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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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Fundamentals
Active Learning
Active learning is a training strategy where the model itself chooses which unlabeled examples a human should label next.
FundamentalsData Augmentation
Data augmentation artificially expands a training set by creating modified copies of existing examples — like flipping or cropping images.
TechnicalMixed Precision Training
Mixed precision training speeds up neural network training and cuts memory use by performing most math in 16-bit floating point instead of 32-bit.
TechnicalSpeculative Decoding
Speculative decoding makes large language models generate text faster by using a small, fast 'draft' model to guess several tokens ahead, then having the big…
TechnicalKV Cache Optimization
The KV cache stores the keys and values a transformer has already computed so it doesn't redo work for every new token — but it can balloon to gigabytes.
ApplicationsAI Email Triage
AI email triage uses language models to read, sort, prioritize, and draft replies for your inbox automatically.
ApplicationsAI Calendar Scheduling
AI calendar scheduling tools find meeting times, resolve conflicts, and book events using natural language and smart preference rules.
ApplicationsAI Spreadsheet Copilots
AI spreadsheet copilots let you analyze data, write formulas, and build charts using plain-English prompts instead of memorizing functions.
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.
TechnicalFlash Attention
Flash Attention is a clever way to compute the attention step inside Transformers without ever writing the giant attention matrix to slow memory.
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