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AI Foundations
Understand what AI is, how systems learn, where they fail, and how to judge claims without hype.
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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
Direct Preference Optimization
Direct Preference Optimization (DPO) is a way to align language models with human preferences without training a separate reward model or running…
Language AIReward Modeling
A reward model is a neural network trained to predict how good an AI response is, acting as an automated stand-in for human judgment.
Language AIProximal Policy Optimization
Proximal Policy Optimization (PPO) is the reinforcement learning algorithm most associated with fine-tuning language models from human feedback.
Language AIBeam Search
Beam search is a decoding strategy that keeps the several most promising partial sequences at each step instead of greedily committing to one.
Language AINucleus and Top-k Sampling
Nucleus (top-p) and top-k sampling are decoding methods that add controlled randomness to text generation by restricting which tokens can be chosen.
Language AIMasked Language Modeling
Masked language modeling teaches an AI to fill in deliberately hidden words using the full surrounding context, both left and right.
Language AINext-Token Prediction
Next-token prediction is the deceptively simple objective behind GPT-style models: given everything so far, guess the next chunk of text.
Language AISequence-to-Sequence Models
Sequence-to-sequence models map one sequence to another of possibly different length, like translating a sentence or summarizing a document.
Language AIEncoder-Decoder Architectures
Encoder-decoder architectures split a model into two halves: one that reads and compresses an input into a rich internal representation, and one…
Language AICross-Attention
Cross-attention is the mechanism that lets one sequence look at another: a decoder generating text can attend to an encoder's representation of the input.
Language AICatastrophic Forgetting
Catastrophic forgetting is when a neural network learns a new task and abruptly loses its ability to perform tasks it had already mastered.
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