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
Kahneman-Tversky Optimization
Kahneman-Tversky Optimization (KTO) is an alignment method that learns from simple thumbs-up or thumbs-down labels instead of paired comparisons.
Language AIRejection Sampling Fine-Tuning
Rejection Sampling Fine-Tuning (RFT) generates many candidate answers, keeps only the best-scoring ones, and retrains the model on those winners.
Language AIProcess Reward Models
Process reward models (PRMs) score each individual step of an AI's reasoning rather than just the final answer.
Language AIMixture-of-Agents Aggregation
Mixture-of-Agents (MoA) is a technique where several language models draft answers and then an aggregator model fuses their best ideas into one improved…
Language AIAdapter Layers for Transfer
Adapter layers are tiny trainable modules inserted into a frozen pretrained model, letting you adapt it to new tasks by updating only a few percent…
Language AIMulti-Head Latent Attention
Multi-Head Latent Attention (MLA) is an attention mechanism, introduced in DeepSeek-V2, that compresses the memory-hungry key-value cache into a small shared…
Language AIMedusa Decoding Heads
Medusa is a speculative-decoding method that bolts several extra prediction 'heads' onto a language model so it can guess multiple future tokens at once.
Language AIMixture of Depths
Mixture of Depths (MoD) lets a transformer spend different amounts of compute on different tokens, routing only the 'important' tokens through each layer's…
Language AIMamba and Selective State Spaces
Mamba is a sequence model built on state space models (SSMs) that processes text in linear time, offering a fast alternative to the Transformer's quadratic…
Language AIRWKV Linear Attention
RWKV is an architecture that trains like a Transformer but runs inference like a recurrent network, giving linear-time, constant-memory generation.
Language AISparse Attention Patterns
Sparse attention makes Transformers cheaper by letting each token attend to only a carefully chosen subset of other tokens rather than all of them.
Language AIQLoRA and 4-Bit Fine-Tuning
QLoRA is a technique that lets you fine-tune a massive language model on a single consumer GPU by storing the frozen model in just 4 bits per weight.
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