01
AI Foundations
Understand what AI is, how systems learn, where they fail, and how to judge claims without hype.
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84 plain-English guides, structured learning paths, and an open library — built by an independent 501(c)(3) nonprofit so anyone can understand modern AI.
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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
What is AI?
Artificial intelligence (AI) is the field of building computer systems that perform tasks such as recognizing patterns, understanding language, planning…
FundamentalsHow AI Learns
Machine-learning systems learn by adjusting a model using data and a training objective.
FundamentalsNeural Networks
A neural network is a machine-learning model made of connected mathematical operations with adjustable parameters.
FundamentalsDeep Learning
Deep learning is a branch of machine learning that uses neural networks with multiple layers to learn representations of data.
FundamentalsAI Training
AI training is the process of adjusting a machine-learning model using examples and a learning objective.
FundamentalsAI Inference
Inference is using a trained model to produce an output from a new input.
FundamentalsAI & Data
Data is the recorded information a machine-learning system learns from or processes.
FundamentalsAI Models Explained
A machine-learning model is a mathematical system that maps inputs to outputs using a structure and learned parameters.
FundamentalsMachine Learning Basics
Machine learning builds models whose behavior is fitted from examples rather than written entirely as explicit rules.
FundamentalsSupervised Learning
Supervised learning fits a model using examples that pair inputs with target outputs.
FundamentalsUnsupervised Learning
Unsupervised learning looks for structure in data without a target label for every example.
FundamentalsGenerative AI
Generative AI produces outputs such as text, images, audio, or code using learned statistical patterns and supplied context.
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