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
Disaggregated Prefill and Decode Serving
A serving architecture that splits large language model inference into two separate phases—prefill and decode—and runs them on different pools of GPUs.
TechnicalLLM Inference Routing and Load Balancing
The control layer that decides which model replica, GPU, or backend should handle each incoming LLM request, and how to spread traffic so no single server is…
TechnicalTensor Parallelism for Large Models
A way to split the math inside a single neural-network layer across multiple GPUs so a model too big for one device can still run.
TechnicalSelf-Reflection in Agent Loops
Self-reflection lets an AI agent critique its own outputs and actions mid-task, then revise based on that critique.
TechnicalTree-of-Thoughts Reasoning
Tree-of-Thoughts lets a model explore many reasoning paths in parallel, like branches of a tree, instead of committing to one line of thought.
TechnicalGraph-of-Thoughts Reasoning
Graph-of-Thoughts models reasoning as a network where thoughts can merge, loop, and connect freely, not just branch.
TechnicalClass Imbalance and Resampling
Class imbalance is when one outcome vastly outnumbers another — like 99.9% legitimate transactions versus 0.
TechnicalAutoencoders
An autoencoder is a neural network that learns to compress data into a compact code and then reconstruct it, forcing the network to capture only the most…
TechnicalSiamese Networks and Triplet Loss
Siamese networks use two or more identical, weight-sharing branches to learn how similar two inputs are, rather than classifying each one.
TechnicalMulti-Task Learning
Multi-task learning trains one model to perform several related tasks at once, sharing internal representations across them.
TechnicalConformal Prediction
Conformal prediction wraps any model to output a set or interval that is guaranteed to contain the true answer with a chosen probability, like 90%.
TechnicalProbability Calibration
Calibration means a model's stated probabilities match reality: when it says 70%, the event should happen about 70% of the time.
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