Trace AI history and terminology while exploring human-in-the-loop systems, ethics, privacy, governance, and AI's broader societal impact.
Build from perceptrons to neural networks and CNNs, then see how visual features support classification and computer-vision applications through the interactive tools.
Explore how agents learn through actions, rewards, policies, and exploration, including Q-tables and decision-making over time in both reinforcement-learning tools.
Understand the GPUs, data centers, computational scale, and energy demands behind modern AI, alongside governance, ethical tensions, and social consequences.
Connect core machine-learning ideas to natural language processing and large language models, including practical applications, capabilities, and common limitations.
Explore reasoning models, AI assistants, evaluation, responsible use, agents, tool use, MCP, and emerging workflows for working with capable AI systems.
Explore how a simple artificial neuron turns several recommendations into a Yes or No decision. Change each response and trust weight, then watch the evidence combine.
Build a network one concept at a time, watch information flow, and see how training changes influence—without equations or code.
Explore how convolutional neural networks (CNN) identify visual patterns by applying filters and combining simple features into increasingly complex representations.
See how a CNN can score well overall while repeatedly missing rare classes, then test how balanced sampling changes the result.
Learn why Q-tables work, calculate one update at a time, and solve guided worksheet-inspired challenges.
Bridge Q-tables and deep reinforcement learning by tracing Breakout frames through a CNN, replay memory, and one action-value update.
Predict a robot’s move, watch it learn a grid world through trial and error, and explain how rewards shape its policy.
Type Python to inspect fish data, build visualizations, and turn patterns into careful evidence-based claims.
Train regression models, protect test data, compare errors, and see when model complexity becomes overfitting.
Build a classifier, expose false approvals and denials, and audit why a headline accuracy score is not enough.
Hide species labels, cluster fish by measurement, and test why discovered groups are not automatically true categories.