Machine Learning might sound complex, but at its core, it's powered by math. Whether it's calculating gradients, scaling vectors, or multiplying matrices, having a solid grasp of the underlying mathematics can take you from just using models to truly understanding them.
In this blog, I break down the core mathematical concepts that form the foundation of ML:
📐 Scalars, Vectors, and Matrices
➕ Vector Operations and Dot Product
✖️ Matrix Multiplication
🧮 Derivatives and Gradient Descent
📊 Probability & Statistics essentials
All explained in a beginner-friendly way using JavaScript, so you can immediately try things out as you learn.
This post is perfect for:
JavaScript developers curious about ML
Beginners looking to understand the “why” behind the math
Anyone who prefers learning by coding instead of just theory
🔗 Read the full blog here:
👉 Mathematical Foundations for Machine Learning in JavaScript
