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A free, lesson-based curriculum for building AI agents in Python from first principles: agentic frameworks, design patterns, tool use, RAG, planning, multi-agent systems, memory, and protocols like MCP and A2A. Hands-on, with Azure AI and Semantic Kernel.
Open textbook for upper-level undergraduates that explains computational principles behind autonomous robots — mechanisms, sensors, actuators, perception, and planning — with exercises and simulation assets. Distributed as LaTeX source under a CC-BY-NC-ND license and accompanied by course materials and Webots examples.
Provides a complete, university-level computer science curriculum assembled from free online courses and books. Curates degree-aligned course sequences (Intro / Core / Advanced) with community support, project guidance, and checklists to track progress for self-directed learners.
Stanford's course teaches deep learning by making you build vision models from scratch — k-NN and linear classifiers up through CNNs, detection, segmentation, and Transformers — with three PyTorch assignments and a self-chosen final project.
Condenses Stanford's CS 229 into one-page visual cheatsheets spanning supervised, unsupervised, and deep learning, plus probability and linear-algebra refreshers. Available in 10+ languages, with all topics merged into one Super VIP PDF.
Notebook-first deep learning textbook that teaches concepts through runnable multi-framework code, math, and exercises. Includes lecture-ready notebooks, community contributions, and broad university adoption—designed for hands-on learners and instructors.
Teaches classic machine learning through a 12-week, 26-lesson curriculum with quizzes, written lessons, assignments, projects, and multilingual translations.
A 12-week, 24-lesson beginner-friendly AI curriculum with executable Jupyter notebooks, quizzes and labs that teach neural networks, computer vision, NLP, generative models and ethics using PyTorch and TensorFlow examples.
Hands-on lecture series that teaches neural networks from first principles up to building a GPT: each lecture pairs a YouTube video with Jupyter notebooks and exercises so you code models (micrograd → MLPs → WaveNet-like convs → GPT) while learning training and debugging.
Teaches generative AI app development through 21 lessons covering LLM basics, prompting, chat, search, image generation, agents, RAG, fine-tuning, small models, and responsible AI.
A free, open textbook on engineering ML systems — building efficient, reliable AI from a single GPU up to warehouse-scale clusters. Goes beyond model design and MLOps tooling to the underlying science: scheduling, quantization, data pipelines, serving.
Nine-chapter course teaching prompt engineering for Claude: from basic prompt structure through roles, output formatting, and hallucination control to complete prompts for chatbot, legal, finance, and coding tasks. Runs as editable Jupyter notebooks.