NOTE 1: The StatQuest PCA Study Guide is available! app.gumroad.com/statquest NOTE 2: A lot of people ask about how, in 3-D, the 3rd PC can be perpendicular to both PC1 and PC2. Regardless of the number of dimensions, all principal components are perpendicular to each other. If that sounds insane, consider a 2-d graph, the x and y axes are perpendicular to each other. Now consider a 3-d graph, the x, y and z axes are all perpendicular to each other. Now consider a 4-d graph..... etc. NOTE 3: A lot of people ask about the covariance matrix. There are two ways to do PCA: 1) The old way, which applies eigen-decomposition to the covariance matrix and 2) The new way, which applies singular value decomposition to the raw data. This video describes the new way, which is preferred because, from a computational stand point, it is more stable. NOTE 4: A lot of people ask how fitting this line is different from Linear Regression. In Linear Regression we are trying to maintain a relationship between a value on the x-axis, and the value it would predict on the y-axis. In other words, the x-axis is used to predict values on the y-axis. This is why we use the vertical distance to measure error - because that tells us how far off our prediction is for the true value. In PCA, no such relationship exists, so we minimize the perpendicular distances between the data and the line. NOTE 5: A lot of people wonder why we divide the sums of the squares by n-1 instead of n. To be honest, in this context, you can probably use 'n' or 'n-1'. 'n-1' is traditionally used because it prevents us from underestimating the variance - in other words, it's related to how statistics are calculated. If you want to learn more, see: ruclips.net/video/vikkiwjQqfU/видео.html ruclips.net/video/SzZ6GpcfoQY/видео.html and ruclips.net/video/sHRBg6BhKjI/видео.html (the last video specifically addresses the 'n' vs 'n-1' thing, but the first two give background that you need to understand first). Support StatQuest by buying my books The StatQuest Illustrated Guide to Machine Learning, The StatQuest Illustrated Guide to Neural Networks and AI, or a Study Guide or Merch!!! statquest.org/statquest-store/
@@clydemccoy1768 There are two ways to do PCA. The original way was to apply eigendecomposition of the covariance matrix. However, that method is not numerically stable, so no one uses it anymore (even though it is still taught as if it is used). The preferred and more modern approach is to use Singular Value Decomposition (SVD), and this is what I cover in this video. At least, I cover the concepts behind how it works. However, I may dive into the math behind singular value decomposition one day. It's a long term goal of mine, but it won't happen for a while.
2:40 Actually 4-D data can be plotted easily. 4th dimension can be specified by color. Shades of gray would be sufficient. For example a very dark gray (i.e. low "intensity") indicates a small number.
Could you please clarify me the following: some sources mention that in addition to centering the data (subtraction of the corresponding mean), it must be also standarized, i.e., divided by their corresponding standard deviation. Why is the data not being normalized here?
For someone who is trying to be a data scientist, this channel is the best thing on the internet. You're better than any other teacher that I've ever had. THANK YOU.
Dr. Starmer - I really really wish I had you as my stats teacher during my student days. I can't put in words how much I appreciate your videos and how to go about explaining core concepts. Thank you very much!
I am a data scientist and have performed PCA using advanced statistical softwares . I have even taken company sponsored expensive MVA courses. THIS is the best explanation of PCA I have seen and cleared my fundamental doubts and missing links. THANK YOU.
uni student day 14 of self-quarantine: the first thing to do in the morning is to watch StatQuest. Thank you Josh. Your videos help me surviving thru my uni in the time of covid19
@@sahej97 Not everyone is a born genius like you some others need detailed explanation. You know Albert Einstein said if you cannot explain to a 6 year old you have not understood it yourself.
This is the most helpful video on PCA I have ever seen. I'm a neuro PhD student and I was struggling to understand this concept for a year. This video is a life saver!
I'm in my first semester of grad school for stats.. and you are single-handedly going to save me! I already knew of all of the concepts of eigenvalues/eigenvectors, loadings, etc. but you summarized the 60 pages of theorems/proofs my prof had us read, and helped my intuition immensely. Please never stop making videos!
I have forgotten how much I love maths. You are such a good teacher.❤❤. Instead of wasting time on shorts or tiktok, I am gonna watch this, learn something and improve my life instead. Thanks.
Thanks! I am a German high school student and we don't learn this in school. I stumbled across this video and have to say, I was able to follow everything and learned a lot. Keep up the great work!
Your work, with 3Blue1Brown, and other online material like Dr.Gilbert Strang, has given me a whole new perspective about Maths, learning and machine learning.
This is simply amazing! Josh, you communicated this idea so incredibly clearly and simply. As a practitioner in the stats/data science world I'm so impressed by how you were able to describe PCA so intuitively and without using the word "orthogonal." I've done the math behind PCA and I actually feel like I have a better understanding of it by watching your video. Thanks for demystifying stats for us all.
As a non-English speaker, I just like, I have no words, I don't no how to tell you how much I appreciate the way you put almost your "lyrics" or whatever you said on the videos that help me a lot. Many thanks to you, Josh!
I saw a lot of videos on RUclips on data science topics but never commented before. But I must appreciate it and thank you for clearing my doubts on PCA
I just took some other online course for hours only to struggle... this video helped me understand the concept, which that course couldn't! Even in way shorter time! And even with the minimum animation! Always grateful
As a current high-ranked uni student may say this channel is a lifesaver for people who cannot understand something without guided decomposition of relatively more complex algorithm than OLS. Thank you Josh.
Buddy you are an awesome guy. I wish one day I could give you a big hug of appreciation, but my honest thank you much has to be sufficient. I'd bet that there are people out there that would not be alive today without you. With your amount of views, there is probably at least one person who succeeded an important test that he would not have without you which may ended in a bad life. You're doing pure good with those videos.
The quality of this explanation is so much above than any other PCA class I've had. I am near to my midterm and the PCA class was about 100 slides full of formulas. You made my day!
I've been watching quite a lot of your videos for the past few days to study for my finals and I'm so glad your videos exist! I'd be very desperate without them. Thank you so much for the amazing work! You have a natural talent of expressing complicated ideas so simply!
This is the easiest way I understand PCA. I've been working with PCA algorithms for 2 years and two of my university professors provide lesson on PCA but couldn't make it clear. But, watching this 20 minutes video I understand in depth. Such a good lesson. Thank you so much!! ☺️ 😊
This is such a complex topic and with the visualization you made it so simple, you really are magical in explaining the concepts, a million thanks for this Josh
Congratulations! This is the best explanation of PCA without overcomplicating things with matrixes. In my opinion, everyone who explain PCA should start from an explanation like this... Thank you for the amazing video!
You really made me feel the mathematics in PCA....... earlier I can solve the math in PCA but now I know what's the purpose of each step in finding PCA components. I'm a B.tech student of final year. Thank you ❤️
I am a cloud architect taking an MIT ADSP course. If I see things are overwhelming there, I come and watch Statquest. What I like the best about Statquest is how you explain terminology alerts in simple layman's terms. Your channel is something I am more than happy to support. Indeed this is a great service.
Thank you very much! Opening my textbook, I was frightened by the Zs, phi, and "principal component directions" everywhere but as always your videos enable me to see the logic and fearlessly read the jargon :P
Hi Josh , I should say this that you make even the more complicated logic so simple for us to understand . For people like us who are in learning phase , that means a lot . Thank you very much .
JUST WOW!! Never I had thought that Stats could be this efficiently & easily understandable!! I feel immensely lucky that the content of this quality is freely available on the internet!! Thanx a lotttt for this magnificent effort from the whole team of StatQuest😇
Very impressive! I searched a lot of websites and you are the only one that make the conceptions clear. Your graphs and explanations are vivid and easy to understand.
Learning PCA was so daunting before watching this video. I have come back re-watching couple of times when every time I got myself stuck in reading other text books. The really amazing thing for me from your work is the development of intuitions on rather abstract concepts. I don't know about others but my limited brain just can not cope with things that are too abstract and lack of visual cues.... Thank you Mr. Starmer.
@@statquest I'm also following your recommendation, reading An Introduction to Statistical Learning, which as you said is a great book but not the easiest to read. Your videos help in great deal. Appreciate your knowledge sharing.
I took a whole seminar on this thing complete with a mini project and I didn't understand a single thing. Then comes along this little video and makes it all crystal clear in just 20 min! Hats off to you sir.
Wow, we should all just start studying solely with yt. There simply is no better way to understand complex stuff than with a nice video like thise one. Thx
Glad I found your channel this month! Looking forward in inhaling all of your content :D It's a very great addition to the very emotionless lectures of my prof..
I'm probably going to have to watch this video a couple more times to fully understand everything but this is really cool and very digestible. I was still able to understand the main concept from the first watch. Thank you Josh!
This video is really awesome !!! I wish I have a teacher like you ! Yours explanations are so clear ! Thank you very much for sharing your knowledge !!!
The quality of the content you provide is top-notch and is incomparable with some of the paid courses I've taken before. Thanks a lot for the effort you put into making these amazing videos. :)
Great job. I've rewatched this countless times today until it became as easy to explain to others as it was to understand ! You're truly a gifted teacher ! Keep up the great work ! Cheers from France !
Man, I'm only halfway through the video and I can tell this is a banger!!! Such a great video with the most vivid and clear explanations I have seen yet. You, sir, saved my semester.
Dude, you did not just explain eigenvectors! I have spent years bumping into this term, googling it, not really getting it, and moving on. That is the first explanation that ever made sense, and its not even what I came here for. Thank you so much.
After a long day of statistics overload, the cocktail recipe gave me giggles 🤣🤣🤣. Awesome content and am learning a lot everyday from your channel during the lockdown 🙏🏿!
Thank you sooooo much. Before I was struck with the amount of math involved and I couldn't understand why we were doing certain things. But this video cleared all my conceptual doubts. TYSM....
I watched this channel to help me study at university. I watched this channel to help me build my first algorithm at work. I watched this channel during my post-graduation studies. And now I’m watching this channel to get some help with my Master’s degree. Josh, you rock! (also, I took a picture with you when you visited Santander Brazil :) great presentation, btw)
BAM! Definitely one of the best PCA explanations I ever saw (especially in this short amount of time) Thanks for this you really helped me out. Could your future you make one more about the correspondence analysis (CA)?
I am a programmer, and apply mathematics within my code for 10 years already. But I know none of the terminology because I am self thought. So, I always feel like a fool watching videos on mathematics because they mix in terminology and go too fast over basic knowledge or terms I do not have. While ultimately I know a lot of the logics which come with it. Leaving me clueless, even with things I already mastered in practice. This video really helped me connect the already linked logics I have, with the knowledge of terms and mathematical notation I don't have. Thank you so much for making this.. It helps a lot being able to connect what I know out of experience with the fundamentals of the knowledge itself and helps me grow within my field of expertise.
If I were the Dean of the faculty of Statistics, I would ask you to advise statistics teachers. I would like you to remind them that there is a technique called "easy explanation".
Glad you like the video! The orientation of the data is different for different disciplines. In genetics, which is my field, the data is laid out like it is in the video. However, I agree that most people see data that is transposed (rows are samples, columns are variables).
Applying Practical principal for Creating Systematic layered Knowledge maps for New Innovations , and Development, from Concrete Proving Concepts or a Way of Consolidating Complex unrelated and Abstract Ideas Ontologically .This Third Approach Could lead to A more Dynamic way of learning and finding Real life Structural Solutions for Complex Engineering problems ,Ex Reducing or Reverse engineering biological systems , to enzymes, DNA , Transcription, Chemical systems to Physical and Quantum representation , Computer systems to Binary and Mathematical inductive Approach ,Ex.Algorithms and Data Matrix Structure, user interface
Awesome videos! Are you planning to make one of Gradient Boosting? It is the worst explained technique for some reason. It would be great to see a StatQuest video explaining it using a simple example; but in detail of course! : )
Fantastic! If you ever get around to doing a follow up could you elaborate on what can be done in cases where the PCA have roughly similar scree values? For example, I'm working with a data set where PCA 1 and 2 explain 61% of the variation with 6 different tests. Is there a data transformation step or better method for exploring this data? Also, when is it inappropriate to use PCA due to experimental design? As an example lets say I have two drugs; an antibiotic and an adjuvant. I test a set of different ratios of these two 8:14:12:11:1 and each ratio has different concentrations and I measure the density of bacteria at the end. Am I biasing a PCA as I know concentrations?
The good news is that you can't bias PCA because you know what the concentrations are. However, if you're not getting good separation, you should consider doing is using Linear Discriminant Analysis (LDA) when you know a lot about your samples (and already have a sense of how you would like them to cluster - in your case, you might want to identify the differences between the different ratios, so you can use LCA to cluster based on the ratios.). It can identify what variables make the biggest difference between the clusters. I have a video on LDA... ruclips.net/video/azXCzI57Yfc/видео.html
NOTE 1: The StatQuest PCA Study Guide is available! app.gumroad.com/statquest
NOTE 2: A lot of people ask about how, in 3-D, the 3rd PC can be perpendicular to both PC1 and PC2. Regardless of the number of dimensions, all principal components are perpendicular to each other. If that sounds insane, consider a 2-d graph, the x and y axes are perpendicular to each other. Now consider a 3-d graph, the x, y and z axes are all perpendicular to each other. Now consider a 4-d graph..... etc.
NOTE 3: A lot of people ask about the covariance matrix. There are two ways to do PCA: 1) The old way, which applies eigen-decomposition to the covariance matrix and 2) The new way, which applies singular value decomposition to the raw data. This video describes the new way, which is preferred because, from a computational stand point, it is more stable.
NOTE 4: A lot of people ask how fitting this line is different from Linear Regression. In Linear Regression we are trying to maintain a relationship between a value on the x-axis, and the value it would predict on the y-axis. In other words, the x-axis is used to predict values on the y-axis. This is why we use the vertical distance to measure error - because that tells us how far off our prediction is for the true value. In PCA, no such relationship exists, so we minimize the perpendicular distances between the data and the line.
NOTE 5: A lot of people wonder why we divide the sums of the squares by n-1 instead of n. To be honest, in this context, you can probably use 'n' or 'n-1'. 'n-1' is traditionally used because it prevents us from underestimating the variance - in other words, it's related to how statistics are calculated. If you want to learn more, see: ruclips.net/video/vikkiwjQqfU/видео.html ruclips.net/video/SzZ6GpcfoQY/видео.html and ruclips.net/video/sHRBg6BhKjI/видео.html (the last video specifically addresses the 'n' vs 'n-1' thing, but the first two give background that you need to understand first).
Support StatQuest by buying my books The StatQuest Illustrated Guide to Machine Learning, The StatQuest Illustrated Guide to Neural Networks and AI, or a Study Guide or Merch!!! statquest.org/statquest-store/
@@clydemccoy1768 If you can tell me how to draw a 5-dimensional graph on a 2-dimensional plane (the computer screen), then we have a deal.
@@clydemccoy1768 There are two ways to do PCA. The original way was to apply eigendecomposition of the covariance matrix. However, that method is not numerically stable, so no one uses it anymore (even though it is still taught as if it is used). The preferred and more modern approach is to use Singular Value Decomposition (SVD), and this is what I cover in this video. At least, I cover the concepts behind how it works. However, I may dive into the math behind singular value decomposition one day. It's a long term goal of mine, but it won't happen for a while.
@@statquest Thank you Josh :)
2:40 Actually 4-D data can be plotted easily. 4th dimension can be specified by color. Shades of gray would be sufficient. For example a very dark gray (i.e. low "intensity") indicates a small number.
Could you please clarify me the following: some sources mention that in addition to centering the data (subtraction of the corresponding mean), it must be also standarized, i.e., divided by their corresponding standard deviation. Why is the data not being normalized here?
For someone who is trying to be a data scientist, this channel is the best thing on the internet. You're better than any other teacher that I've ever had. THANK YOU.
Wow! Thank you very much!
Can't agree more! This is my second time viewing this PCA video!
@@statquest Safa is right !
@@statquest I agree entirely, very well explained.
Exactly, I still sometimes come back to this channel to refresh my memories!
Dr. Starmer - I really really wish I had you as my stats teacher during my student days. I can't put in words how much I appreciate your videos and how to go about explaining core concepts. Thank you very much!
Wow, thank you very much!!! I really appreciate your support! It means a lot to me that you care enough to contribute.
I am a data scientist and have performed PCA using advanced statistical softwares . I have even taken company sponsored expensive MVA courses. THIS is the best explanation of PCA I have seen and cleared my fundamental doubts and missing links. THANK YOU.
Hooray! I'm glad the video was helpful. :)
Can you please help me with the names of best software for PCA analysis!?
Hi kush i request you to guide me to become a data scientist like you
6 years into my phd and I finally understand how a PCA plot actually works. Thank you!
BAM! :)
Same!!!!!!
What research topic / thesis did you do sir @Dee T?
Where did you study? :|
little bam
uni student day 14 of self-quarantine: the first thing to do in the morning is to watch StatQuest.
Thank you Josh. Your videos help me surviving thru my uni in the time of covid19
Awesome!!!! I'm so glad to hear my videos are helpful and good luck with your courses.
This is honestly the best explanation on PCA. connects lot of loose strings of ideas. thanks !!
I'm so glad to hear that you like this video! :)
Then you haven't watched any other PCA videos
I agree 👍
@@sahej97 Not everyone is a born genius like you some others need detailed explanation. You know Albert Einstein said if you cannot explain to a 6 year old you have not understood it yourself.
Josh is brilliant.
I can say without a shadow of doubt that you are one of the world's best teachers. Mr. Starmer I can never thank you enough.
Hooray! Thank you very much!!! :)
This is the most helpful video on PCA I have ever seen. I'm a neuro PhD student and I was struggling to understand this concept for a year. This video is a life saver!
Thanks!
I'm studying for a final and this is way better explained than the entire semester.
Awesome! Good luck on your final. :)
Hi, what class is this you are preparing? Just curious
@@user-xn4yu5rn9qFor me it's Multivariate Data Analysis. Honestly, I got totally lost when just reading the lecture notes.
@@user-xn4yu5rn9q Exploratory Data Analysis quite similar to @irelia tt
@@ireliatt2190 ha! mine was Exploratory Data Analysis, which included Supervised/Unsupervised learning and multivariate data analysis
I'm in my first semester of grad school for stats.. and you are single-handedly going to save me! I already knew of all of the concepts of eigenvalues/eigenvectors, loadings, etc. but you summarized the 60 pages of theorems/proofs my prof had us read, and helped my intuition immensely. Please never stop making videos!
Thank you very much and good luck with graduate school. :)
I love how you explained "linear combination is just basically this... no big deal" when my lecturer makes it such a BIG DEAL! Thank you!!!
Hooray! I'm glad the video was helpful. :)
I have forgotten how much I love maths. You are such a good teacher.❤❤. Instead of wasting time on shorts or tiktok, I am gonna watch this, learn something and improve my life instead. Thanks.
bam!
I had to pause and let you know, that this is gold! The way you simply describe terrifying names, it just makes it look so easy. Thank you so much
Glad you liked it!
I have seen no book explaining this topic better than you. Your skills and efforts are invaluable!
Wow, thank you!
I honestly think you deserve a noble prize or something. You're amazing. Like, just amazing!
Thank you! :)
Thanks! I am a German high school student and we don't learn this in school. I stumbled across this video and have to say, I was able to follow everything and learned a lot. Keep up the great work!
Wow!!! Thank you so much for supporting StatQuest! BAM! :)
Your work, with 3Blue1Brown, and other online material like Dr.Gilbert Strang, has given me a whole new perspective about Maths, learning and machine learning.
Awesome! Those guys are my heros!
You are a real godsend. Teaching such advanced concepts in such a simple manner and that too in just 20 mins is exceptional
Thank you very much! :)
This is simply amazing! Josh, you communicated this idea so incredibly clearly and simply. As a practitioner in the stats/data science world I'm so impressed by how you were able to describe PCA so intuitively and without using the word "orthogonal." I've done the math behind PCA and I actually feel like I have a better understanding of it by watching your video. Thanks for demystifying stats for us all.
Thank you so much! I'm glad you picked up on my omission of the word "orthogonal". :)
As a non-English speaker, I just like, I have no words, I don't no how to tell you how much I appreciate the way you put almost your "lyrics" or whatever you said on the videos that help me a lot. Many thanks to you, Josh!
Thank you! :)
The true definition of genius: teach other a very complex concept in a very simple way.
Thanks!
You teach way better than so called professors who have phds post docs and years worth of teaching experience! Truly amazing!
Thank you!
I saw a lot of videos on RUclips on data science topics but never commented before. But I must appreciate it and thank you for clearing my doubts on PCA
Thank you!
I just took some other online course for hours only to struggle... this video helped me understand the concept, which that course couldn't! Even in way shorter time! And even with the minimum animation! Always grateful
Thank you very much!!! I'm glad to hear that the video was so effective, even though it's not very fancy. :)
As a current high-ranked uni student may say this channel is a lifesaver for people who cannot understand something without guided decomposition of relatively more complex algorithm than OLS. Thank you Josh.
Thanks!
Buddy you are an awesome guy. I wish one day I could give you a big hug of appreciation, but my honest thank you much has to be sufficient. I'd bet that there are people out there that would not be alive today without you. With your amount of views, there is probably at least one person who succeeded an important test that he would not have without you which may ended in a bad life. You're doing pure good with those videos.
Thank you very much! :)
The quality of this explanation is so much above than any other PCA class I've had. I am near to my midterm and the PCA class was about 100 slides full of formulas. You made my day!
Thanks and good luck!
I've been watching quite a lot of your videos for the past few days to study for my finals and I'm so glad your videos exist! I'd be very desperate without them. Thank you so much for the amazing work! You have a natural talent of expressing complicated ideas so simply!
Good luck with your finals and let me know how they go. :)
This is the easiest way I understand PCA. I've been working with PCA algorithms for 2 years and two of my university professors provide lesson on PCA but couldn't make it clear. But, watching this 20 minutes video I understand in depth. Such a good lesson. Thank you so much!! ☺️ 😊
Awesome!!! I'm glad the video was helpful. :)
This is such a complex topic and with the visualization you made it so simple, you really are magical in explaining the concepts, a million thanks for this Josh
Thank you!
Explained it so much better than a 2 hour lecture. The concept makes more sense now.
Thanks!
Congratulations! This is the best explanation of PCA without overcomplicating things with matrixes. In my opinion, everyone who explain PCA should start from an explanation like this... Thank you for the amazing video!
Wow, thanks!
You really made me feel the mathematics in PCA....... earlier I can solve the math in PCA but now I know what's the purpose of each step in finding PCA components.
I'm a B.tech student of final year.
Thank you ❤️
BAM! :)
This is the best PCA video ever, finally understand how PCs come out, Josh u r genius
Wow, thanks!
I am a cloud architect taking an MIT ADSP course. If I see things are overwhelming there, I come and watch Statquest. What I like the best about Statquest is how you explain terminology alerts in simple layman's terms. Your channel is something I am more than happy to support. Indeed this is a great service.
Thank you very much!
Thank you very much! Opening my textbook, I was frightened by the Zs, phi, and "principal component directions" everywhere but as always your videos enable me to see the logic and fearlessly read the jargon :P
Awesome! :)
one of best interpretation of PCA. It is like turning several math techniques into one idea.
Thank you! :)
Have a test on my Data Science course tomorrow, I was so lost with how to visualise what we are doing in every step and this video helped me so much!
Best of luck on the exam. Let me know how it goes. :)
It is unbelievable how Dr. Starmer makes such complex concepts so easily understandable!!! Thanks a lot!
Glad it was helpful!
*with sad voice* "Little bam..."
Awesome video. Thanks!
Little bam got me too 🤣
Hi Josh , I should say this that you make even the more complicated logic so simple for us to understand . For people like us who are in learning phase , that means a lot . Thank you very much .
Thank you! :)
Thanks dude. You are a godsend
@19:41 should be triple bam
BAM!!!!
Double BAM!
Bam is not sufficient!
Please never stop making videos. This is the only explanation of PCA I've seen that makes intuitive sense.
Thank you!
JUST WOW!! Never I had thought that Stats could be this efficiently & easily understandable!! I feel immensely lucky that the content of this quality is freely available on the internet!! Thanx a lotttt for this magnificent effort from the whole team of StatQuest😇
Glad it helped!
How ur calculating the parts that u said like 4 parts of gene 1 and 1 part of gene 2 please respond cannot understand
simply amazing. I just hope u never stop teaching us those 'complex' topics in such friendly and clear way.
That's the plan!
Very impressive! I searched a lot of websites and you are the only one that make the conceptions clear. Your graphs and explanations are vivid and easy to understand.
Thank you very much! :)
Learning PCA was so daunting before watching this video. I have come back re-watching couple of times when every time I got myself stuck in reading other text books. The really amazing thing for me from your work is the development of intuitions on rather abstract concepts. I don't know about others but my limited brain just can not cope with things that are too abstract and lack of visual cues.... Thank you Mr. Starmer.
I'm glad the video is helpful!
@@statquest I'm also following your recommendation, reading An Introduction to Statistical Learning, which as you said is a great book but not the easiest to read. Your videos help in great deal. Appreciate your knowledge sharing.
@@minweideng4595 BAM!!!
I took a whole seminar on this thing complete with a mini project and I didn't understand a single thing. Then comes along this little video and makes it all crystal clear in just 20 min! Hats off to you sir.
Hooray! :)
Wow, we should all just start studying solely with yt. There simply is no better way to understand complex stuff than with a nice video like thise one. Thx
Thank you! :)
No joke, this is seriously the best PCA explanation I've ever seen! You have a great talent of explaining things. Thank you for making videos!
Wow, thanks!
You have not seen Louis G Serano explanation do you ?
Simply this is the best explanation of PCA on entire RUclips platform, After this you don't need to get anywhere
Bam!
No words to tell how this video helped me! Thanks a lot, Josh! Your way to explain is extremely simple and makes anything easy for us.
Thank you very much! :)
Glad I found your channel this month! Looking forward in inhaling all of your content :D It's a very great addition to the very emotionless lectures of my prof..
Awesome, thank you! And thank you for your support!!!
you took 10 minutes to blow my mind with your amazing explanation. Thanks!
I pulled through many materials and wasted too much time..and this video explains everything in 20 min! thanks!
Bam! :)
Wow man thank you! Just had a moment of clarity, teachers like you are rare!
Hooray!!! I'm glad the video was helpful. :)
You saved my future!!! Oh my god, this is amazing! You are a talent who can transfer complex to simple.
Awesome! :)
I'm probably going to have to watch this video a couple more times to fully understand everything but this is really cool and very digestible.
I was still able to understand the main concept from the first watch.
Thank you Josh!
Awesome, thank you!
You are probably the best teacher of maths in the world. I salute you.
Thank you very much! :)
This video is really awesome !!!
I wish I have a teacher like you ! Yours explanations are so clear !
Thank you very much for sharing your knowledge !!!
The quality of the content you provide is top-notch and is incomparable with some of the paid courses I've taken before. Thanks a lot for the effort you put into making these amazing videos. :)
Much appreciated!
Man, this work of yours is pure gold. can't thank you enough
Thanks! :)
Great job. I've rewatched this countless times today until it became as easy to explain to others as it was to understand ! You're truly a gifted teacher ! Keep up the great work !
Cheers from France !
Merci!
just amazing, how you clearly put across so complex concepts with ease..!
Thanks! :)
The best PCA explanation ever! Plus a linear algebra bonus!!! Thanks a lot!
Thanks! :)
Man, I'm only halfway through the video and I can tell this is a banger!!! Such a great video with the most vivid and clear explanations I have seen yet. You, sir, saved my semester.
Glad it was helpful!
Dude, you did not just explain eigenvectors! I have spent years bumping into this term, googling it, not really getting it, and moving on. That is the first explanation that ever made sense, and its not even what I came here for. Thank you so much.
bam! :)
I really enjoyed StatQuest videos and I just bought StatQuest DOUBLE BAM T-shirt :))
HOORAY!!! BAM!!! :)
I lost it at "Little BAM!!!"
:)
Same😂
same here 😂
"Mathematicians call this recipe a linear combination". I would have called it a Gene tonic!
This might be the best comment ever. BAM! :)
Hi Josh, You are the gold standard that all teachers from all spheres should aspire to be.
bam! :)
This hidden gem channel is so great, yet I found it 1 year after my graduation :/
better late than never? :)
@@statquest true, thank you for sharing your knowledge! :D
After a long day of statistics overload, the cocktail recipe gave me giggles 🤣🤣🤣. Awesome content and am learning a lot everyday from your channel during the lockdown 🙏🏿!
Hooray!!! :)
Thank you sooooo much. Before I was struck with the amount of math involved and I couldn't understand why we were doing certain things. But this video cleared all my conceptual doubts. TYSM....
Hooray!!! I'm glad to hear the video is helpful.
You are the best teacher I came across in the internet, thanks
Glad you're enjoying the videos!
All i can say after watching the video is "Double BAM"😂
genius you are in the presentation of your knowledge...
Thank you!
Josh saying "BAM" is one of my favorite things in life
BAM! :)
Mine is Wha Wha..:)
I watched this channel to help me study at university.
I watched this channel to help me build my first algorithm at work.
I watched this channel during my post-graduation studies.
And now I’m watching this channel to get some help with my Master’s degree. Josh, you rock!
(also, I took a picture with you when you visited Santander Brazil :) great presentation, btw)
BAM! Thank you very much! Hopefully we can meet again in Brazil soon.
that 'little BAM' killed me
Awesome explanation!! Subscribed (hooray!)
Thank you very much, great video, it explains something very complex very clearly and simply. Thanks again.
Hooray!!! You're welcome! I'm glad you like the video. :)
BAM! Definitely one of the best PCA explanations I ever saw (especially in this short amount of time) Thanks for this you really helped me out.
Could your future you make one more about the correspondence analysis (CA)?
I'll keep that in mind.
Original "explain to me like I am 5 year old" guy. You are superb mate, thanks!
Thanks!
2:44 😭😭😭😭😭😭😭😭 this guy is outta hands casually said "womp womp" great lecture btw
:)
this cleared up some misunderstandings I was really struggling with :'] thank you!
Hooray! You're welcome. :)
Dear Josh. Thank you very much I would love to have a stats like this in my classes. The explanation was amazing. Subscribed.
Thank you! :)
I am a programmer, and apply mathematics within my code for 10 years already. But I know none of the terminology because I am self thought.
So, I always feel like a fool watching videos on mathematics because they mix in terminology and go too fast over basic knowledge or terms I do not have. While ultimately I know a lot of the logics which come with it. Leaving me clueless, even with things I already mastered in practice.
This video really helped me connect the already linked logics I have, with the knowledge of terms and mathematical notation I don't have.
Thank you so much for making this.. It helps a lot being able to connect what I know out of experience with the fundamentals of the knowledge itself and helps me grow within my field of expertise.
Thanks! I'm glad the video is helpful. :)
If I were the Dean of the faculty of Statistics, I would ask you to advise statistics teachers. I would like you to remind them that there is a technique called "easy explanation".
bam! :)
Well explained. However, it would be more intuitive if you 'transpose' the data set, i.e. make the Gene as the columns and Mouse as rows.
Glad you like the video! The orientation of the data is different for different disciplines. In genetics, which is my field, the data is laid out like it is in the video. However, I agree that most people see data that is transposed (rows are samples, columns are variables).
Great Video, Great Instructor … but i agree with you about 'Transpose' thing ...
Applying Practical principal for Creating Systematic layered Knowledge maps for New Innovations , and Development, from Concrete Proving Concepts or a Way of Consolidating Complex unrelated and Abstract Ideas Ontologically .This Third Approach Could lead to A more Dynamic way of learning and finding Real life Structural Solutions for Complex Engineering problems ,Ex Reducing or Reverse engineering biological systems , to enzymes, DNA , Transcription, Chemical systems to Physical and Quantum representation , Computer systems to Binary and Mathematical inductive Approach ,Ex.Algorithms and Data Matrix Structure, user interface
Awesome videos! Are you planning to make one of Gradient Boosting? It is the worst explained technique for some reason. It would be great to see a StatQuest video explaining it using a simple example; but in detail of course! : )
When I search for PCA on youtube and I saw that Statquest has a video for it, I was sure that I will learn PCA without any pain. Thank you!!
Bam!
You had me at "Wahh Wahh"
:)
Thanks for this brilliant tutorial... looking forward to more! 😋
Shared it with twitter since it goes into just the right level of detail and at a great pace: twitter.com/stornoli/status/988830814094942208
BAM
godly video!
Thank you! :)
The ability to just explain complexe idea is just amazing! Thank you !
Thanks!
ProTip: Play at 1.5 times the original speed
Fantastic! If you ever get around to doing a follow up could you elaborate on what can be done in cases where the PCA have roughly similar scree values? For example, I'm working with a data set where PCA 1 and 2 explain 61% of the variation with 6 different tests. Is there a data transformation step or better method for exploring this data? Also, when is it inappropriate to use PCA due to experimental design? As an example lets say I have two drugs; an antibiotic and an adjuvant. I test a set of different ratios of these two 8:1 4:1 2:1 1:1 and each ratio has different concentrations and I measure the density of bacteria at the end. Am I biasing a PCA as I know concentrations?
The good news is that you can't bias PCA because you know what the concentrations are. However, if you're not getting good separation, you should consider doing is using Linear Discriminant Analysis (LDA) when you know a lot about your samples (and already have a sense of how you would like them to cluster - in your case, you might want to identify the differences between the different ratios, so you can use LCA to cluster based on the ratios.). It can identify what variables make the biggest difference between the clusters. I have a video on LDA... ruclips.net/video/azXCzI57Yfc/видео.html
man was womp womping before womp womping was "cool"
:)
My jaw dropped after watching such a fantastic explanation. Truly amazing!!
The intuitive build-up and pacing are so good.
Wow, thank you!