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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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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
1-Bit and Ternary BitNet Models
BitNet is Microsoft's line of research showing that large language models can be trained with weights restricted to just 1 bit, or three values…
TechnicalGPTQ and AWQ Post-Training Quantization
GPTQ and AWQ are two leading methods for shrinking already-trained language models to 4-bit precision so they run on cheaper, smaller hardware.
TechnicalROUGE and BLEU Evaluation Metrics
ROUGE and BLEU are the workhorse automatic metrics for comparing machine-generated text against human references.
TechnicalSpeculative RAG and Retrieval-Augmented Drafting
Speculative RAG speeds up and sharpens retrieval-augmented generation by having a small, fast model draft multiple candidate answers from retrieved…
TechnicalBlock-Sparse and Native Sparse Attention
Block-sparse and native sparse attention let transformers attend to only the most relevant chunks of a long sequence instead of every token, slashing…
TechnicalWatermarking Language Model Outputs
Watermarking embeds a hidden statistical signal into AI-generated text so it can later be detected as machine-written, without changing what a human reader…
TechnicalSpeculative Decoding with EAGLE
Speculative decoding speeds up large language model inference by letting a tiny draft model guess several tokens ahead, which the big model then verifies…
TechnicalInfluence Functions for Training Data Attribution
Influence functions estimate how much each training example shaped a model's prediction, letting you trace an output back to the data that caused it.
TechnicalBatch Normalization
Batch normalization is a technique that rescales the inputs to each layer of a neural network during training, making deep networks train faster and more…
TechnicalHyperparameter Tuning
Hyperparameters are the settings you choose before training, like learning rate or model size, that the model does not learn on its own.
TechnicalContrastive Learning
Contrastive learning teaches a model to pull similar things together and push dissimilar things apart in an embedding space.
TechnicalReinforcement Learning From Human Feedback
RLHF is the technique that turns a raw language model into a helpful, polite assistant by training it on human preferences.
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