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
Superposition and Polysemanticity in AI Interpretability
Learn superposition and polysemanticity in AI interpretability: why models pack features into shared directions, why neurons become polysemantic…
TechnicalLogit Lens and Tuned Lens
The logit lens and tuned lens are interpretability techniques that peek at a transformer's hidden states layer by layer to see what the model is 'thinking'…
Language AIWord2Vec 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…
TechnicalDeepSpeed and Megatron Training Stacks
DeepSpeed (Microsoft) and Megatron-LM (NVIDIA) are the software stacks that make training models with billions of parameters across thousands of GPUs…
Technical1-Bit and Ternary BitNet Models
BitNet is Microsoft's line of research showing that large language models can be trained with weights restricted to just 1 bit, or three values…
TechnicalGPTQ and AWQ Post-Training Quantization
GPTQ and AWQ are two leading methods for shrinking already-trained language models to 4-bit precision so they run on cheaper, smaller hardware.
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…
Visual AINull-Text Inversion
Null-text inversion is a technique that lets you edit a real photo with a text-driven diffusion model like Stable Diffusion while keeping everything you…
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