Finding good resources is easy. Finding free, high-quality ML/AI resources? Insanely difficult—more limited than fossil fuels, honestly. In this video, I compiled the best 100% free resources I’ve personally hunted down over the last year, and I also explain how to navigate them properly (because free resources without a roadmap just becomes chaos). I’m sharing what I wish I had when I was 18, applying for my first ML internship with zero experience and getting rejected left, right, and center. I walk you through free substitutes for expensive Coursera tracks (ML, AI, deep learning—taught by Andrew Ng), plus the full math foundations you actually need: linear algebra, probability & stats, and calculus. I also talk about NPTEL, where you can learn from IIT professors for free—and even earn a certificate via an offline exam that’s genuinely a nice LinkedIn/resume eye-catcher. Then we hit what matters a lot in 2026 interviews: system design (including ML system design), with a free repo you should bookmark immediately. Finally, I share my favorite blogs/LinkedIn people to follow, a goldmine of ML project notebooks (with a strict warning: don’t copy-paste—make variants), and platforms/repos for ML/LLM interview questions and agentic workflows so you can practice like LeetCode, but for AI.