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
Word2Vec Skip-Gram and CBOW
Word2Vec is a 2013 technique from Google that learns dense word vectors by predicting words from their neighbors, turning language into geometry where…
Language AIGloVe Global Vectors
GloVe (Global Vectors for Word Representation) is a 2014 Stanford embedding method that learns word vectors directly from global co-occurrence counts across…
Language AIFastText Subword Embeddings
FastText is a 2016 Facebook AI method that represents each word as a bag of character n-grams, so it can build vectors even for words it never saw during…
Language AIELMo Contextual Embeddings
ELMo (Embeddings from Language Models) was a 2018 breakthrough that gave each word a representation shaped by its sentence, so 'bank' in 'river bank' differs…
Language AIMatryoshka Representation Embeddings
Matryoshka Representation Learning (MRL) trains embeddings so the most important information is packed into the first dimensions, letting you truncate a long…
Language AIColBERT and Multi-Vector Retrieval
ColBERT represents each document and query as many token-level vectors instead of one, then scores relevance by matching every query token to its best…
Language AIConstituency Parsing
Constituency parsing breaks a sentence into nested phrases like noun phrases and verb phrases, revealing its grammatical structure as a tree.
Language AILemmatization and Stemming
Stemming and lemmatization both reduce words to a base form so that 'running', 'ran', and 'runs' can be treated as one concept.
Language AITF-IDF and Bag-of-Words Models
Bag-of-words turns text into word counts ignoring order, and TF-IDF weights those counts so rare, distinctive words matter more than common ones.
Language AICross-Encoders vs Bi-Encoders
Two ways neural models compare text: bi-encoders embed each piece separately for fast search, while cross-encoders read both texts together for higher…
Language AIAbstractive vs Extractive Summarization
Two strategies for shrinking text: extractive summarization copies the most important sentences verbatim, while abstractive summarization writes new…
Language AIProcess Supervision for Math Reasoning
Process supervision rewards a model for every correct step in a chain of reasoning, not just the final answer.
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