The most-recognized non-technical AI course on the planet. Andrew Ng explains what AI can and cannot do, and how to apply it strategically in any organization — no coding required.

The gold standard of ML education. A three-course specialization covering supervised, unsupervised learning, and reinforcement learning with hands-on Python labs — taught by Andrew Ng.

Ng's flagship for non-engineers. Covers how LLMs work, prompt engineering, RAG, and real business use cases across marketing, operations, and product teams.

Teaches the four foundational agentic AI patterns — Reflection, Tool Use, Planning, and Multi-Agent collaboration — with practical code examples across all major frameworks.

MIT's annual deep learning flagship, refreshed every January. The 2026 edition adds expanded LLM and agentic AI coverage across neural nets, computer vision, generative models, and RL.

12-course path from Google engineers covering LLMs, image generation, responsible AI, and Gemini API development. Earns verifiable Google digital badges per completed module.

Written by the engineers who built the Transformers library. Covers LLM training, RLHF, fine-tuning, and deployment across the Hugging Face ecosystem. Continuously updated as models evolve.

A European university staple with over 1 million completions. No-code, concept-first course covering AI reasoning, ethics, machine learning, and neural networks with broad real-world context.

Microsoft's official AI foundations path covering embeddings, RAG, responsible AI, NLP, computer vision, and Azure AI deployment. Free path; optional paid exam for the official badge.

Stanford's full graduate ML course — the mathematical bedrock of modern AI. Covers linear models, SVMs, deep learning, unsupervised learning, and RL with full mathematical derivations.