AI has changed quickly in the last few years. This page highlights practical resources for learning modern AI, building with foundation models, following research, and understanding responsible AI.
Instead of focusing only on traditional machine learning, I wanted this page to reflect the way people learn AI today: starting with fundamentals, then moving into LLMs, multimodal models, prompt design, evaluations, and AI governance.
This page is for students, analysts, marketers, data practitioners, and builders who want a practical way to make sense of modern AI. Some resources are better for learning concepts, while others are more useful when you are ready to build applications or follow industry changes.
A short introduction to core AI ideas and how machine learning fits into the broader AI landscape.
A practical, updated introduction to core ML concepts such as regression, classification, neural networks, embeddings, large language models, and fairness.
Hands-on tutorials and exercises for machine learning, Python, data visualization, and model building.
Free courses covering LLMs, transformers, agents, diffusion models, and other modern AI topics.
Short, practical courses on prompt engineering, multimodal AI, RAG, agents, and other current generative AI workflows.
A practical guide to writing stronger prompts, managing prompt versions, and improving output quality for production use cases.
Useful if you are building AI products and need a better way to test quality, reliability, and regressions.
A good starting point for semantic search, recommendations, clustering, and retrieval-augmented generation workflows.
A clear reference for building agent-style workflows that call tools, retrieve data, and complete multi-step tasks.
A practical walkthrough for building web apps with multimodal generative AI using the Gemini API.
One of the best yearly snapshots of the AI landscape, covering model performance, adoption, investment, policy, and public sentiment.
A strong resource for connecting research papers to code implementations and state-of-the-art benchmark results.
Helpful for tracking open model evaluations and comparing model performance across benchmarks.
A direct feed of new AI papers if you want to follow research more closely.
A practical framework for thinking about trustworthy AI, risk management, and deployment decisions in real organizations.
Includes supporting resources and the generative AI profile for teams working with modern foundation models.
Research, convenings, and practical guidance on how AI affects workers, media, safety, and society.