01
AI Foundations
Understand what AI is, how systems learn, where they fail, and how to judge claims without hype.
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177 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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Technical
Multi-Armed Bandits
A multi-armed bandit is a decision problem where you repeatedly choose among options with unknown payoffs and learn as you go, balancing exploring new…
TechnicalBentoML and Model Packaging
BentoML is an open-source Python framework that packages trained machine learning models into standardized, deployable units called 'Bentos'.
TechnicalKServe and Model Serving on Kubernetes
KServe is a standardized, Kubernetes-native platform for serving machine learning models at scale.
TechnicalSeldon Core and Inference Graphs
Seldon Core is an open-source platform for deploying machine learning models on Kubernetes, with a standout feature: inference graphs.
TechnicalVanishing and Exploding Gradients
When training deep networks, error signals shrink toward zero or blow up toward infinity as they travel backward through many layers.
TechnicalGradient Clipping
A simple, widely used safeguard that caps how large gradient updates can get during training.
TechnicalWeight Initialization
How you set a neural network's starting weights before training begins, which strongly shapes whether signals and gradients stay healthy through deep layers.
TechnicalImitation Learning
Imitation learning teaches an AI to perform a task by copying expert demonstrations instead of learning from trial-and-error rewards.
TechnicalInverse Reinforcement Learning
Inverse reinforcement learning (IRL) flips standard RL: instead of being given a reward and finding a policy, it watches expert behavior and infers…
TechnicalOffline Reinforcement Learning
Offline reinforcement learning trains agents purely from a fixed, previously collected dataset, with no live interaction with the environment.
TechnicalSmoothQuant and Activation Quantization
SmoothQuant is a technique that makes it possible to compress large language models down to 8-bit integers for both weights and activations without…
TechnicalStructured Pruning and Layer Dropping
Structured pruning removes whole components of a neural network, such as attention heads, neurons, or entire layers, so the slimmer model runs faster…
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