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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Technical
Knowledge Distillation
Knowledge distillation trains a small 'student' model to imitate a large, accurate 'teacher' model.
TechnicalMixture of Experts
Mixture of Experts (MoE) is a model design that splits a network into many specialized sub-networks and activates only a few per input.
FundamentalsEmbeddings
Embeddings turn words, images, or other data into lists of numbers (vectors) so that similar things end up close together in a high-dimensional space.
FundamentalsTokenization
Tokenization is the step that chops text into smaller pieces called tokens, the units a language model actually reads and predicts.
FundamentalsFeature Engineering
Feature engineering is the craft of turning raw data into informative inputs (features) that help a model learn.
TechnicalModel Quantization
Model quantization shrinks a neural network by storing its numbers in fewer bits, so the same model runs faster and on smaller hardware.
TechnicalFederated Learning
Federated learning trains a shared model across many devices or organizations without collecting their raw data in one place.
FundamentalsDecision Trees and Random Forests
A decision tree makes predictions by asking a series of simple yes/no questions, like a flowchart.
FundamentalsSupport Vector Machines
A support vector machine (SVM) is a classic algorithm that separates two groups by drawing the widest possible boundary between them.
FundamentalsDimensionality Reduction
Dimensionality reduction shrinks data from many columns (features) down to a few while keeping the important structure.
FundamentalsTransfer Learning
Transfer learning reuses a model already trained on a large dataset and adapts it to a new, related task.
FundamentalsSelf-Supervised Learning
Self-supervised learning trains models on unlabeled data by inventing a task whose answer is hidden inside the data itself.
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