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
Self-Play Fine-Tuning
Self-play fine-tuning improves a model by having it compete against or learn from its own past outputs, generating its own training signal.
FundamentalsTest-Time Training
Test-time training (TTT) lets a model keep learning from each new input at the moment it makes a prediction, instead of staying frozen after training.
FundamentalsGrokking and Delayed Generalization
Grokking is a startling phenomenon where a neural network first memorizes its training data, sits at near-zero validation accuracy for a long time, and then…
FundamentalsOverfitting and Underfitting
Overfitting is when a model memorizes its training data and fails on new examples; underfitting is when it is too simple to capture the real pattern.
FundamentalsRegularization
Regularization is a set of techniques that deliberately constrain a model so it generalizes to new data instead of memorizing the training set.
FundamentalsBackpropagation
Backpropagation is the algorithm that lets a neural network learn from its mistakes by efficiently calculating how much each weight contributed to the error.
FundamentalsGradient Descent
Gradient descent is the optimization method that actually moves a model's weights downhill toward lower error, one small step at a time.
FundamentalsLoss Functions
A loss function is the single number that tells a model how wrong its predictions are, turning a vague goal into something math can optimize.
FundamentalsActivation Functions
Activation functions are the small nonlinear gates inside each neuron that let neural networks learn complex, curved patterns instead of just straight lines.
FundamentalsConvolutional Neural Networks
Convolutional Neural Networks (CNNs) are the workhorse architecture for understanding images.
FundamentalsRecurrent Neural Networks
Recurrent Neural Networks (RNNs) are built to handle sequences like text, speech, and time series.
FundamentalsGenerative Adversarial Networks
Generative Adversarial Networks (GANs) create realistic new data by pitting two neural networks against each other in a contest.
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