based approach but again data of user is taken in user based approach. Please clear this picture for me i am new to this course
@marathonour2 жыл бұрын
I also didn't get this point, it's great video overall, but I'm working on recomendation system and trying to figure out how svd solves this problem and should I use mult-vae instead or try content-based recsys with word2vec embeddings
@MegaJohnwesly Жыл бұрын
Love this explanation Ritvik.. Thank you
@srikarsundram46335 ай бұрын
Do you have content on content based filtering??
@alexivanov88003 жыл бұрын
Excellent content! thanks!
@ritvikmath3 жыл бұрын
Glad you liked it!
@venkatnetha83823 жыл бұрын
For a 1200 long pages of question bank on real world scenarios to make you think like a data scientist. please visit: payhip.com/b/ndY6 You can download the sample pages so as to see the quality of the content.
@rmiliming2 жыл бұрын
excellently explained.
@ayeshaayub31312 жыл бұрын
Can you explain pearson correlation co.efficient similirity measures
@sabazainab15243 жыл бұрын
I wish you can add some content related to GANS as well.
@ramonsantiago45732 жыл бұрын
This is great, but doesn't seem to work well for spares datasets. The one thing you can do is when predicting the rating you should only divide by the sum of similarities that have ratings, otherwise your rating will be much smaller than it should be.
@abhishekchandrashukla3814 Жыл бұрын
Sir, great work.
@tony0731 Жыл бұрын
Well explained!
@ritvikmath Жыл бұрын
Thanks!
@k5555-b4f3 жыл бұрын
Awesome videos - concepts very clearly explained
@venkatnetha83823 жыл бұрын
For a 1200 long pages of question bank on real world scenarios to make you think like a data scientist. please visit: payhip.com/b/ndY6 You can download the sample pages so as to see the quality of the content.
@soner59172 жыл бұрын
Super nice explanation. Thank you :)
@AbhishekChandraShukla5 ай бұрын
Is this user-based or item-based collaborative filtering?
@leorousseau39592 жыл бұрын
Really great video
@xuxu40683 жыл бұрын
Perfect. Thank you.
@user-or7ji5hv8y3 жыл бұрын
So well explained.
@bastianlipka24062 жыл бұрын
love it !
@xxshogunflames3 жыл бұрын
Awesome vid, thank you!
@venkatnetha83823 жыл бұрын
For a 1200 long pages of question bank on real world scenarios to make you think like a data scientist. please visit: payhip.com/b/ndY6 You can download the sample pages so as to see the quality of the content.
@durgaprasadvadlamoodi12713 жыл бұрын
very nice video. thanks for this.
@MightyFrostDragon3 жыл бұрын
Thank you so much, it was super useful
@gholamrezadar3 ай бұрын
what if the similarities are negative??
@ellos983 жыл бұрын
incredible!!!
@venkatnetha83823 жыл бұрын
For a 1200 long pages of question bank on real world scenarios to make you think like a data scientist. please visit: payhip.com/b/ndY6 You can download the sample pages so as to see the quality of the content.
@iaaan12453 жыл бұрын
Bro this is awesome stuff
@coldwinter18843 жыл бұрын
What order would you recommend me for watching your playlists?
@venkatnetha83823 жыл бұрын
For a 1200 long pages of question bank on real world scenarios to make you think like a data scientist. please visit: payhip.com/b/ndY6 You can download the sample pages so as to see the quality of the content.
@user-or7ji5hv8y3 жыл бұрын
Has another method become more popular than collaborative filtering?
@trongnhantran3358 Жыл бұрын
Love it!!!
@sakshamverma31142 жыл бұрын
just love it.....
@ccuuttww3 жыл бұрын
what is next R-SVD?
@ccuuttww3 жыл бұрын
For alternative we can use NMF to fullfill those missing value
@venkatnetha83823 жыл бұрын
For a 1200 long pages of question bank on real world scenarios to make you think like a data scientist. please visit: payhip.com/b/ndY6 You can download the sample pages so as to see the quality of the content.
@levimungai18463 ай бұрын
please do a real project on this with actual code and explanation
@happyhippo3794 ай бұрын
👏
@roykumuda9 ай бұрын
There is clarity in your explanation. But , Is it possible for you to tune yourself into Indian accent than the american.
@lexihilton71362 жыл бұрын
You’re so handsome
@hughlysds54113 жыл бұрын
First to comment I guess
@matthewchunk36893 жыл бұрын
your time to shine brotha
@venkatnetha83823 жыл бұрын
For a 1200 long pages of question bank on real world scenarios to make you think like a data scientist. please visit: payhip.com/b/ndY6 You can download the sample pages so as to see the quality of the content.