I almost never comment on youtube videos, but this is hands down one of the best videos I have seen on any topic ever! This is my first time watching your any of your videos. Thanks for what you do!👏👏👏
Thank you Anthony, I'm so glad you like the videos! And thank you so much for your really kind donation, I really appreciate it! Please let me know if there are any topics you'd like to see a video on, I'm always looking for suggestions. Have a wonderful day!
You are one of the best educators. I hope you read this message. I hope you understand the positive impact you are having on all of us around the world. Thank you.
Fantastic explanation and presentation. I just finished reading 49 pages of a textbook and got more out of this video in 16 minutes than from my textbook... thank you sir I truly appreciate it.
I watched a lot of videos explaining k-mean but none of them give me a great and easy way to understand what is meant as you did! thank you for this video
Thank you so much Luis! Your explanations have been so important for me to learn key concepts whilst in my DS/ML bootcamp. The way you present initial intuition of the algorithms without delving into any of the math and then slowly introducing the principles is brilliant! Other courses can't wait to throw all the math on you from the very beginning, getting you lost before you could ever start to learn. Your animations are also much appreciated!
Thanks a lot for the simple way that you have explained the concepts of K-means Clustering and Hierarchical Clustering. The K-means Clustering examples with pizza parlor locations such that each person is going to go the nearest to them and every pizza parlor should be located at the center of the houses that it serves is simply awesome. Also the example using dendogram for Hierarchical Clustering is so cool and easy to understand.
Thank you! Your video saves my final exam! I had struggled with hierarchical clustering for a pretty long time until I saw your amazing videos today! Big Thank you again!
Wow - better than my 2 hour long lecture on unsupervised learning but can one really visualise a dendogram with several dimensions and millions of records? it sounds insane
Great explanation with real time examples. Loved the clustering Applications (Recommendation) example which was the exact reason why I watched this video. Awesome!
Entraba buscando las diferencias entre un modelo y el otro pero... no solo entendí las diferencias sino que me di cuenta que no entendía ninguno de los dos. Mil gracias
Hey, thanks for the explanation. I had a doubt about feature scaling when clustering. In the first example for k-means, age and engagements are on different scales, so if you don't scale the data you're making the assumption that an age difference of a year has the same 'weight' as 1 engagement a week for k-means's sum of squares optimisation. Some sources recommend scaling or normalising your data before clustering and other don't. How does one make that choice of scaling the data before clustering? Is it a business/use case decision (what do you want to give weight to) or do you always leave the data as is (like in the example) or do you always scale the data (equal weightage) ?
I know you haven't covered Silhoutte scores in this video but I have a question. What to do if elbow method and silhoutte scores gives different number of clusters? Like elbow method suggests 3 clusters and silhoutte score suggests 2 clusters. Also, I love your way of explaining things. I have confused about hierarchical clustering but you made it so clear. Keep making these videos.
i dont usually comment here. but your explanation is so amazing. easy to understand. hope you doing well sir. If possible could you explan on Stochastic gradient decent and Gradient decent? and also content-based, collaboratave-based filter, clustering-based recommender system? thank you
Your explanations are just so easy to understand and brilliant..........
How you do your explanation is amazing. An earth-shattering simplification of the two methods delivered by a great cool voice!
I took a loan to pay for my college tuition to teach me this and what's worse is that they didn't teach it half as good as you. Thanks a ton, Luis!
Easily the clearest explanation of the two clustering concepts. Thank you!
I almost never comment on youtube videos, but this is hands down one of the best videos I have seen on any topic ever! This is my first time watching your any of your videos. Thanks for what you do!👏👏👏
Thank you Anthony, I'm so glad you like the videos! And thank you so much for your really kind donation, I really appreciate it! Please let me know if there are any topics you'd like to see a video on, I'm always looking for suggestions. Have a wonderful day!
Just want to leave a comment so that more people could learn from your amazing videos! Many thanks for the wonderful and fun creation!!!
You are one of the best educators. I hope you read this message. I hope you understand the positive impact you are having on all of us around the world. Thank you.
Thank you so much for your kind message. :) I love teaching these concepts, and it's very rewarding to hear that you're enjoying the videos. :)
I can't believe how simple and easy to understand you made clustering. Thank you Luis!
This is by far the best explanation of the elbow method I've seen. Thank you!
Fantastic explanation and presentation. I just finished reading 49 pages of a textbook and got more out of this video in 16 minutes than from my textbook... thank you sir I truly appreciate it.
I get this video through UA-cam searching. Your explanation is better than the others. thanks for your contribution.
Luis's explanation is outstanding and he is really master in simplifying the concepts ! Thanks Luis ! .
The explanation is so good
Great explanation, really helped my understanding of hierarchical clustering and dendrograms!
I watched a lot of videos explaining k-mean but none of them give me a great and easy way to understand what is meant as you did! thank you for this video
one of the best explanation on k- means and hierarchial clustering
Underrated channel, great explanation.
I'm taking a course in data science by hk university and I didn't new what the points of clustering but now I know, you got a new subscriber.
I've studied the topic for a minute. This is the best explanation/analogy I have ever come across. Subscribed!
Luis, I have been viewing multiple videos to find someone who could explain these concepts with clarity and you nailed it! Thakn you
Thank you for the most clear explanation I’ve heard so far.
Your explanation was just out of this world
The best explanation of clustering which I have seen!
You make dreadful theories amazingly simple! Thank you very much for the great explanations AND the super-cool animations!!! Keep up!
Thank you so much Luis! Your explanations have been so important for me to learn key concepts whilst in my DS/ML bootcamp. The way you present initial intuition of the algorithms without delving into any of the math and then slowly introducing the principles is brilliant! Other courses can't wait to throw all the math on you from the very beginning, getting you lost before you could ever start to learn. Your animations are also much appreciated!
Great simplification in explaining the clustering principles!
Absolutely loved it, very resourceful, and has all the clarity that's needed regarding clustering algorithms.
The best explanation I've seen/heard so far!
Simple, concise and informative video. All educative videos should be like this.I have subscribed
I really like the way you teach these topics. thank you 🤩
Hope this type of video will be on UA-cam Trending someday. Thanks Luis
You're really good at explaining concepts
I always start watching your video with liking it. Thank you so much!
Very simple way to introduce a complex process, your video never let me down:).
Amazingly composed, a much simpler and understandable version, Hats off Luis, loved it, thanks.
Interesting video content. I am a newbie to the area of data mining but it gave me a clear insight about clustering. Cheers!
Amazing video , No one can beat your approach in explaining concepts
Thank you so much for making the video. Your explanation is very clear and easy to understand
You explained these theories by examples which are super clear! Thanks a lot! Keep updating more videos. Really helpful!
Thanks a lot for the simple way that you have explained the concepts of K-means Clustering and Hierarchical Clustering. The K-means Clustering examples with pizza parlor locations such that each person is going to go the nearest to them and every pizza parlor should be located at the center of the houses that it serves is simply awesome. Also the example using dendogram for Hierarchical Clustering is so cool and easy to understand.
Best video explanation I ever saw. Thank you Luis
Thanks so much for this truely great explanation. You make these topics feel so simple and easy by your excellent way of demonstration.
I am crying looking at the level of simplicity. Felt people waste 1000s of $ in Uni .. thanks
I don't have words to express my views. I can simply say Awesomeeeeeeee . Keep uploading for other techniques also.
This is how you teach! Basic theory followes by immediate example.
Excellent and simple explanation with good visual representations of K-means & Hierarchical models Luis!
Wow... it's amazingly simpler after watching your video... thank you so much Luis!
Dude, I love your lectures....Best in the world...
Brilliant video - such a clear and understandable explanation! Thank you so much :)
You are great! The way you simplify and the examples you give are so memorable- Thank you!
Best explanation so far. Thanky you sir.
I have watched a lot of videos about hierarchical, your video is epic! thanks...
I've been looking around trying to understand what they mean. You're video explains very well...
Thank you for making this video. This is so visual and it makes it so easy to understand.
This is the best explanation I have heard of any concept :)
Great explanation, thank you.
You are amazing lecturer Mr Luis.
Excellent .....It will help me to better explain the application of optimization (locator-allocator) theory to my colleagues in Public Health
One of the good visual way to explain K-means, Thanks Luis :)
Excellent Video! So easy to understand with the geographic example, thank you for the informative content.
Absolutely simplified explanation..thanks
Great explanations! Thank you. I’d love to see a video explaining the use of silhouette scores and plots for picking the best number of clusters.
Congratulations!!! Very easy to understand how clusters works!
Thanks for the help! The explanation was very clear and simple to understand
Thank you! Your video saves my final exam! I had struggled with hierarchical clustering for a pretty long time until I saw your amazing videos today! Big Thank you again!
Thank you. You have made this really easy to understand. Wonderfully helpful.
Awesome explanation. Very intuitive.
excellent way of explaining difficult concepts !!! Keep it up and thank you
Wow - better than my 2 hour long lecture on unsupervised learning but can one really visualise a dendogram with several dimensions and millions of records? it sounds insane
Excellent! Excellent!! Excellent!!! Explanation. Many Thanks!
I really appreciate your explanation of this topic. Thank you!
thank you for this explanation Luis
you are amaaaaaaaaaaaaaazing, i was really confused during the class
This is a very helpful video Luis. Thank you. (PS - This is also my very first UA-cam comment)
Very good explanation, can understand better now.
Great explanation with real time examples. Loved the clustering Applications (Recommendation) example which was the exact reason why I watched this video. Awesome!
Next Level Explanation.........................
Very informative! I am more prepared now for my data mining exam tomorrow! Thank you!
Sayyid Rasool thank you, best of luck on the exam!
Im such a visual person. and the pizza example helped so much
Entraba buscando las diferencias entre un modelo y el otro pero... no solo entendí las diferencias sino que me di cuenta que no entendía ninguno de los dos. Mil gracias
Hey, thanks for the explanation. I had a doubt about feature scaling when clustering. In the first example for k-means, age and engagements are on different scales, so if you don't scale the data you're making the assumption that an age difference of a year has the same 'weight' as 1 engagement a week for k-means's sum of squares optimisation. Some sources recommend scaling or normalising your data before clustering and other don't.
How does one make that choice of scaling the data before clustering? Is it a business/use case decision (what do you want to give weight to) or do you always leave the data as is (like in the example) or do you always scale the data (equal weightage) ?
Thanks for the very easy explanation.
Great Explanation and illustration
Awesome Video. Very helpful indeed Luis
YOURE JUST AMAZING SIR I WISH YOU FLOWERS AND JOY AND ETERNAL GLORY ! THANK YOU
Thank you so much for putting out this content! Really well explained. Much appreciated.
I know you haven't covered Silhoutte scores in this video but I have a question. What to do if elbow method and silhoutte scores gives different number of clusters? Like elbow method suggests 3 clusters and silhoutte score suggests 2 clusters.
Also, I love your way of explaining things. I have confused about hierarchical clustering but you made it so clear. Keep making these videos.
I love this explanation! Thank you!
Thank you for the application oriented explanation. :)
great content, clear explanation. Thank you!
Excellent explanation 👍🏻
Very helpful and nicely animated, thanks
Very good explanation. Thank you
If all my teachers were like you, I had a Nobel prize now!
Hi. Great thanks for the simple explanation of the concept.
another great course like everytime, thankiiies!
Amazing explanation Luis
Luis a Goodman on the earth!!
Thanks Mr. Serrano!!!
Thank you Luis!! Amazing explanation. Easy to understand.
i dont usually comment here. but your explanation is so amazing. easy to understand. hope you doing well sir.
If possible could you explan on Stochastic gradient decent and Gradient decent?
and also content-based, collaboratave-based filter, clustering-based recommender system? thank you