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
Depthwise Separable Convolutions
Depthwise separable convolutions factor a standard convolution into two cheaper steps, slashing the number of multiplications and parameters.
TechnicalDilated and Atrous Convolutions
Dilated convolutions (also called atrous convolutions) insert gaps between filter weights so a kernel covers a much larger area without adding parameters.
TechnicalHighway Networks and Skip Connections
Skip connections let information jump past layers, and highway networks were an early gated version of this idea.
Language AIELECTRA Pretraining
ELECTRA is a more efficient way to pretrain language models by teaching them to spot fake words instead of guessing hidden ones.
Language AIRoBERTa Training Recipe
RoBERTa showed that BERT was significantly undertrained: by tuning the recipe rather than the architecture, it set new benchmark records.
Language AIXLNet Permutation Modeling
XLNet blends the bidirectional context of BERT with the autoregressive prediction of GPT by training over random word orderings.
TechnicalDomain Adaptation
Domain adaptation is a set of techniques for making a model trained on one kind of data (the source domain) work well on a different but related kind of data…
Language AITree of Thoughts
Tree of Thoughts (ToT) is a prompting framework that lets a language model explore multiple reasoning paths in parallel, like branches of a tree, instead…
Language AISelf-Consistency Decoding
Self-consistency is a decoding strategy that samples many different reasoning paths from a language model and then picks the answer most of them agree on.
FundamentalsK-Means Clustering
K-Means is an unsupervised algorithm that automatically sorts data into K groups by finding cluster centers.
FundamentalsK-Nearest Neighbors
K-Nearest Neighbors (KNN) classifies a new data point by looking at the K closest examples and taking a majority vote.
FundamentalsLogistic Regression
Logistic regression predicts the probability that something belongs to a class, like spam or not spam, by squashing a weighted sum through an S-shaped curve.
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