Learning Resources – AI and Quantum International Hub

AI and Quantum Learning Resources

Every resource below is free to access and comes from a major lab, university, or UN body. These are organized as paths rather than a flat list, so you can work through them in order rather than collecting browser tabs.

AI Programs

Path A: AI Literacy (no coding required, 10–15 hours)
Start here if you’re new to the field.

AI Fluency: Frameworks & Foundations — Anthropic. A structured course on working effectively and responsibly with AI systems, not just prompting tricks.
OpenAI Academy — OpenAI. Free, self-paced tracks organized by role: educator, nonprofit, government, small business, developer.
Google AI Essentials — Google. Under 10 hours of hands-on activities for total beginners. Free to learn; the certificate is paid with financial aid available.
Microsoft AI Skills Navigator — Microsoft. Routes you to the right learning path based on your role and starting point.
Elements of AI — University of Helsinki. The original mass-market AI literacy course, still one of the best written.

Path C: Machine Learning Fundamentals (technical)

MIT OCW 18.06 Linear Algebra — MIT. Gilbert Strang’s course; the prerequisite almost everyone skips and later regrets.
Machine Learning Crash Course — Google. Interactive, with real code exercises.
ML-For-Beginners and AI-For-Beginners — Microsoft. Full 12- and 24-week curricula on GitHub.
Stanford CS229: Machine Learning — Stanford. Lecture notes and problem sets published openly.
Stanford CS231n: Deep Learning for Computer Vision — Stanford.
MIT 6.S191: Introduction to Deep Learning — MIT. Refreshed and re-recorded every year.

Path B: Ethics, Policy and Governance
Run this alongside Path A rather than after it.

AI and the Rule of Law — UNESCO and the University of Oxford. Launched globally in April 2026.
Defending Human Rights in the Age of Artificial Intelligence — UNITAR and UNESCO. A microlearning course available in 25+ languages.
Educating in the Age of AI: Digital Citizenship at School — UNESCO. Now running in English as well as Spanish.
AI for Good — ITU (United Nations). Free webinars, summit recordings, and learning tracks.
Stanford HAI — Stanford. Policy briefs, the annual AI Index Report, and free seminar recordings.

Path D: Building with LLMs and Agents

Anthropic Academy and the Anthropic Cookbook — Anthropic. API fundamentals, prompt engineering, tool use, and Model Context Protocol.
Generative AI for Beginners, AI Agents for Beginners, and MCP for Beginners — Microsoft.
OpenAI Cookbook — OpenAI. Working code recipes for retrieval, evals, and agent patterns.
Google Cloud Skills Boost — Generative AI — Google. Free tier covers the introductory generative AI path.
NVIDIA Deep Learning Institute — NVIDIA. Several self-paced courses are free, including Building RAG Agents with LLMs and Getting Started with Deep Learning.
IBM SkillsBuild — IBM. Free AI fundamentals and generative AI modules with digital badges.

Quantum Programs

Path A: Quantum Literacy (no physics background)

IBM Quantum Learning — IBM. Begin with Basics of Quantum Information. The best-maintained free quantum curriculum anywhere.
Quantum Computing Fundamentals — Microsoft Learn.
WISER On-Demand Learning — WISER. Short courses spanning quantum science, engineering, computing, and careers, from school level through research depth. Several are free.
Google Quantum AI — Google. Research explainers, hardware roadmap, and education resources.
International Year of Quantum — UNESCO-endorsed. Public resource hub and event archive.

Path C: Programming Quantum Computers

Qiskit tutorials and textbook — IBM. Run circuits on real hardware through the free tier.
Qiskit Global Summer School — IBM. A free, fully virtual two-week program with recorded lectures, guided coding labs, live Q&As with IBM Quantum researchers, and a global Discord community. The 2026 edition added a beginners-only track. Registration fills in under a week, so watch for the next announcement.
Quantum Katas — Microsoft. Open-source, self-paced programming exercises in Q#.
Cirq tutorials — Google.
PennyLane Codebook — Xanadu. Browser-based, and the strongest free on-ramp to quantum machine learning.
NVIDIA CUDA-Q — NVIDIA. Hybrid quantum-classical programming, with free DLI modules.

Path B: Mathematical and Physical Prerequisites

Unlike the AI track, this one is genuinely a gate. Attempting Path C without linear algebra and complex numbers is where most people stall.

MIT OCW 18.06 Linear Algebra — MIT.
MIT OCW 8.04 Quantum Physics I — MIT.
The Theoretical Minimum: Quantum Mechanics — Leonard Susskind, Stanford. Free full lecture series.

Path D: Algorithms, Error Correction and Research

MIT OCW Quantum Information Science — MIT, taught by Peter Shor.
Stanford CS269Q: Elements of Quantum Computer Programming — Stanford.
IBM Quantum Learning advanced modules — quantum error correction, phase estimation, and variational algorithms.
WISER Quantum Algorithms — WISER. Recorded instruction plus live expert-led sessions with WISER and Fraunhofer researchers.

Path E: Where AI and Quantum Converge

WISER Summer Program — WISER. A six-week live program bringing classical methods, AI, and quantum together around optimization: what each approach is good at, where it breaks, and how to use them together. Free access is available through the Elevate Quantum regional code; student and scholar places require an application.
NVIDIA CUDA-Q — hybrid workflows that put GPUs and QPUs in the same pipeline.
PennyLane demos — Xanadu. Runnable quantum machine learning notebooks.

Note: For AI, work through Path A, then C, then D, and treat Path B as parallel reading rather than a checkpoint. For quantum, Path B really is a prerequisite. Pick two resources, not twelve. Finish one project with each. In a field moving this fast, a portfolio of things you have actually built carries more weight than a folder of certificates.

Program dates and free-tier terms change frequently. Verify enrollment windows on each provider's page before planning around them.

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