Hey, I'm a web developer - building UIs with React, TypeScript, Tailwind, and modern web tools. A few months ago, I decided to step into Machine Learning. No prior ML background. Just curiosity and basic Python knowledge.I started Andrew Ng’s Machine Learning Specialization and created my own study plan. Here’s what the first 30 days looked like — the concepts that clicked, the mistakes I made, and what surprised me most. Week 1: Building the Math Foundation I started with prerequisites because they’re essential: Linear Algebra (vectors, matrices, eigenvalues) Calculus (derivatives, partial derivatives, gradients) Probability & Statistics Concept: Why Gradients Matter Gradient descent is the engine behind almost all modern ML. Imagine you’re trying to reach the bottom of a valley in the dark. The gradient tells you the direction and steepness of the slope. You take a small step downhill. ( w = w - learning_rate * gradient ) Enter fullscreen mode Exit fullscreen mode Repeat until you reach the minimum.…