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!
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 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! th-cam.com/video/FgakZw6K1QQ/w-d-xo.html
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 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!
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: th-cam.com/video/FgakZw6K1QQ/w-d-xo.html
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 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.
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): th-cam.com/video/FgakZw6K1QQ/w-d-xo.html
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! :)
Your voice tone reflects how confident and smart you are... Thanks, plz we need more videos related to machine learning stuff
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!
Every maths prof. must be like, way of explaination is as simple as possible. Thank you.
Very nicely explained thank you so much for Putting this
Hooray! I'm glad you like it. :)
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!
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! :)
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.
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!
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! th-cam.com/video/FgakZw6K1QQ/w-d-xo.html
i was also having the same problem. i watched his 20 min video but I couldn't understand anything.
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! :)
You are helping me survive my Research Analytics class - HOORAY! :-)
BAM! :)
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! :)
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!
Thanks! Now I finally understand what I am doing in the lab! 🇧🇷
Hooray! :)
Thank you for these sequencing, singing, and recipe videos, this channel needs more subscribers.
Thank you very much! :)
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!
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 always come across your videos when looking for stat information. And always your videos are the best.
Awesome! :) Thank you! :)
I just cried after watching your video.. I looking to easy concept for one hour .... Thank you
Glad it helped!
Holy moly. I finally understand the concept of PCA plots :O THANK YOU SO MUCH
Bam! :)
Thanks Josh, I can always get something new from your videos.
bam! :)
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!
100s of lines in 5 min.. great work sir.
wrg
Great!! Your Calm and crystal pronunciation makes the concept very clear to understand. Thanks
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!
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: th-cam.com/video/FgakZw6K1QQ/w-d-xo.html
Wow, this sheds a lot of light on dimension reduction. Very clearly explained & illustrated. TQVM!!!
Thanks! :)
You're a lifesaver, Josh!
Thanks!
Thanks!
WOW. YOUR EXPLANATIONS, MY GOOD MAN, WERE CLEAR.
Thank you! :)
thank you statquest, you're a real one🙏
Thanks! :)
The way you easily and calmly explain such complex topics is outstanding. Thank you very much.
Thanks!
Thank you for the Arabic subtitling, as I have always recommended your channel to my students; best wishes.
Thanks!
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. :) :) :)
You have a teaching talent.
Thank you for the all your videos!
i watched your 20 min video too. But this was easier to understand. Thank you so much.
Hooray!
Good stuff Josh. Going to the lengthier version to further blast this through my thick skull. 😃 Appreciate your efforts with this!
Enjoy!
StatQuest is really the best! that you so much to prevent my brain to explode!!!
Thanks!
StatQuest is indeed the best.
Thanks!
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!
You have saved me from the sea of formulas. Thank you!
bam!
I was dumb before watching this video. Now I am still dumb but at least I understand PCA.
:)
Just want to let you know, the 'Awesome song' just won you a subscriber.
Bam! :)
Best explanations of PCA in layman terms. Great work. Thank you!
Wow, thanks!
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!
StatQuest is definitely the best 📊
Hooray!!! :)
What a great video that clearly and concisely explains PCA. Great job, keep these up.
Clearly explained Josh. Thank you
Thank you!
such a great summary, BAM! thanks for your work.
Thanks!
You just earned yourself a subscriber!!!!!
bam!
thanks man.. your videos are both very informative and fun.. really appreciated ❤❤❤❤❤❤
Glad you like them!
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.
This was so smoothly explained. Thank you soooooooooooooooo much!!!!!
Thanks!
thanks for these videos it helps me understand better compared to classes
Thanks! :)
Wow--this was SO very helpful, even if corny at times, lol. Thank you so much!
Thanks so much! :)
this saved my life thank you i hope you're doing well sir
Thanks!
This guy always sounds like he's trying to hypnotize someone.
Noted!
It worked. I learned!
simple enough for my understanding. thanks a lot.
Glad it helped!
WTF HOW IT CAN BE THIS SIMPLE OMG! THANKS.
bam! :)
Phew, thank you so much! This was very helpful.
Glad it helped!
Thanks!
WOW!!! Thank you very much for your support! :)
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!
Im so thankful for your videos bro !
Glad you like them!
Dude, you are my hero. Thanks!
bam!
Great explination as always👍
Thanks again!
Thank you for all the videos. It is super easy to understand.
Thanks! :)
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... ;)
This is EXCELLENT! Thank you good sir!
Thank you! :)
StatQuest is the best
Hooray!!! :)
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): th-cam.com/video/FgakZw6K1QQ/w-d-xo.html
Excellent explanation! Thank you
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! :)
So well explained it!!! AMAZING!!! Thank you very much for making this video!!!
Glad you enjoyed it!
Came for the explanations and definitely stayed for the openings
bam!
That Intro 🔥🔥🔥!
Thanks!
I looovee your voice and your explanation... Great job, Sir.. Thank you !!!
Hooray! I’m glad you like the video!! :)
Great explanation as always! Thanks a lot for your effort!
Glad you liked it!
Thanks, really great explanation. Easy to understand 😍😍😍😍
Glad it was helpful!
Thank you. This video is help me so many.
Glad it was helpful!
Thank you very much for this video! Really great video :)
BAM!
Congratulation. it is an excellent example of PCA.
Thanks! :)
awesome pca for dim reduction with vertical+horizontal+depth all in one 3-d rotates
:)
Great concise presentation!
Much appreciated!👍
Thanks!
Best regards from brazil, you are the best! thank you
Thanks for the explanation!! It makes sense to use it with dendrograms for plant breeding!!
BAM! :)
Thank you for existing!
Thanks!
thank you so much... it is quite informative and understandable...
Glad it was helpful!
I really really enjoy your videos!!! Thank you so much !!!!
Thank you! :)
Excellent video, really excellent. You are great at what you do.
Thank you!
This was such a great explanation and so entertaining!
Glad you enjoyed it!
Such a beautiful work
Great video indeed
thanks !!
Thank you! :)
Very well explained!
Thanks! :)
i love your way of explaining things tnx alot ...these videos are really helpful
Glad you like them!
Josh is the best!
:)
That was very informative ! keep going !
Thanks!
BBBBBaaaaaaaaaaaammm!! Good song and good video!!! Please do one on factor analysis!!!
Thanks!