Explaining PCA

With this video we are wrapping up with PCA. This last installment will show us that we might gain additional insight into our data if we observe what dimensions comprise a given principal component. We conclude that PCA is generally useful for dimensionality reduction and noise removal.
This video is a part of Introduction to Data Science video series that dives into machine learning, visual analytics, and joys of interactive data analysis using Orange Data Mining software (orangedatamining.com).
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The development of this video series was supported by grants from the Slovenian Research Agency (including P2-0209, V2-2274, and L2-3170), Slovenia Ministry of Digital Transformation, European Union (including xAIM and ARISA) and Google.org/Tides foundation.
#machinelearning #orange #visualanalytics #datamining
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Written by: Blaž Zupan (biolab.si/blaz)
Presented by: Noah Novšak
Production and edit: Lara Zupan
Intro/outro: Agnieszka Rovšnik
Music by: Damjan Jović - Dravlje Rec
Orange is developed by Biolab at University of Ljubljana (www.biolab.si)

[@dedoyxp]

I really love this series! Makes it really easy to learn visually...

[@Tttt-yi9rk]

Hi! I am currently using version Orange3-3.36.2, and I cannot find the "Feature Constructor". How can I access it?

[@truptirathod677]

Which data set you are using

[@bowenjing3674]

Hi, thanks for your video
May I know what app you use in the video for drawing diagram ?

[@OrangeDataMining]

Orange Data Mining. 💪