Principal Component Analysis (PCA)
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- čas přidán 27. 06. 2024
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A conceptual description of principal component analysis, including:
- variance and covariance
- eigenvectors and eigenvalues
- applications
As usual, very little formulas, lots and lots of pictures!
0:00 Introduction
0:46 Taking a picture
1:13 Dimensionality Reduction
2:02 Housing Data
5:09 Mean
7:46 Variance?
12:47 Covariance matrix
13:58 Linear Transformations
18:12 Eigenstuff
19:16 Eigenvalues
19:53 Eigenvectors
20:51 Principal Component Analysis (PCA)
26:05 Thank you! - Věda a technologie
I haven't seen any clearer explanation of PCA on the internet than this one. Many thanks!
I agree!
I second this - great channel!
agreeeeeeeeee
Completely Agree!
+1
Judging by how much you simplified the lesson without sacrificing the actual concept, I trust you have spent good time preparing for your lesson and animation. Thank you very much
I haven't seen a better explanation for PCA. Love it! And I always had trouble to conceptually understand covariance. I knew it was related to direction but always found it difficult to conceptualize. Thank you for your explanation.
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Excellent video. Very intuitive. Thanks
hi
we may be aware of terms like mean, variance, covariance, linear-transforms, eigenvectors and eigenvalues, PCA. This video puts together all the relationships in an amazing way
I have confusion even after watching videos of 2+ hours...
26 minutes solved all doubts.
I like the way how you put all the concepts together.
Thanks.
lol this cracks me up
You are the tutor we all need but don't deserve.
Quite literally the best video on PCA. You explained everything without skipping the math and the basics, amazing!
This is without a doubt the best explanation on youtube.
That arguably might be the best tutorial I've ever watched on CZcams. Thanks.
This was AMAZING!!! You reconciled so many disparate concepts I had learned in college...I understand how it fits together now. Very down-to-earth explanation, and amazing animations!
I am yet to see a clearer explanation of PCA in my life! Thank you so much!
I've watched so many videos, read through so many websites, and asked so many questions to chatgpt about pca. I have to learn it as quick as possible for a research program. This is by far the best explanation I've seen!!
Great explanation Luis! It took me a whole class and a conversation after with an instructor to get PCA, and still your video cleared up so much uncertainty/misconceptions I had on the subject.
This is the best PCA video ever seen, it's quite easy to follow and understand deeply.Thanks!
I would literally clap for you if you gave this lecture in person. Thanks for this man. Keep doing what u do
Well Luis, that's the best and most intuitive PCA tutorial I've seen so far.
Bravo!
This is the clearest explanation of PCA I've ever seen. As a wannabe analytical chemist, this is pure gold because it gives you hints about the true meaning of PCA without excessively digging into statistics and pure mathematics which just blows your mind.
Congrats for the amazing explanation and the effort to make this video!
far better PCA explanation on youtube, I saw a lot of videos, but only learned with this one.
have never seen a better explanation of pca than this
I watched many videos on PCA over the last year and by far this is the best. Thank you.
wow! this was VERY worthwhile. thank you! so many lights switched on
I am impresed with the level of information you were able to put together in a 20 minutes video. I mean you summarized a whole semester in such a way that a beginner could follow. Thats amazing
PCA, that I couldn’t really understand in five years (although I have been using in analysis without being clear), I am now crystal clear. There are so many articles and videos. Those are like a parroting. This lesson is the best.
One of the most lucid presentations on PCA I've ever seen. Nice work!
well thats the best explanation i have ever seen, great job sir, thank you so much !
Thank you for taking time to explain small detail
Dear Sir, you are the best at explaining PCA!. I thank you very much.
By far the best conceptual and visual explanation online. Great work. Thanks!
Most comprehensive explanation, thank you so much. Tomorrow, I have interview for big tech company and now after watching whole video, I feel confident to answer questions on PCA. \m/
Thank you! I hope your interview went great!
Awesome explanation for PCA beats any in youtube tilll date
EXACTLY!!!!
Agree, even the one from 3Blue1Brown
Just discovered it. Phenomenally perfect balance between intuition and quantitative explanation.
This is the best PCA explanation. If only I had this explanation 11 years ago...
HIT THE LIKE BUTTON IF YOU LOVE THIS VIDEO!!!
You did it! You nailed it!! You killed it!!!
Just when I was about to give up on this topic after watching a series on PCA, I ran into yours and BOOM!!! IT WAS THE BEST THING I DID!!!!
I am going to watch it again because you gave me an ideal explanation of the topic.
You started off with related background knowledge necessary for perfect understanding.
THANKS A LOT!
This excellent, Luis. My only thought to improve the video would be to pull the thread through using your original example of housing data. Good job, and thank you!
This is the best explanation of PCA, individual components broken down, emphasis on visualization instead of just the method and then all the components put together for the big picture. I cant thank you enough for the fantastic teaching style that clicks with me.
Best ever explanation on PCA. You put so much effort into this. Thanks!!
Recognizing your name from your Udacity lectures, I was thrilled to have stumbled upon your youtube channel today - really gentle, concise, intuitive explanation of a complex topic. Nice shout out to 3blue1brown - watching this reminded me of his videos and style (though his visualizations are insane :] ). One question: from the end of your video, it seems to me that eigenvalues can be thought of as measures of feature importance, but in your housing example, multiple similar features (size, #rooms, etc) become a new single representative feature. How do we know what the features are that we are left with after PCA in much higher dimensional examples? Say we reduce a 100 dimensional data set to 10 dimensions, how do we know what those 10 dimensions represent? Thanks again - looking forward to diving into your channel.
footfalcon thank you for the kind message! (And sorry for the late reply). The new features are combinations of previous ones, but in general they may not mean much. However, since you know what combinations they are, you can look for example at the biggest coefficients in the linear combination, and see if you find trends. For example, if the top feature has big coefficients for several features that are size related (in a house) and small coefficients for other features that are not related to size, you can associate that feature with size in general. But it’s more of an observation than a mathematical way to do it.
This is great tutorial.
Can you make a similar video about - Markov Chain Monte Carlo, since its too complicated to understand why are we doing what we do?
f
Same here, really would love a video on this topic
up
I second that
Only explanation which has core PCA explained in a very simple yet effective and useful manner.
His teaching skills are exemplary.
You have a very intuitive way of teaching things that's hard to infer when reading or watching lectures from other teachers.
Thanks! Very good explanation of how projections, eigenvectors and eigenvalues all come together in PCA analysis!
One of best explanation after going through lot of PCA videos. Now it will make sense to drill down further.
Thanks!
Awesome Luis! very informative graphics to understand the complex concepts. Great Job :)
Thank you for making this video!! PCA and linear algebra have never been so easy and understandable to beginners, but you’ve made things clear and logical!
One of the best descriptions of PCA I've seen. Bravo!
Never have I understood PCA in such a simplified and intuitive way. You're a magician! ❤️
The most informative but graspable explanation of PCA I have encountered online ever. Thanks so much!
Thank you! This is the most comprehensible and complete video about PCA I've found!
Best PCA explanation I have ever seen. Thank you so much Luis.
Best explanation available on CZcams for PCA!! Amazing work Luis :)
This is one of the best tutorials which I have ever seen. Being a teacher, I appreciate the way how difficult concepts are explained in a simple way. It is must-to-read video for all students who want to understand PCA.
Probably the best explanation of PCA. Mean, variance, covariance, correlation such a smooth explanation, great.
I have spent 3 days chasing eigenvectors and eigenvalues, and then I found you video. It puts it all together very nice, and mentally delivers it with ease - like a boss. I was very close to spending two weeks on Khan academy to understand what you delivered in 26 minutes. Thank you so much.
Just amazing. I have an exam and I have gone through so much material already but nothing could have summed PCA better than you did. Thank you!
I spent so many hours looking at other videos and content online and could not make sense of it. This video really helped me a lot. Especially in understanding the role of eigenvectors and eigenvalues. Thank you so much!
This is by far the best PCA video i have come across. Thank You!!
One of the best explanation to PCA on youtube , amazing video
This is how you teach!! I wish my professors from university could do even 1% of this... I am paying thousands of dollars for something I get for free on youtube!! Very good job!!
The best explanation of PCA on CZcams!! Thank you very much!
Best video i have even seen about PCA. Thank you so much sir.
You're a hero!
Great explanation!
Probably the best video kn PCA on CZcams.
This is the best explanation of PCA in the whole CZcams!Thanks so much!!
i search all the pca tutorial on youtube, and this is the best one.
This is my first comment ever in youtube ,I was searching for PCA videos and was unable to figure out how exactly PCA works,but this video gave me complete clarity on the topic....Thanks Luis Serrano!!!
Amazing. Perhaps the best PCA explanation video in the internet.
All these CZcams videos are great. There is a 100% mapping between this and all the AI stuff with real brain observations and computational neuroscience. This author is brilliant!
Thank you so much for this amazing explanation Luis!! This is the best and most intuitive explanation of PCA I've found on the internet! I was literally struggling to understand this concept for weeks! :')
By far the very best PCA explanation! Very clear and understandable for everyone!
Luis, I have seen so many videos for PCA but this one is the best that I came across. Really appreciate your work and thank you for making the video. It's really awesome.
This video is amazing! Many students in college are taught how to compute the eigenvector and eigenvalue but the college seldom explained its geometrical meaning, but you did this which helped me a lot! Thank you!
The best PCA explanation in CZcams! felicidades Luis!
You are a gifted lecturer. You know how to motivate the theory and provide that understanding of WHY we're taking all the computational steps. That is rare!
By far the best video on PCA. Explanation was top notch! Thank You!
This was by far the best explanation that I found on youtube. Keep up the good work man
This is the best video on PCA I have come across. Its so well explained
One of the best explanation I have ever come across. Thank you very much
I went over at-least 30+ videos + materials , articles from 2 weeks , At last you made it so clear . You are wonderful teacher 🙏
There are so many videos on the internet but none explained to this detailed and fundamental level as you did! Thank you sir!
One of the best explanations ever, PCA or no PCA!
Thank you!! Out of all the videos I watched on CZcams this one helped me understand the most about what PCA is, how it works, and when to use it.
Great explanation, focussing only on the necessary things and giving hints to look further if necessary.
Thank you for your efforts!
Simply awesome. PCA conceptually cannot be explained better than this.
Very comprehensively explained,cleared a lot of concepts.Bravo!
This is the best explanation for PCA. I have tried with so many videos, but this saves me. Thank you
This is one the simplest and most precise explanation of PCA. Thanks !!
The covariance explanation just struck me unguarded, it blew my mind. Thank you
Well done, Luis. The recap at the end brought it all together. Thank you for posting.
the best PCA video on the internet. i always struggled with eigen value and eigen vectors thanks man
By far the best basic explanation of PCA on the internet! Thank you so much, I did not think I could ever understand this without being an expert mathematician.
This is the best of all lectures about PCA! Thanks a lot.
You explain the concepts sooooo simply. Hats off! Funny how I saw this video and then took your course on Coursera and it led back to this video .
The best PCA Explanation I've ever seen
Great explanation! It's always easy to find these things with complicated math but then you lose the idea, your video was easy to follow and I felt like I actually understood these concepts :D Thank you!
This is an excellent explainer. Beautiful job. Thank you
The best and neatest explanation of PCA.. bravo, and you’ve earned a lifelong fan and follower!!
I cannot state how intuitive this video is! Even a layman can now understand this! Loved it, subscribed and looking for more!!
Luis, thank you so much for sharing your knowledge. I've seen countless videos on data science and this PCA explanation is one of (if not the) best ones out there! This is the first of your videos that I've seen, but I'm excited to find others now. Again, thank you!
The best PCA tutorial I have seen anywhere on youtube. Thanks.
This was fantastic. You made the concept so intuitive and easy to understand. Thank you.
Wow..One of the best explanation about PCA..The best part is that you explained clearly the purpose of PCA in simple way!!!Billion thanks.