01
AI Foundations
Understand what AI is, how systems learn, where they fail, and how to judge claims without hype.
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Five outcome-based courses
Each course includes explicit outcomes, mapped competencies, practice activities, and an applied capstone.
02
Responsible AI User
Use AI productively while protecting privacy, checking outputs, and preserving human accountability.
03
AI at Work
Evaluate workplace use cases, run safe pilots, measure value, and communicate changes responsibly.
04
AI Policy & Society
Analyze AI systems through rights, equity, governance, safety, and public-interest outcomes.
05
Building with AI Systems
Understand language models, retrieval, agents, evaluation, cost, and deployment safeguards through practical system design.
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FundamentalsCore concepts: how AI works at a high level.84 guides Language AILLMs, NLP, ChatGPT, prompting, and text understanding.155 guides Visual AIImage generation, computer vision, video, and design.133 guides Audio AIVoice, music, podcasting, and audio-first AI.117 guides CompaniesProfiles of OpenAI, Anthropic, Google, Meta, and more.121 guides ApplicationsHow AI shows up in coding, search, agents, and daily life.111 guides IndustriesAI in healthcare, finance, education, energy, and more.91 guides TechnicalChips, fine-tuning, RAG, vectors, and AI infrastructure.177 guides SocietyBias, privacy, copyright, safety, and the AI future.30 guides
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Fundamentals
AI Evaluation Basics
AI evaluation tests whether a system meets a defined purpose under stated conditions.
FundamentalsHuman-AI Collaboration
Human-AI collaboration divides work between people and AI systems while keeping responsibility and control clear.
FundamentalsAI Decision-Making
AI can supply predictions, organize evidence, or recommend actions, but choosing an action also requires goals, constraints, and responsibility.
FundamentalsAI Failure Modes
An AI failure mode is a repeatable way a system can produce an unacceptable result.
Language AILLM Evaluations
LLM evaluation measures a language model or application against defined tasks and failure conditions.
Language AIModel Context Protocol
Model Context Protocol, or MCP, defines a common interface through which an AI host can connect to servers offering tools, resources, and prompts.
Language AITool Calling
Tool calling lets a model request an operation through a defined interface.
Language AIAI Summarization
AI summarization creates a shorter representation of source material.
Language AIRetrieval Quality
Retrieval quality measures whether a search system returns useful evidence for a query and places it where a reader or downstream model can use it.
Language AIStructured Outputs
Structured outputs organize model responses into a defined shape, such as a JSON object validated against a schema.
Language AIMultilingual LLMs
A multilingual language model works with more than one language using shared learned representations.
Visual AIVisual Reasoning
Visual reasoning involves answering questions about relationships, quantities, spatial arrangements, or other information in visual material.
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