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/ Special thanks to PROTIST for the Russian subtitles!!! :)
Hi Josh, Love your content. Has helped me to learn a lot & grow. You are doing an awesome work. Please continue to do so. Wanted to support you but unfortunately your Paypal link seems to be dysfunctional. Please update it.
I appreciated your effort spent on these videos. Sadly, since I am still a student, I have no money to support you just a bit. So, I have spent much of my effort to translate your videos into my language as it is my best language as a thank to you. Hope you would accept my thank.
@StatQuest with Josh Starmer I didn't think you would check this that soon :))) thanks for accepting my contribution!. I'll translate more of your videos whenever I have free time (and wifi haha :D )
No wonder the subtitle was spot on! Great work mate, thanks for that! Also thanks @StatQuest with Josh Starmer, nice video with simple explanation, I'm trying to make sense of it.
This is by far the best on internet, Khan Academy doesnt have this content, all courses on coursera,udemy either wave formulas in the air or dont bother for a simple yet enlightening explanation..This is what practicioners need.Bravo!
I enjoy your videos and you are performing a valuable service. The few things I would mention that would be helpful are that PCA is really a measure of covariance in a sample and that PCA does NOT provide ANY indication of statistical significance. Understanding Covariance is helpful to really understand PCA. Also, PCA is particularly useful when patterns emerge between experimental and non-experimental parameters. If patterns associated with experimental parameters are observed (i.e. treatment conditions) it indicates that there may be changes between samples/populations that are of interest; in cases where there are patterns associated with non-experimental parameters (such as collection date or incubation conditions) it indicates that the date of collection resulted in more variance than experimental parameters. In such a case, it points to a possible flaw in experimental design so that it would be of benefit to re-evaluate sample collection/preparation/incubation etc... in the workflows to minimize the influence on the studied populations.
I've been searching PCA for dummies for so long and I'm glad I found this! I can finally understand what the researchers in this journal I'm reading are trying to say Haha!
Hooray!!! I'm so glad I could help. If you want to go a little bit deeper, let me recommend my other PCA video. If you watch that one, you will be a PCA master! ua-cam.com/video/FgakZw6K1QQ/v-deo.html
2:03 People should stop here and listen very carefully because this is a really important concept, and I mean - Really important! When analyzing data and the parameters effecting the outcome of something - this must be the way to think. Great work
I had to wrap my head around PCA plots as part of a presentation and just could not understand it. This was really well done and I'll be taking this knowledge in with me. Thank you!
so well structured, so on point - like all of the videos. very rare quality of a teacher: the comibnation of deep understanding AND the ability to narrow it down... almost like reducing dimensions to make things simpler to understand ;) what a great work!
I have taken ML this semester and to be very honest I am understanding all the concepts from your videos. I would be really grateful if you could upload a playlist on Neural network and Deep Learning.
@@statquest Thank you for so clearly explaining these concepts. Looking forward to your Neural Networks videos! Will share your videos with my colleagues.
I love your humor! What a lovely way to present and explain. ahem.. what could be daunting to some lol (such as myself!) Grateful for the work and the passion! Keep up the good work! A new subscriber!
Thanks! This was a great overview. I am in big data for a pharma company and we added PCA to one of our data tools. The documentation we received was a little "dry" so thank you for putting this into easy to understand key concepts. This helped a lot. Also, I did my original graduate work in mRNA decay so bonus points for dragging mRNA into this. :) :) :)
I'm not kidding, you're getting me through the hard concepts of grad school! You should teach on Coursera or Udemy, you could become the fastest millionaire in education! :);)
I am fan of your videos and the way you explain. Most part of this video, and more prominently after 3:52, it started appearing like the plots of cells whereas it is really the plot of Genes and Cells are the dimentions of those Genes..
The video is correct - if you do PCA based on correlations, you start with plots of genes and end up with plots of cells. This is confusing and one of the reasons I don't think it's a good idea to teach PCA from the perspective of correlations. However, people still do it, so I have this video. However, in my opinion, an easier to understand method (because we plot cells the entire time) is the more modern approach that uses SVD and is explained in this video: ua-cam.com/video/FgakZw6K1QQ/v-deo.html
Great video! I became a bit addicted to the StatQuest videos and my anxiety levels increased for a while, not seeing the usual morning Monday upload. Now I need to figure out what t-SNE plots are... ;)
Extremely helpful thanks, explaining the principal components in the order that you did, you nearly lost me I would consider rearranging the explanation of what pc1 and pc2 are in the video.
Glad you liked this video! If you have time, you should check out the new and improved version (which is longer, but it's worth it, I promise you): ua-cam.com/video/FgakZw6K1QQ/v-deo.html
How long do you take preparing such an amazing explanation?. Everything is so clear that I can not even imagine why professors at university are not able to explain this way, after so many years of experience. Everything is so mathematical and let us say not intuitive at all, that make us think we are dumb.
Love your tutorials - don't have those leaps that can easily derail. One premise that might have been left out is, what are you actually trying to determine? And why is it not obviously accomplished by simple inspection?
The motivation for PCA starts at 0:27 Alternatively, you can watch the my step-by-step video on PCA (it's not hard to follow): ua-cam.com/video/FgakZw6K1QQ/v-deo.html
Dear Josh Starmer. First of all, thank you so much for making this channel with such amazing content. What about a StatQuest about Causal Inference? Big fan! Cheers!
This is a pretty different explanation of PCA than I've usually seen. I've seen some signal processing explanations and some statistics explanations about perpendicular/uncorrelated basis vectors
Thank you for your help! Your videos are amazing! If I can suggest you another topic to discuss about, one of your clear description of UMAP method would be very helpful :)
UMAP is very, very similar to t-SNE. The only differences are very subtle, so if you understand the main ideas of t-SNE, then you understand the main ideas of UMAP. Here's my video on t-SNE: ua-cam.com/video/NEaUSP4YerM/v-deo.html
StatQuest is the best! Do you have any suggestions on how to do the last step: take clusters identified visually by PCA and clearly separate them? Particularly when they're not as cleanly clustered so discrimination becomes more subjective!
YES!! What an awesome video. Love everything about it. So clearly explained, I like that you're only saying things that are humorous or extremely relevant. +1 subscribers.
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/
Special thanks to PROTIST for the Russian subtitles!!! :)
Hi Josh,
Love your content. Has helped me to learn a lot & grow. You are doing an awesome work. Please continue to do so.
Wanted to support you but unfortunately your Paypal link seems to be dysfunctional. Please update it.
I appreciated your effort spent on these videos. Sadly, since I am still a student, I have no money to support you just a bit. So, I have spent much of my effort to translate your videos into my language as it is my best language as a thank to you. Hope you would accept my thank.
Thank you very much!!!! :)
@StatQuest with Josh Starmer I didn't think you would check this that soon :))) thanks for accepting my contribution!. I'll translate more of your videos whenever I have free time (and wifi haha :D )
@@tuongminhquoc I really appreciate it! :)
No wonder the subtitle was spot on! Great work mate, thanks for that! Also thanks @StatQuest with Josh Starmer, nice video with simple explanation, I'm trying to make sense of it.
Really excited when watching this video in Vietnamese subtitle, thank you!!!
This is by far the best on internet, Khan Academy doesnt have this content, all courses on coursera,udemy either wave formulas in the air or dont bother for a simple yet enlightening explanation..This is what practicioners need.Bravo!
Thank you! :)
Without Statquest, I cannot imagine how hard my academic and professional life would be.... Thanks a lot Prof Starmer!
Thanks!
If my university would have been teaching 10% like you I would have completed my engineering in just 1year
Awesome video ♥
Bam! :)
Thank you Josh for the clearly explained abstract concepts! It is even more informational than a 2-hour lecture in a college.
Glad it was helpful!
I wanted to browse a video with the title ''HOW TO THANK STAT QUEST?" the only answer I got is just pray for the channel's success....
Thank you! :)
Your voice tone reflects how confident and smart you are... Thanks, plz we need more videos related to machine learning stuff
I enjoy your videos and you are performing a valuable service. The few things I would mention that would be helpful are that PCA is really a measure of covariance in a sample and that PCA does NOT provide ANY indication of statistical significance. Understanding Covariance is helpful to really understand PCA. Also, PCA is particularly useful when patterns emerge between experimental and non-experimental parameters. If patterns associated with experimental parameters are observed (i.e. treatment conditions) it indicates that there may be changes between samples/populations that are of interest; in cases where there are patterns associated with non-experimental parameters (such as collection date or incubation conditions) it indicates that the date of collection resulted in more variance than experimental parameters. In such a case, it points to a possible flaw in experimental design so that it would be of benefit to re-evaluate sample collection/preparation/incubation etc... in the workflows to minimize the influence on the studied populations.
You just saved my life, sir! Doing journal club tomorrow and I had no idea how to read a PCA from steady-state metabolomics. Thank you!
Glad I could help!
Every maths prof. must be like, way of explaination is as simple as possible. Thank you.
I've been searching PCA for dummies for so long and I'm glad I found this! I can finally understand what the researchers in this journal I'm reading are trying to say Haha!
Hooray!!! I'm so glad I could help. If you want to go a little bit deeper, let me recommend my other PCA video. If you watch that one, you will be a PCA master! ua-cam.com/video/FgakZw6K1QQ/v-deo.html
i was also having the same problem. i watched his 20 min video but I couldn't understand anything.
Very nicely explained thank you so much for Putting this
Hooray! I'm glad you like it. :)
2:03 People should stop here and listen very carefully because this is a really important concept, and I mean - Really important!
When analyzing data and the parameters effecting the outcome of something - this must be the way to think.
Great work
Nice! :)
Thank you for these sequencing, singing, and recipe videos, this channel needs more subscribers.
Thank you very much! :)
It is more understandable that my 1.5 h lecture and a good start of PCA class.
Thank you for the video. Very well created.
Glad it was helpful!
As I learn PCA in a machine learning course, I knew that you have a good video explanation on this topic!! thanks!
Glad it was helpful!
This is one of the most great channels I have ever seen . If u are looking for a good ,easy and quick explanation you are in the right place ;)
Wow, thanks!
I had to wrap my head around PCA plots as part of a presentation and just could not understand it. This was really well done and I'll be taking this knowledge in with me. Thank you!
Hooray! :)
You are helping me survive my Research Analytics class - HOORAY! :-)
BAM! :)
so well structured, so on point - like all of the videos. very rare quality of a teacher: the comibnation of deep understanding AND the ability to narrow it down... almost like reducing dimensions to make things simpler to understand ;) what a great work!
Thank you! :)
I have taken ML this semester and to be very honest I am understanding all the concepts from your videos. I would be really grateful if you could upload a playlist on Neural network and Deep Learning.
Awesome! Neural Networks should come out in the next few months.
@@statquest Thank you for so clearly explaining these concepts. Looking forward to your Neural Networks videos! Will share your videos with my colleagues.
I always come across your videos when looking for stat information. And always your videos are the best.
Awesome! :) Thank you! :)
I love your humor! What a lovely way to present and explain. ahem.. what could be daunting to some lol (such as myself!) Grateful for the work and the passion! Keep up the good work!
A new subscriber!
Thanks so much!
Thank you for the Arabic subtitling, as I have always recommended your channel to my students; best wishes.
Thanks!
I just cried after watching your video.. I looking to easy concept for one hour .... Thank you
Glad it helped!
100s of lines in 5 min.. great work sir.
wrg
Thanks! This was a great overview. I am in big data for a pharma company and we added PCA to one of our data tools. The documentation we received was a little "dry" so thank you for putting this into easy to understand key concepts. This helped a lot. Also, I did my original graduate work in mRNA decay so bonus points for dragging mRNA into this. :) :) :)
Thanks! Now I finally understand what I am doing in the lab! 🇧🇷
Hooray! :)
You tech Harvard type of kind of stuff in elementary school way in all your videos, how do you that man! It's amazing, Thank you so much
Thanks!
Good stuff Josh. Going to the lengthier version to further blast this through my thick skull. 😃 Appreciate your efforts with this!
Enjoy!
Wow, this sheds a lot of light on dimension reduction. Very clearly explained & illustrated. TQVM!!!
Thanks! :)
Thanks Josh, I can always get something new from your videos.
bam! :)
I'm not kidding, you're getting me through the hard concepts of grad school! You should teach on Coursera or Udemy, you could become the fastest millionaire in education! :);)
Thanks and good luck!
I am fan of your videos and the way you explain. Most part of this video, and more prominently after 3:52, it started appearing like the plots of cells whereas it is really the plot of Genes and Cells are the dimentions of those Genes..
The video is correct - if you do PCA based on correlations, you start with plots of genes and end up with plots of cells. This is confusing and one of the reasons I don't think it's a good idea to teach PCA from the perspective of correlations. However, people still do it, so I have this video. However, in my opinion, an easier to understand method (because we plot cells the entire time) is the more modern approach that uses SVD and is explained in this video: ua-cam.com/video/FgakZw6K1QQ/v-deo.html
Great!! Your Calm and crystal pronunciation makes the concept very clear to understand. Thanks
Thank you!
The way you easily and calmly explain such complex topics is outstanding. Thank you very much.
Thanks!
You have a teaching talent.
Thank you for the all your videos!
Holy moly. I finally understand the concept of PCA plots :O THANK YOU SO MUCH
Bam! :)
Thank you so much for making this video! I've got my final year project due soon and Id lost the plot before this video!
bam!
What a great video that clearly and concisely explains PCA. Great job, keep these up.
Just want to let you know, the 'Awesome song' just won you a subscriber.
Bam! :)
Great video! I became a bit addicted to the StatQuest videos and my anxiety levels increased for a while, not seeing the usual morning Monday upload. Now I need to figure out what t-SNE plots are... ;)
Best explanations of PCA in layman terms. Great work. Thank you!
Wow, thanks!
i watched your 20 min video too. But this was easier to understand. Thank you so much.
Hooray!
WOW. YOUR EXPLANATIONS, MY GOOD MAN, WERE CLEAR.
Thank you! :)
Extremely helpful thanks, explaining the principal components in the order that you did, you nearly lost me I would consider rearranging the explanation of what pc1 and pc2 are in the video.
Glad you liked this video! If you have time, you should check out the new and improved version (which is longer, but it's worth it, I promise you): ua-cam.com/video/FgakZw6K1QQ/v-deo.html
StatQuest is really the best! that you so much to prevent my brain to explode!!!
Thanks!
thank you statquest, you're a real one🙏
Thanks! :)
StatQuest is indeed the best.
Thanks!
You're a lifesaver, Josh!
Thanks!
Thanks!
You have saved me from the sea of formulas. Thank you!
bam!
This is saving my mind. I am an archaeologist trying to understand statistics and really reading abount ir has been nothing but torture XD
Wow can I ask, do you study statistics out of pure interest?
Good luck with your studies!
StatQuest is definitely the best 📊
Hooray!!! :)
thanks for these videos it helps me understand better compared to classes
Thanks! :)
This was so smoothly explained. Thank you soooooooooooooooo much!!!!!
Thanks!
Thanks!
WOW!!! Thank you very much for your support! :)
such a great summary, BAM! thanks for your work.
Thanks!
How long do you take preparing such an amazing explanation?. Everything is so clear that I can not even imagine why professors at university are not able to explain this way, after so many years of experience. Everything is so mathematical and let us say not intuitive at all, that make us think we are dumb.
Thank you very much! :)
this saved my life thank you i hope you're doing well sir
Thanks!
Clearly explained Josh. Thank you
Thank you!
thanks man.. your videos are both very informative and fun.. really appreciated ❤❤❤❤❤❤
Glad you like them!
You just earned yourself a subscriber!!!!!
bam!
Wow--this was SO very helpful, even if corny at times, lol. Thank you so much!
Thanks so much! :)
StatQuest is the best
Hooray!!! :)
awesome pca for dim reduction with vertical+horizontal+depth all in one 3-d rotates
:)
Love your tutorials - don't have those leaps that can easily derail. One premise that might have been left out is, what are you actually trying to determine? And why is it not obviously accomplished by simple inspection?
The motivation for PCA starts at 0:27 Alternatively, you can watch the my step-by-step video on PCA (it's not hard to follow): ua-cam.com/video/FgakZw6K1QQ/v-deo.html
Thank you for all the videos. It is super easy to understand.
Thanks! :)
Dear Josh Starmer. First of all, thank you so much for making this channel with such amazing content.
What about a StatQuest about Causal Inference?
Big fan! Cheers!
I'll keep that in mind.
@@statquest Thank you very much! :))
Phew, thank you so much! This was very helpful.
Glad it helped!
simple enough for my understanding. thanks a lot.
Glad it helped!
Im so thankful for your videos bro !
Glad you like them!
Great explination as always👍
Thanks again!
This is a pretty different explanation of PCA than I've usually seen.
I've seen some signal processing explanations and some statistics explanations about perpendicular/uncorrelated basis vectors
This is the main ideas in a short video. If you want to learn more, see: ua-cam.com/video/FgakZw6K1QQ/v-deo.html
Thank you for your help! Your videos are amazing! If I can suggest you another topic to discuss about, one of your clear description of UMAP method would be very helpful :)
UMAP is very, very similar to t-SNE. The only differences are very subtle, so if you understand the main ideas of t-SNE, then you understand the main ideas of UMAP. Here's my video on t-SNE: ua-cam.com/video/NEaUSP4YerM/v-deo.html
@@statquest thank you for the answer and the link!
StatQuest is the best! Do you have any suggestions on how to do the last step: take clusters identified visually by PCA and clearly separate them? Particularly when they're not as cleanly clustered so discrimination becomes more subjective!
It's a good question. You could try k-means clustering.
WTF HOW IT CAN BE THIS SIMPLE OMG! THANKS.
bam! :)
Excellent explanation! Thank you
Thanks!
Dude, you are my hero. Thanks!
bam!
I looovee your voice and your explanation... Great job, Sir.. Thank you !!!
Hooray! I’m glad you like the video!! :)
Great concise presentation!
Much appreciated!👍
Thanks!
Thank you so much for such high quality videos.. I am broke right now but when I will have money, I will definitely join your membership
Thank you! :)
This guy always sounds like he's trying to hypnotize someone.
Noted!
It worked. I learned!
So well explained it!!! AMAZING!!! Thank you very much for making this video!!!
Glad you enjoyed it!
Congratulation. it is an excellent example of PCA.
Thanks! :)
This is EXCELLENT! Thank you good sir!
Thank you! :)
Great explanation as always! Thanks a lot for your effort!
Glad you liked it!
Thank you. This video is help me so many.
Glad it was helpful!
That Intro 🔥🔥🔥!
Thanks!
Thanks for the explanation!! It makes sense to use it with dendrograms for plant breeding!!
BAM! :)
Thanks, really great explanation. Easy to understand 😍😍😍😍
Glad it was helpful!
Came for the explanations and definitely stayed for the openings
bam!
I was dumb before watching this video. Now I am still dumb but at least I understand PCA.
:)
Excellent video, really excellent. You are great at what you do.
Thank you!
YES!! What an awesome video. Love everything about it. So clearly explained, I like that you're only saying things that are humorous or extremely relevant. +1 subscribers.
Awesome!!! :)
Very well explained!
Thanks! :)
This was such a great explanation and so entertaining!
Glad you enjoyed it!
Thank you very much for this video! Really great video :)
BAM!
I really really enjoy your videos!!! Thank you so much !!!!
Thank you! :)
i love your way of explaining things tnx alot ...these videos are really helpful
Glad you like them!