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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Industries
AI in Social Work and Child Welfare
Child welfare agencies are using predictive AI to help screen abuse and neglect reports, while social workers use AI tools to cut paperwork and surface risk.
IndustriesAI in Maritime and Shipping
AI is steering the shipping industry toward smarter routes, predictive maintenance, and even crewless vessels.
FundamentalsBias-Variance Tradeoff
The bias-variance tradeoff explains why a model can fail by being too simple or too complex.
FundamentalsCross-Validation
Cross-validation is a resampling technique for estimating how well a model will generalize to unseen data.
FundamentalsPrecision and Recall
Precision and recall are two complementary metrics for evaluating classifiers, especially when classes are imbalanced.
Language AIWordPiece Tokenization
WordPiece is the subword tokenization algorithm that powers BERT and many Google models, splitting words into reusable fragments so a model can handle any…
Language AISentence-BERT Embeddings
Sentence-BERT (SBERT) adapts BERT to produce a single fixed-length vector for an entire sentence, so meaning can be compared with fast cosine similarity.
Language AIT5 and Text-to-Text Transfer
T5 (Text-to-Text Transfer Transformer), from Google in 2019, reframes every NLP task, translation, summarization, classification, even regression, as feeding…
Visual AIDeformable Convolutions
Deformable convolutions let a neural network bend its sampling grid to follow the actual shape of objects instead of forcing it through a rigid square window.
Visual AIDINO Self-Distillation
DINO is a self-supervised method that trains a vision transformer to understand images with no labels at all, by having the network teach itself.
Visual AIImage Matting
Image matting is the art of cutting a subject out of a photo with pixel-perfect, semi-transparent edges — capturing every wispy strand of hair or motion blur.
TechnicalBentoML and Model Packaging
BentoML is an open-source Python framework that packages trained machine learning models into standardized, deployable units called 'Bentos'.
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