I recently kicked off the Stanford Machine Learning course by Andrew Ng, one of the most widely recommended starting points in the field. As someone prepping for Georgia Tech’s Master’s in Computational Analytics program this fall, I realized something pretty quickly: passively watching videos wasn’t going to cut it.
So, here I am, blogging my way through every concept I learn — not just for others, but for myself. It’s like reinforcement learning for my brain… and maybe a bit of deep learning too (pun fully intended 😏).
Here’s a breakdown of what I learned in Week 1 — from core concepts like supervised learning to training models with gradient descent — written for both the future and anyone else trying to get a grip on this stuff.