Welcome to The Math of Intelligence! In this 3 month course, we'll cover the most fundamental math concepts in Machine Learning. In this first lesson, we'll go over a very popular optimization technique called gradient descent to help us predict how many calories a cyclist would burn given just their distance traveled. We'll also follow the story of 2 data scientists as they attempt to find the Higgs-Boson (God particle) via anomaly detection. No collaborations, this is an independent course.
Code for this video (with challenge details):
https://github.com/llSourcell/Intro_to_the_Math_of_intelligence
TypicalHog's winning code:
https://github.com/TypicalHog/THCrypt
Syllabus for this course:
https://github.com/llSourcell/The_Math_of_Intelligence
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More learning resources:
http://machinelearningmastery.com/linear-regression-tutorial-using-gradient-descent-for-machine-learning/
https://spin.atomicobject.com/2014/06/24/gradient-descent-linear-regression/
https://www.coursera.org/learn/machine-learning/lecture/kCvQc/gradient-descent-for-linear-regression
http://cs229.stanford.edu/notes/cs229-notes1.pdf
http://blog.hackerearth.com/gradient-descent-algorithm-linear-regression
https://www.r-bloggers.com/linear-regression-by-gradient-descent/
https://www.youtube.com/watch?v=XdM6ER7zTLk&t=1650s
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