[05x11] Principal Component Analysis with MultivariateStats.jl | Julia Unsupervised Machine Learning

In this Julia Unsupervised Machine Learning coding tutorial, you'll learn about Dimensionality Reduction by using Principal Component Analysis (PCA) from the MultivariateStats.jl package.
After learning the basic concepts, you'll apply PCA to the Iris Flower Data Set to project the 4-dimensional data set into a 3-dimensional space in order to generate a 3D interactive plot.

- This tutorial is intended for students, hobbyists and amateurs.
- This tutorial is episode 11 of a 13-part series and is part of the Julia Machine Learning for Beginners playlist.
- Schedule: New tutorials are posted on Sundays / Mondays.
- Prerequisites: Julia, VS Code and Episodes 501 through 510.

00:00 Intro
01:10 Motivation
05:07 Concepts
15:00 Application
22:44 Recap
24:35 Outro

########################################
Links for this tutorial
########################################

Code for this tutorial:
https://github.com/julia4ta/tutorials...

Link to MultivariateStats.jl (GitHub):
https://github.com/JuliaStats/Multiva...

Link to MultivariateStats.jl (documentation):
https://juliastats.org/MultivariateSt...

Link to PlotlyJS.jl (GitHub):
https://github.com/JuliaPlots/PlotlyJ...

Link to Plots.jl (documentation):
https://docs.juliaplots.org/stable/

Link to RDatasets.jl (GitHub):
https://github.com/JuliaStats/RDatase...

########################################
Links for this series
########################################

Link to Series 5 Playlist [Julia Machine Learning for Beginners]
   • [05x01] What is Machine Learning?  

Andrew Ng's Stanford Machine Learning Course
Stanford CS229: Machine Learning | Autumn 2018
   • Stanford CS229: Machine Learning Cour...  

The Julia Programming Language
https://julialang.org/
https://docs.julialang.org/en/v1/
   / thejulialanguage  

VS Code
https://code.visualstudio.com/

########################################

Notice of Non-Affiliation and Disclaimer:
I am not affiliated, associated, authorized, endorsed by, or in any way officially connected with Andrew Ng or Stanford University.
Nor am I affiliated, associated, authorized, endorsed by, or in any way officially connected with The Julia Programming Language, Julia Academy, Julia Computing, Microsoft, or any of their subsidiaries or their affiliates.
Nor am I affiliated, associated, authorized, endorsed by, or in any way officially connected with any software, packages or libraries used in this video.
All marks, emblems and images are registered trademarks of their respective owners. Use of them does not imply any affiliation with or endorsement by them.

########################################

Join Button (Channel Membership):
If you like what I do, then please consider Joining and becoming a Channel Member.

   / @doggodotjl  

Thank you!