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
AI Prompt Security
Prompt security addresses attempts to make a language-model application treat untrusted content as instructions or disclose information it should protect.
TechnicalAI Data Governance
AI data governance assigns responsibility and rules for how data is collected, used, shared, retained, and corrected throughout an AI system.
TechnicalAI Cloud Architecture
AI cloud architecture organizes compute, storage, networking, models, and application services into an operating system for an AI workload.
TechnicalBYOL and Non-Contrastive Self-Supervision
BYOL (Bootstrap Your Own Latent) learns useful image representations without any labels and, surprisingly, without negative examples.
TechnicalBarlow Twins and Redundancy Reduction
Barlow Twins is a self-supervised method that learns representations by making the cross-correlation matrix between two augmented views close to the identity…
TechnicalPseudo-Labeling and Self-Training
Pseudo-labeling is a semi-supervised technique where a model trained on a small labeled set generates its own labels for unlabeled data, then trains on those…
TechnicalConsistency Regularization in Semi-Supervised Learning
Consistency regularization teaches a model to give the same answer when an unlabeled input is perturbed in small, label-preserving ways.
TechnicalHard Parameter Sharing in Multi-Task Networks
Hard parameter sharing is the classic multi-task learning design where several tasks share the same hidden layers and only split into separate output 'heads'…
TechnicalGating and Routing in Conditional Computation
Gating and routing let a neural network activate only the parts it needs for each input instead of running the whole model every time.
TechnicalGumbel-Softmax and Reparameterization
Gumbel-Softmax is a trick that lets neural networks 'sample' from discrete categories while still being trainable by gradient descent.
TechnicalStraight-Through Estimator
The Straight-Through Estimator (STE) is a simple trick for training networks that contain hard, non-differentiable steps like rounding or thresholding.
TechnicalBidirectional Recurrent Networks
A bidirectional recurrent network reads a sequence both forward and backward, so each position's representation draws on context from the past and the future.
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