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
CUDA and GPU Programming
CUDA is NVIDIA's platform for writing programs that run on GPUs, unlocking thousands of cores for parallel computation.
TechnicalExplainable AI and SHAP
Explainable AI (XAI) is the toolkit for turning a model's opaque prediction into a human-readable reason.
TechnicalMechanistic Interpretability
Mechanistic interpretability is the effort to reverse-engineer the internal computations of neural networks into human-understandable algorithms.
TechnicalAdversarial Examples and Robustness
Adversarial examples are inputs perturbed by tiny, often imperceptible changes that cause a model to make confident, wrong predictions.
TechnicalData Parallelism
Data parallelism trains one model faster by replicating it across many GPUs, with each GPU processing a different slice of the data batch.
TechnicalModel and Pipeline Parallelism
When a model is too large to fit on one GPU, model and pipeline parallelism split the model itself across devices.
TechnicalZeRO and Sharded Optimizers
ZeRO (Zero Redundancy Optimizer) eliminates the wasteful memory duplication of data parallelism by sharding optimizer state, gradients, and weights across…
TechnicalExperiment Tracking
Experiment tracking is the practice of systematically recording every machine learning run — its code, data, hyperparameters, metrics, and outputs — so…
TechnicalCI/CD for Machine Learning
CI/CD for machine learning extends continuous integration and continuous delivery pipelines to cover not just code, but also data and models.
TechnicalCanary and Shadow Deployments
Canary and shadow deployments are two low-risk strategies for releasing a new model or service to production.
TechnicalPrompt Caching
Prompt caching lets an AI model reuse the computational work it did on a repeated chunk of text instead of reprocessing it every time.
TechnicalReranking Models
A reranker is a second-stage model that re-scores a shortlist of search results for relevance to a query, sharpening the ordering after a fast retriever…
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