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
Test-Time Augmentation
Test-time augmentation (TTA) runs a trained model on several altered versions of the same input and averages the predictions.
TechnicalNegative Sampling and Noise Contrastive Estimation
Negative sampling and Noise Contrastive Estimation (NCE) are tricks that let models learn over huge vocabularies without computing a costly full softmax.
TechnicalInfoNCE and SimCLR Objectives
InfoNCE is the contrastive loss that teaches a model to pull matching pairs together and push mismatched pairs apart in embedding space.
TechnicalStochastic Weight Averaging
Stochastic Weight Averaging (SWA) takes a simple average of the model's weights from several points late in training instead of just keeping the final…
TechnicalLookahead and Lion Optimizers
Lookahead and Lion are two modern twists on neural-network optimization.
TechnicalMixup and CutMix Augmentation
Mixup and CutMix are data-augmentation methods that create new training examples by blending two images and their labels.
FundamentalsTriplet Loss and Metric Learning
Triplet loss teaches a neural network to place similar items close together and dissimilar items far apart in an embedding space.
TechnicalOnline and Hard Negative Mining
Hard negative mining picks the most informative, difficult-to-distinguish examples to train on instead of wasting effort on easy ones the model already gets…
FundamentalsLabel Smoothing
Label smoothing is a simple regularization trick that softens hard one-hot training targets, telling the model the correct answer is very likely but not 100…
TechnicalCapsule Networks
Capsule networks are a neural architecture that groups neurons into 'capsules' that output vectors encoding both whether a feature exists and its pose…
TechnicalEcho State Networks and Reservoir Computing
Reservoir computing is a clever shortcut for training recurrent networks: leave a large, randomly connected 'reservoir' of neurons fixed and only train…
FundamentalsLong Short-Term Memory Cells
Long Short-Term Memory (LSTM) cells are a special kind of recurrent neural network unit built to remember information across long sequences.
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