I this video I continue my Machine Learning series and attempt to explain Linear Regression with Gradient Descent.

This video is part of session 3 of my Spring 2017 ITP "Intelligence and Learning" course (https://github.com/shiffman/NOC-S17-2-Intelligence-Learning/tree/master/week3-classification-regression)

Videos on Calculus:
Power Rule: https://youtu.be/IKb_3FJtA1U
Chain Rule: https://youtu.be/cE6wr0_ad8Y
Partial Derivative: https://youtu.be/-WVBXXV81R4

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Links discussed in this video:
Session 3 of Intelligence and Learning: https://github.com/shiffman/NOC-S17-2-Intelligence-Learning/tree/master/week3-classification-regression
Nature of Code: http://natureofcode.com/
kwichmann's Linear Regression Diagnostics: https://kwichmann.github.io/ml_sandbox/linear_regression_diagnostics/
Linear Regression on Wikipedia: https://en.wikipedia.org/wiki/Linear_regression

Source Code for the all Video Lessons: https://github.com/CodingTrain/Rainbow-Code

p5.js: https://p5js.org/
Processing: https://processing.org

For More Coding Challenges: https://www.youtube.com/playlist?list=PLRqwX-V7Uu6ZiZxtDDRCi6uhfTH4FilpH
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