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
Data 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.
Visual AIAction Recognition
Action recognition is the task of teaching computers to identify what people or objects are *doing* in video — running, waving, falling, opening a door — not…
Visual AIVideo Frame Interpolation
Video frame interpolation generates new, in-between frames from existing ones to make video smoother or slower — turning 30fps footage into 60fps…
Visual AIMulti-Object Tracking
Multi-object tracking (MOT) follows many objects — pedestrians, cars, players — across the frames of a video, giving each a consistent identity over time.
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.
IndustriesAI in Pathology
AI in pathology applies computer vision to digitized tissue slides, helping pathologists detect cancer, count cells, and grade disease faster and more…
Visual AIAI in Medical Imaging
AI in medical imaging uses computer vision to read X-rays, CT scans, MRIs, ultrasounds, and mammograms, spotting abnormalities and prioritizing urgent cases.
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