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
Linear Probing and Frozen Feature Evaluation
Linear probing tests how good a pretrained model's internal representations are by freezing the network and training only a simple linear classifier on top.
Language AISpeculative Sampling Verification
Speculative sampling speeds up large language model generation by letting a small 'draft' model guess several tokens ahead, then having the big model verify…
Language AIEntropy-Based Sampling
Entropy-based sampling adapts how an LLM picks its next token based on how uncertain the model is at that moment.
TechnicalDenseNet and Dense Connectivity
DenseNet is a convolutional network where every layer receives the feature maps of all preceding layers as input.
TechnicalBottleneck Architectures
A bottleneck architecture squeezes data through a narrow intermediate layer before expanding it again, forcing the network to learn compact, efficient…
TechnicalGradient Accumulation
Gradient accumulation lets you simulate a large batch size on limited GPU memory by summing gradients over several small mini-batches before updating…
FundamentalsEarly Stopping
Early stopping is a regularization technique that halts model training the moment performance on held-out validation data stops improving.
TechnicalTeacher Forcing in Sequence Models
Teacher forcing is a training trick for sequence models where the true previous token, not the model's own guess, is fed in as the next input.
TechnicalScheduled Sampling and Exposure Bias
Exposure bias is the gap that appears when a model trained only on perfect prefixes must, at inference, condition on its own imperfect outputs.
TechnicalFocal Loss for Imbalanced Detection
Focal loss is a modified loss function that down-weights easy examples so a detector can focus on the hard, rare ones.
FundamentalsStochastic Gradient Descent with Momentum
Momentum is a tweak to gradient descent that accumulates a running average of past gradients, letting optimization roll faster through valleys and dampen…
FundamentalsNesterov Accelerated Gradient
Nesterov Accelerated Gradient (NAG) is a smarter form of momentum that peeks ahead before computing the gradient, giving it a corrective look-ahead.
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