Adversarial ML: Attack & Defend Neural Networks

About this course

This course contains the use of artificial intelligence.

We only charge a fee solely for the time invested in building this comprehensive curriculum.

The "98% Accuracy" Illusion

Every ML team has celebrated a model that hits 95%, 98%, even 99% test accuracy — and then deployed it without ever asking what happens when someone deliberately tries to break it. That's the gap adversarial machine learning exists to close: a model that performs beautifully on clean data can collapse under a perturbation so small a human can't even see it, can be poisoned during training by a handful of mislabeled examples, can leak its own training data through membership inference, or can be copied wholesale by an attacker who does nothing but query its public API a few hundred times. In 2026, these aren't research curiosities — they're operational risks sitting behind fraud detection, identity verification, healthcare triage, and content moderation systems.

This course closes that gap the only way that actually works: by making you build, attack, and defend a real neural network yourself, with every result measured and none of it assumed. You don't learn adversarial ML by reading about FGSM. You learn it by watching your own model's accuracy collapse under an attack you wrote, then engineering a defense and proving — with a shared evaluation harness — whether it actually helped.

The Solution: 100 Labs, One Growing AI Security Platform

Every lab follows the same Zero-Failure workflow: a pre-flight check, a Git checkpoint, and a documented rollback. By Lab 10 you'll have trained a baseline neural network, evaluated it, run your first FGSM attack, visualized the perturbation, built a basic defense, and turned the whole cycle into a regression test — a complete, working milestone before the deeper architecture arrives.

What's InsideModules 1–2 build your workstation and evaluation discipl

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