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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Language AI
Chinchilla Scaling Laws
The Chinchilla scaling laws, from DeepMind in 2022, showed that most large language models were badly undertrained: for a fixed compute budget, you should…
Language AISelf-Refine Iterative Output Improvement
Self-Refine is a prompting technique where a language model critiques its own output and rewrites it, looping until the answer improves.
Language AILeast-to-Most Prompting
Least-to-Most prompting breaks a hard problem into a sequence of simpler subproblems, solving them in order so each answer feeds the next.
Language AIAnalogical and Step-Back Prompting
Analogical and Step-Back prompting both guide a model to reason at a higher level first: analogical prompting has it recall similar solved problems…
Language AIRelation Extraction from Text
Relation extraction pulls structured facts out of unstructured text, identifying how two entities connect (like 'works for' or 'located in').
Language AISemantic Role Labeling
Semantic role labeling (SRL) answers 'who did what to whom, when, where, and why' by tagging the roles each phrase plays around a verb.
Language AIDependency Parsing
Dependency parsing maps the grammatical structure of a sentence as a tree of word-to-word relationships, showing which words depend on which.
Language AITokenizer-Free Byte-Level Models
Tokenizer-free models drop the fixed vocabulary of word-pieces and operate directly on raw bytes, letting one model handle any language, code, or even noisy…
Language AIFunction Vectors and Task Representations
Function vectors are compact directions inside a language model's hidden states that encode an entire task, like 'translate to French' or 'return the antonym.
Language AIBest-of-N Sampling and Reranking
Best-of-N sampling generates several candidate answers from a model and then picks the best one using a separate scoring step.
Language AINatural Language Inference and Entailment
Natural language inference asks whether one sentence logically follows from another.
Language AISlot Filling and Intent Detection
Intent detection figures out what a user wants, and slot filling extracts the specific details needed to act on it.
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