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Kim Kardashian Thanks Kanye "Ye" West at 2021 PCAs | People's Choice Awards
Kim Kardashian thanks Kanye "Ye" West for his help while accepting the Fashion Icon award at the 2021 People's Choice Awards.
#KimKardashian #2021PCAs #PeoplesChoiceAwards #ERedCarpetAndAwardShows
Subscribe: http://eonli.ne/RedCarpetSubscribe
About The Peoples Choice Awards:
The "People’s Choice Awards" is the only major awards show voted on entirely by fans in movies, music, television and digital. See all the biggest stars like The Rock, Halle Berry \u0026 Christina Aguilera receive Icon Awards. Plus, find out who will win the awards in the categories fans voted on. Hosted by Kenan Thompson.
About E! Red Carpet and Award Shows:
E! is the ultimate destination for Red Carpet coverage. From the Academy Awards to the Emmys to the E! People’s Choice Awards, we give you an insider's view of the arrivals, exclusive interviews and of course, cover every stitch of the fabulous fashion. Only "Live From the Red Carpet" host Laverne Cox, along with E!’s entire awards team of fashion and entertainment experts, have the most comprehensive LIVE coverage of the biggest premieres and events.
Connect with E! Red Carpet and Award Shows:
Visit the E! News WEBSITE: http://eonli.ne/enews
Follow E! Live from the Red Carpet on TWITTER: http://eonli.ne/RedCarpetTwitter
Follow E! News on TWITTER: http://eonli.ne/ENewsTW
Like E! News on FACEBOOK: http://eonli.ne/ENewsFB
Check out E! News on INSTAGRAM: http://eonli.ne/ENewsIG
Kim Kardashian Thanks Kanye "Ye" West at 2021 PCAs | People's Choice Awards
https://www.youtube.com/ERedCarpetandAwardShows
E! Red Carpet & Award Shows : The title of Fashion Icon is well deserved!
John Wayne : I'm glad that everyone has come to the realization that kanye literally built Kim's legacy in fashion and without him she wouldn't even be in the fashion industry
Abajjaakakk Koko : She proofed everyone wrong! From starting out as a reality tv star that no one wanted to do anything with, to a Fashion Icon…! This is truly inspiring and a proof that you can reach everything you want to, if you only work for it as if there is no other choice…!
Sharon Judd : Kim has gotten so poised and confident. Love that!
Lady LaTre'viette Phantumhive : If she doesn’t deserve anything else she for sure deserves this award. Congratulations Kim !!!!!
StatQuest: Principal Component Analysis (PCA), Step-by-Step
Principal Component Analysis, is one of the most useful data analysis and machine learning methods out there. It can be used to identify patterns in highly complex datasets and it can tell you what variables in your data are the most important. Lastly, it can tell you how accurate your new understanding of the data actually is.
In this video, I go one step at a time through PCA, and the method used to solve it, Singular Value Decomposition. I take it nice and slowly so that the simplicity of the method is revealed and clearly explained.
1:47: Points 5 and 6 are not in the right location
If you are interested in doing PCA in R see:
If you are interested in learning more about how to determine the number of principal components, see:
For a complete index of all the StatQuest videos, check out:
https://statquest.org/video-index/
If you'd like to support StatQuest, please consider...
Buying The StatQuest Illustrated Guide to Machine Learning!!!
PDF - https://statquest.gumroad.com/l/wvtmc
Paperback - https://www.amazon.com/dp/B09ZCKR4H6
Kindle eBook - https://www.amazon.com/dp/B09ZG79HXC
Patreon: https://www.patreon.com/statquest
...or...
YouTube Membership: https://www.youtube.com/channel/UCtYLUTtgS3k1Fg4y5tAhLbw/join
...a cool StatQuest t-shirt or sweatshirt:
https://shop.spreadshirt.com/statquest-with-josh-starmer/
...buying one or two of my songs (or go large and get a whole album!)
https://joshuastarmer.bandcamp.com/
...or just donating to StatQuest!
https://www.paypal.me/statquest
Lastly, if you want to keep up with me as I research and create new StatQuests, follow me on twitter:
https://twitter.com/joshuastarmer
0:00 Awesome song and introduction
0:30 Conceptual motivation for PCA
3:23 PCA worked out for 2-Dimensional data
5:03 Finding PC1
12:08 Singular vector/value, Eigenvector/value and loading scores defined
12:56 Finding PC2
14:14 Drawing the PCA graph
15:03 Calculating percent variation for each PC and scree plot
16:30 PCA worked out for 3-Dimensional data
Correction:
1:47: Points 5 and 6 are not in the correct location
#statquest #PCA #ML
StatQuest with Josh Starmer : Correction:
1:47: Points 5 and 6 are not in the correct location
NOTE 1: The StatQuest PCA Study Guide is available! https://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.
Support StatQuest by buying my book The StatQuest Illustrated Guide to Machine Learning or a Study Guide or Merch!!! https://statquest.org/statquest-store/
Safa Abdeljabbar : 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.
Dee T : 6 years into my phd and I finally understand how a PCA plot actually works. Thank you!
Josiah Nielsen : 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!
kush saran : 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.
StatQuest: PCA main ideas in only 5 minutes!!!
The main ideas behind PCA are actually super simple and that means it's easy to interpret a PCA plot: Samples that are correlated will cluster together apart from samples that are not correlated with them. In this video, I walk through the ideas so that you will have an intuitive sense of how PCA plots are draw. If you'd like more details, check out my full length PCA video here:
For a complete index of all the StatQuest videos, check out:
https://statquest.org/video-index/
If you'd like to support StatQuest, please consider...
Buying The StatQuest Illustrated Guide to Machine Learning!!!
PDF - https://statquest.gumroad.com/l/wvtmc
Paperback - https://www.amazon.com/dp/B09ZCKR4H6
Kindle eBook - https://www.amazon.com/dp/B09ZG79HXC
Patreon: https://www.patreon.com/statquest
...or...
YouTube Membership: https://www.youtube.com/channel/UCtYLUTtgS3k1Fg4y5tAhLbw/join
...a cool StatQuest t-shirt or sweatshirt:
https://shop.spreadshirt.com/statquest-with-josh-starmer/
...buying one or two of my songs (or go large and get a whole album!)
https://joshuastarmer.bandcamp.com/
...or just donating to StatQuest!
https://www.paypal.me/statquest
Lastly, if you want to keep up with me as I research and create new StatQuests, follow me on twitter:
https://twitter.com/joshuastarmer
0:00 Awesome song and introduction
0:27 Motivation for using PCA
1:23 Correlations among samples
3:36 PCA converts correlations into a 2-D graph
4:26 Interpreting PCA plots
5:08 Other options for dimension reduction
#statquest #PCA #ML
StatQuest with Josh Starmer : Support StatQuest by buying my book The StatQuest Illustrated Guide to Machine Learning or a Study Guide or Merch!!! https://statquest.org/statquest-store/
Special thanks to PROTIST for the Russian subtitles!!! :)
Quốc Tưởng Minh : 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.
Guandong Kou : Thank you Josh for the clearly explained abstract concepts! It is even more informational than a 2-hour lecture in a college.
aborucu : 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!
fartissimo : 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.
Kim Kardashian thanks Kanye "Ye" West for his help while accepting the Fashion Icon award at the 2021 People's Choice Awards.
#KimKardashian #2021PCAs #PeoplesChoiceAwards #ERedCarpetAndAwardShows
Subscribe: http://eonli.ne/RedCarpetSubscribe
About The Peoples Choice Awards:
The "People’s Choice Awards" is the only major awards show voted on entirely by fans in movies, music, television and digital. See all the biggest stars like The Rock, Halle Berry \u0026 Christina Aguilera receive Icon Awards. Plus, find out who will win the awards in the categories fans voted on. Hosted by Kenan Thompson.
About E! Red Carpet and Award Shows:
E! is the ultimate destination for Red Carpet coverage. From the Academy Awards to the Emmys to the E! People’s Choice Awards, we give you an insider's view of the arrivals, exclusive interviews and of course, cover every stitch of the fabulous fashion. Only "Live From the Red Carpet" host Laverne Cox, along with E!’s entire awards team of fashion and entertainment experts, have the most comprehensive LIVE coverage of the biggest premieres and events.
Connect with E! Red Carpet and Award Shows:
Visit the E! News WEBSITE: http://eonli.ne/enews
Follow E! Live from the Red Carpet on TWITTER: http://eonli.ne/RedCarpetTwitter
Follow E! News on TWITTER: http://eonli.ne/ENewsTW
Like E! News on FACEBOOK: http://eonli.ne/ENewsFB
Check out E! News on INSTAGRAM: http://eonli.ne/ENewsIG
Kim Kardashian Thanks Kanye "Ye" West at 2021 PCAs | People's Choice Awards
https://www.youtube.com/ERedCarpetandAwardShows
E! Red Carpet & Award Shows : The title of Fashion Icon is well deserved!
John Wayne : I'm glad that everyone has come to the realization that kanye literally built Kim's legacy in fashion and without him she wouldn't even be in the fashion industry
Abajjaakakk Koko : She proofed everyone wrong! From starting out as a reality tv star that no one wanted to do anything with, to a Fashion Icon…! This is truly inspiring and a proof that you can reach everything you want to, if you only work for it as if there is no other choice…!
Sharon Judd : Kim has gotten so poised and confident. Love that!
Lady LaTre'viette Phantumhive : If she doesn’t deserve anything else she for sure deserves this award. Congratulations Kim !!!!!
StatQuest: Principal Component Analysis (PCA), Step-by-Step
Principal Component Analysis, is one of the most useful data analysis and machine learning methods out there. It can be used to identify patterns in highly complex datasets and it can tell you what variables in your data are the most important. Lastly, it can tell you how accurate your new understanding of the data actually is.
In this video, I go one step at a time through PCA, and the method used to solve it, Singular Value Decomposition. I take it nice and slowly so that the simplicity of the method is revealed and clearly explained.
1:47: Points 5 and 6 are not in the right location
If you are interested in doing PCA in R see:
If you are interested in learning more about how to determine the number of principal components, see:
For a complete index of all the StatQuest videos, check out:
https://statquest.org/video-index/
If you'd like to support StatQuest, please consider...
Buying The StatQuest Illustrated Guide to Machine Learning!!!
PDF - https://statquest.gumroad.com/l/wvtmc
Paperback - https://www.amazon.com/dp/B09ZCKR4H6
Kindle eBook - https://www.amazon.com/dp/B09ZG79HXC
Patreon: https://www.patreon.com/statquest
...or...
YouTube Membership: https://www.youtube.com/channel/UCtYLUTtgS3k1Fg4y5tAhLbw/join
...a cool StatQuest t-shirt or sweatshirt:
https://shop.spreadshirt.com/statquest-with-josh-starmer/
...buying one or two of my songs (or go large and get a whole album!)
https://joshuastarmer.bandcamp.com/
...or just donating to StatQuest!
https://www.paypal.me/statquest
Lastly, if you want to keep up with me as I research and create new StatQuests, follow me on twitter:
https://twitter.com/joshuastarmer
0:00 Awesome song and introduction
0:30 Conceptual motivation for PCA
3:23 PCA worked out for 2-Dimensional data
5:03 Finding PC1
12:08 Singular vector/value, Eigenvector/value and loading scores defined
12:56 Finding PC2
14:14 Drawing the PCA graph
15:03 Calculating percent variation for each PC and scree plot
16:30 PCA worked out for 3-Dimensional data
Correction:
1:47: Points 5 and 6 are not in the correct location
#statquest #PCA #ML
StatQuest with Josh Starmer : Correction:
1:47: Points 5 and 6 are not in the correct location
NOTE 1: The StatQuest PCA Study Guide is available! https://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.
Support StatQuest by buying my book The StatQuest Illustrated Guide to Machine Learning or a Study Guide or Merch!!! https://statquest.org/statquest-store/
Safa Abdeljabbar : 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.
Dee T : 6 years into my phd and I finally understand how a PCA plot actually works. Thank you!
Josiah Nielsen : 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!
kush saran : 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.
StatQuest: PCA main ideas in only 5 minutes!!!
The main ideas behind PCA are actually super simple and that means it's easy to interpret a PCA plot: Samples that are correlated will cluster together apart from samples that are not correlated with them. In this video, I walk through the ideas so that you will have an intuitive sense of how PCA plots are draw. If you'd like more details, check out my full length PCA video here:
For a complete index of all the StatQuest videos, check out:
https://statquest.org/video-index/
If you'd like to support StatQuest, please consider...
Buying The StatQuest Illustrated Guide to Machine Learning!!!
PDF - https://statquest.gumroad.com/l/wvtmc
Paperback - https://www.amazon.com/dp/B09ZCKR4H6
Kindle eBook - https://www.amazon.com/dp/B09ZG79HXC
Patreon: https://www.patreon.com/statquest
...or...
YouTube Membership: https://www.youtube.com/channel/UCtYLUTtgS3k1Fg4y5tAhLbw/join
...a cool StatQuest t-shirt or sweatshirt:
https://shop.spreadshirt.com/statquest-with-josh-starmer/
...buying one or two of my songs (or go large and get a whole album!)
https://joshuastarmer.bandcamp.com/
...or just donating to StatQuest!
https://www.paypal.me/statquest
Lastly, if you want to keep up with me as I research and create new StatQuests, follow me on twitter:
https://twitter.com/joshuastarmer
0:00 Awesome song and introduction
0:27 Motivation for using PCA
1:23 Correlations among samples
3:36 PCA converts correlations into a 2-D graph
4:26 Interpreting PCA plots
5:08 Other options for dimension reduction
#statquest #PCA #ML
StatQuest with Josh Starmer : Support StatQuest by buying my book The StatQuest Illustrated Guide to Machine Learning or a Study Guide or Merch!!! https://statquest.org/statquest-store/
Special thanks to PROTIST for the Russian subtitles!!! :)
Quốc Tưởng Minh : 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.
Guandong Kou : Thank you Josh for the clearly explained abstract concepts! It is even more informational than a 2-hour lecture in a college.
aborucu : 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!
fartissimo : 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.
... 
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