The Math Behind AI | AI Explained (Part 2)

AI is built on math — but you don't need a math degree to understand it.

This is Part 2 of AI Explained — the foundation series that builds Machine
Learning, Neural Networks, and Generative AI from the ground up, in plain
English.

In this episode, we tackle the first and most fundamental pillar of AI math:
Linear Algebra. We start with the simplest equation you've ever seen — y = mx + b —
and work our way up to the matrix equation that powers every modern AI system
on the planet.

No formulas to memorize. No long calculations. Just the intuition behind
how AI actually thinks in numbers.

By the end, you'll understand:

📐 The four pillars of AI math — and why they matter
📏 What y = mx + b really means (and why it's the start of AI)
⚖️ How "weights" and "bias" connect to a straight line
🧮 Why one input becomes many — and how the math scales
🔢 How a data table becomes a matrix — and why it's everywhere in AI

This series doesn't rush into Python. We build the intuition first. Then
the code feels natural — not magical.

🔔 Subscribe for the rest of the series — Calculus, Probability, Neural
Networks, CNNs, Transformers, and Generative AI, all from scratch.

📺 Watch Part 1 first: How Machine Learning Actually Works
[paste your Part 1 URL here]

— Chapters —
0:00 Intro
1:23 The four pillars
2:28 Linear Algebra
2:49 y = mx + b
4:05 When one input isn't enough
4:55 Matrix form
5:33 The heartbeat of modern AI

#AIExplained #LinearAlgebra #MachineLearning #AI #NeuralNetworks #DeepLearning #AIForBeginners #MathForAI #FromScratch

Видео The Math Behind AI | AI Explained (Part 2) канала PiOrbit