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
Multi-Token Prediction Training
Instead of predicting just the next token, the model is trained to predict several future tokens at once.
Language AISparse Autoencoders for Feature Extraction
Sparse autoencoders crack open the tangled activations inside a neural network into thousands of human-readable features.
Language AIConstrained and Grammar-Guided Generation
Constrained generation forces a language model to produce output that always conforms to a defined structure, like valid JSON, SQL, or a regular expression.
Language AIWatermarking LLM-Generated Text
Watermarking embeds a hidden, statistically detectable signal into text as a language model generates it, so the output can later be identified…
Language AIPerplexity and Language Metrics
Perplexity is the classic score for how 'surprised' a language model is by real text — lower means it predicts words more confidently.
Language AILoRA and Parameter-Efficient Tuning
LoRA lets you customize a giant pretrained model by training only a tiny set of new weights instead of all billions.
Language AIConstitutional AI
Constitutional AI is Anthropic's method for aligning models using a written set of principles — a 'constitution' — so the AI critiques and revises its own…
Language AIIn-Context Learning
In-context learning is the surprising ability of large language models to pick up a new task from a few examples placed in the prompt, without any retraining.
Language AIInstruction Tuning
Instruction tuning is the training step that turns a raw text-predictor into a model that actually follows instructions like 'summarize this' or 'write…
Language AIAI Hallucinations
An AI hallucination is when a model states something false as if it were true — a fake citation, a made-up statistic, a wrong fact — fluently and confidently.
Language AITemperature and Sampling
Temperature and sampling are the dials that control how 'random' or 'safe' a language model's wording is.
Language AIWord Embeddings
Word embeddings turn words into lists of numbers so that words used in similar ways end up close together in a mathematical space.
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