Most of the datasets you'll find will have more than 3 dimensions. How are you supposed to understand visualize n-dimensional data? Enter dimensionality reduction techniques. We'll go over the the math behind the most popular such technique called Principal Component Analysis.
Code for this video:
https://github.com/llSourcell/Dimensionality_Reduction
Ong's Winning Code:
https://github.com/jrios6/Math-of-Intelligence/tree/master/4-Self-Organizing-Maps
Hammad's Runner up Code:
https://github.com/hammadshaikhha/Math-of-Machine-Learning-Course-by-Siraj/tree/master/Self%20Organizing%20Maps%20for%20Data%20Visualization
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I used a screengrab from 3blue1brown's awesome videos: https://www.youtube.com/channel/UCYO_jab_esuFRV4b17AJtAw
More learning resources:
https://plot.ly/ipython-notebooks/principal-component-analysis/
https://www.youtube.com/watch?v=lrHboFMio7g
https://www.dezyre.com/data-science-in-python-tutorial/principal-component-analysis-tutorial
https://georgemdallas.wordpress.com/2013/10/30/principal-component-analysis-4-dummies-eigenvectors-eigenvalues-and-dimension-reduction/
http://setosa.io/ev/principal-component-analysis/
http://sebastianraschka.com/Articles/2015_pca_in_3_steps.html
https://algobeans.com/2016/06/15/principal-component-analysis-tutorial/
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