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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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Fundamentals
Stochastic 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.
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.
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…
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.
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.
FundamentalsNaive Bayes Classifiers
Naive Bayes is a fast, probabilistic classifier built on Bayes' theorem that assumes every feature is independent given the class.
FundamentalsROC Curves and AUC
An ROC curve plots how well a classifier separates two classes across every possible decision threshold, and AUC compresses that whole curve into one number.
FundamentalsConfusion Matrices
A confusion matrix is a simple table that breaks a classifier's predictions into correct and incorrect counts for each class.
FundamentalsBias-Variance Tradeoff
The bias-variance tradeoff explains why a model can fail by being too simple or too complex.
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