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Anthropic's own free course catalog covering Claude, the Claude API, Model Context Protocol and AI fluency, with a certificate of completion for each course.
Free
Machine learning, deep learning, and modern AI, from first concepts to practice.
12 resources
Anthropic's own free course catalog covering Claude, the Claude API, Model Context Protocol and AI fluency, with a certificate of completion for each course.
Machine learning and AI workload concepts taught against real Azure services — computer vision, language, and generative AI — with a free guided path to the exam.
The full developer course: messages, streaming, tool use, prompt caching and structured outputs, taught against the real API.
The starting point: using Claude for everyday work, its core features, and how to prompt it well. No coding required, certificate on completion.
Using Claude Code in a real development workflow — driving it from the terminal, giving it context, and reviewing what it produces.
Building MCP servers and clients — the open standard for connecting AI assistants to tools and data sources, from its authors.
The University of Helsinki and MinnaLearn's famous free introduction to what AI is and is not — no math or programming required, available in 26 languages including French.
Grant Sanderson's beautifully animated video series building neural networks and gradient descent from visual intuition — free to watch, a legendary starting point.
Google's fast-paced practical introduction to machine learning with video lectures, visualizations and hands-on exercises; site content published under a Creative Commons BY 4.0 license.
Refined machine learning cheat sheets for Stanford's CS 229 — supervised, unsupervised, deep learning — by Afshine and Shervine Amidi, MIT-licensed with official French translations.
Free courses on NLP, LLMs, diffusion models and reinforcement learning from Hugging Face, with open-source course materials released under the Apache 2.0 license.
A free, open-source interactive deep learning book with runnable code in PyTorch, JAX and TensorFlow, by Aston Zhang and colleagues — adopted at 500+ universities.