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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.
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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
Cross-Validation
Cross-validation is a resampling technique for estimating how well a model will generalize to unseen data.
FundamentalsPrecision and Recall
Precision and recall are two complementary metrics for evaluating classifiers, especially when classes are imbalanced.
FundamentalsDouble Descent Phenomenon
Double descent is the surprising observation that as a model gets bigger, test error first gets worse near the 'interpolation threshold' but then gets better…
FundamentalsNeural Tangent Kernel Theory
The Neural Tangent Kernel (NTK) is a mathematical tool showing that infinitely wide neural networks behave like a specific, fixed kernel method during…
FundamentalsLottery Ticket Hypothesis
The Lottery Ticket Hypothesis says that inside a large, randomly initialized neural network hides a small subnetwork — a 'winning ticket' — that, trained…
FundamentalsScaling Laws for Neural Networks
Scaling laws are empirical formulas showing that a neural network's loss falls predictably as you grow model size, dataset size, and compute.
FundamentalsChinchilla Compute-Optimal Training
Chinchilla is a 2022 DeepMind finding that most large language models were badly undertrained: for a fixed compute budget you should scale parameters…
FundamentalsGrouped Reward Normalization in RLHF
Grouped reward normalization standardizes a model's rewards within a batch of responses to the same prompt, turning noisy scores into a stable training…
FundamentalsWorld Models and Learned Simulators
A world model is a neural network that learns to predict how an environment changes over time, letting an AI 'imagine' future outcomes before acting.
FundamentalsLength Normalization in Preference Optimization
Length normalization adjusts preference-tuning objectives so models stop winning approval just by writing longer answers.
FundamentalsBradley-Terry Reward Modeling
The Bradley-Terry model is a century-old statistical method for turning pairwise comparisons (A beats B) into numeric scores.
FundamentalsIterative DPO and Online Preference Tuning
Iterative DPO repeatedly aligns a language model to human or AI preferences by generating fresh responses, ranking them, and tuning on those new pairs each…
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