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.
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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
Meta-Learning
Meta-learning, or 'learning to learn,' trains models to adapt quickly to brand-new tasks from only a handful of examples.
FundamentalsEnsemble Methods and Gradient Boosting
Ensemble methods combine many simple models so the group makes better predictions than any single model.
FundamentalsBayesian Deep Learning
Bayesian deep learning treats a neural network's weights as probability distributions rather than fixed numbers, so the model can say how confident it is.
FundamentalsCurriculum Learning
Curriculum learning trains AI models on examples in a deliberate order — easy first, hard later — instead of feeding data in random order.
FundamentalsNeural Architecture Search
Neural Architecture Search (NAS) automates the design of neural network structures — letting algorithms, not humans, decide how many layers, what operations…
FundamentalsContinual Learning and Catastrophic Forgetting
Continual learning is the goal of training AI on a stream of new tasks over time without erasing what it already knows.
FundamentalsVariational Autoencoders
Variational autoencoders (VAEs) are generative neural networks that learn to compress data into a smooth, probabilistic latent space and then reconstruct…
FundamentalsState Space Models and Mamba
State space models (SSMs) are sequence models that carry information forward through a compressed hidden state, scaling linearly with sequence length instead…
FundamentalsGraph Neural Networks
Graph neural networks (GNNs) are models that learn directly on graph-structured data — nodes connected by edges — by passing and aggregating information…
FundamentalsMulti-Agent Reinforcement Learning
Multi-Agent Reinforcement Learning (MARL) trains several learning agents that share an environment, each adapting its behavior while the others adapt too.
FundamentalsActive Learning
Active learning is a training strategy where the model itself chooses which unlabeled examples a human should label next.
FundamentalsData Augmentation
Data augmentation artificially expands a training set by creating modified copies of existing examples — like flipping or cropping images.
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