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I've tested every FREE AI course from MIT, Stanford, DeepMind, and Berkeley. 8 survived. $120K worth of education. $0. Most engineers hoard 50 courses and finish none. This stack is sequenced so you finish all 8: 1️⃣ Foundations: MIT 6.S191 Deep learning from zero. Neural nets, CNNs, transformers, generative models. The fastest on-ramp that exists. 10 lectures. Skip: Andrew Ng's older Coursera courses. This covers more in less time. → youtube.com/playlist?list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI 2️⃣ Build a GPT from scratch: Karpathy's Zero to Hero Backprop by hand. Tokenization. Attention. Build GPT from an empty file. Skip: every "transformer explained" blog post. This is the source. → karpathy.ai/zero-to-hero.html 3️⃣ Language models deep: Stanford CS336 Data preprocessing. Scaling laws. Evals. Reasoning. The course most ML engineers wish existed when they started. Skip: generic "intro to LLMs" content. This assumes you can code. → http://cs336.stanford.edu 4️⃣ Computer vision: UMich Deep Learning for CV CNNs to modern vision architectures. Detection. Segmentation. Generation. Skip: OpenCV tutorials. This teaches the models, not the library. → youtube.com/playlist?list=PL5-TkQAfAZFbzxjBHtzdVCWE0Zbhomg7r 5️⃣ Generative AI: Stanford CS236 Diffusion models. VAEs. Flows. Image synthesis. The theory behind Stable Diffusion and DALL-E explained properly. Skip: "how to use Midjourney" content. This teaches how it works. → youtube.com/playlist?list=PLoROMvodv4rPOWA-omMM6STXaWW4FvJT8 6️⃣ Reinforcement learning: DeepMind x UCL RL Course Policy optimization. Value learning. Planning. RL powers reasoning in every frontier model. This is the foundation. Skip: OpenAI Gym toy tutorials. This is production RL theory. → youtube.com/playlist?list=PLqYmG7hTraZBKeNJ-JE_eyJHZ7XgBoAyb 7️⃣ AI agents: Berkeley LLM Agents Planning engines. Tool use. Reasoning systems. Taught by practitioners who've built real agent systems. Skip: LangChain YouTube tutorials. This teaches architecture, not wrappers. → youtube.com/playlist?list=PLS01nW3RtgopsNLeM936V4TNSsvvVglLc 8️⃣ ML systems: Stanford MLSys Seminars System architecture. Productionization. Performance tuning. The bridge between "my model works in a notebook" and "my model runs in production." Skip: MLOps certification programs. This is free and better. → youtube.com/playlist?list=PLSrTvUm384I9PV10koj_cqit9OfbJXEkq 💸 CS degree: $120,000+ 💸 This stack: $0 The engineers learning from these playlists aren't paying $120K. They're outperforming the ones who did. The order matters. Each course builds on the last. Which course are you starting? 👇 💾 Bookmark this before you waste money on another bootcamp. ♻️ Repost for someone still paying for AI education that's free