Browse every Foundation, Track A (AI Software & Data Science), Track B (AI-Powered Robotics & Physical AI), and Research & Competition module. Most students take only the modules they need — we recommend a personalized sequence after a free Project Path Consultation.
Looking for a customized project instead of a course? Our 6–8 month Project Mentorship builds a real, personalized project end-to-end.
See Project MentorshipJump to a section: Foundation · Track A — AI Software · Track B — Robotics · Research & Competition · Bootcamp
Programming, data, documentation, and presentation skills students need to begin real AI, software, or robotics projects.
Most students take 2–3 of these four modules.Best for: Students new to coding who are heading toward AI, data, web app, or robotics projects.
Core Python building blocks (variables, loops, functions, file I/O), Git basics, and how to use AI coding tools as a tutor — not a crutch.
2–3 small Python projects on GitHub, plus a short presentation explaining one of them.
Continue with 2420 Data Science & Math or move into Track A or Track B based on interests.
Best for: Students heading toward AI / data / Track A — strongly recommended before Machine Learning Project Builder.
Loading real datasets, cleaning messy data, making charts that tell a story, and the basic statistics that machine learning depends on.
A complete data analysis project from CSV to written report, with charts and a GitHub notebook.
2460 Machine Learning Project Builder or any Track A course.
Best for: Students heading toward AI web apps and LLM tools. Optional but highly recommended for Track A students.
Turning creative ideas into web app prototypes using modern AI-assisted coding workflows — prompts, iteration, deployment.
A live web app project with a polished UI, demo video, and short presentation.
Best for: All students. Essential before any competition or college-application project work.
How to research, document, showcase, and present technical projects using GitHub, project portfolios, demo videos, slide decks, AI tools, and effective technical communication.
A professional project portfolio with a GitHub repository and project page, polished README, research summary, demo video, slide deck, and practiced technical presentation.
Apply these skills to any Track A or Track B project.
Four project-building modules. Build AI software, machine learning projects, data science projects, LLM tools, and agent-based applications.
Students usually take 2–3 of these modules based on background and goals.Best for: Students who want a research-style AI/ML project for portfolio or science-fair use.
The full ML workflow: problem types, data cleaning, feature engineering, baseline models, evaluation, error analysis, and improvement.
A complete ML mini-project with a notebook, charts, model evaluation, GitHub README, and short presentation.
Best for: Students who want to build a real AI-powered product as a portfolio piece.
Prompt engineering, LLM APIs, simple frontend/backend architecture, database basics, and deployment workflows.
A working AI web app or LLM tool (e.g., AI study coach, research helper, FAQ chatbot).
Best for: Students aiming at serious research projects or competition submissions. Not for first-time AI students.
Neural network intuition, CNN/RNN/LSTM patterns, computer vision, NLP, embeddings, RAG, agent workflows, tool use, and memory.
An advanced AI prototype demonstrating one or more advanced patterns (vision, RAG, agents).
Best for: Capstone for Track A. A guided studio course — different from full long-term project mentorship.
Integrating previously-learned AI skills into a single applied AI project: problem definition, prototype, improvement, testing, demo.
A polished applied AI project with full documentation, GitHub repo, report, demo, and slide deck.
8-Month Project Mentorship for a fully customized, portfolio-grade project.
Four robotics project levels — from electronics fundamentals to intelligent robotics with edge AI.
Students do not need every level. We recommend the right starting level based on robotics, coding, and hardware background.Best for: Students brand new to electronics or hardware. The entry point for the Robotics path.
Circuits, breadboards, LEDs, buttons, resistors, sensors, the Arduino IDE, and basic Arduino programming.
A sensor-controlled alarm, smart light, servo gate, or mini smart-home device.
Best for: Students who liked Level 1 and want a working autonomous robot.
Motors, motor drivers, chassis, ultrasonic sensors, line tracking, obstacle avoidance, and simple state machines.
An obstacle-avoiding robot, line-follower, maze-navigation robot, or smart delivery mini-robot.
Best for: Students moving from sensor-based to vision-based robotics.
Setting up Raspberry Pi, Python on Pi, the camera module, OpenCV, color/shape detection, and object tracking.
An object-tracking robot, vision-based line follower, color-targeting robot, or camera-based obstacle detector.
Best for: Students aiming at robotics competitions or research projects that integrate AI with hardware.
Edge AI and TinyML concepts: lightweight object detection, sensor data classification, edge inference, intelligent decision logic, and reliability testing.
An edge-AI object recognition robot, smart sorting robot, AI environmental monitor, gesture-controlled robot, or TinyML anomaly detector.
8-Month Project Mentorship for a research- or competition-grade project.
Long-term project mentorship for serious portfolio and competition work. These are not standalone courses — they're the three phases of a 6 or 8-month engagement.
See Project Mentorship for how these phases combine.Best for: Phase 1 of the standard 8-month customized project.
How to choose a meaningful project direction, define a real problem, analyze feasibility, and match the project with competition or portfolio goals.
Project topic, problem statement, proposal, feasibility & competition-fit analysis, and a 6–8 month roadmap.
Best for: Students building serious AI, ML, data science, LLM, computer vision, NLP, or agent projects. Phase 2 of the 8-month customized project, AI track.
Experiments, model improvement, error analysis, ethics, and end-to-end documentation under weekly mentor + monthly senior review.
A working AI prototype, dataset or API pipeline, model or system implementation, experiments and results, GitHub repository, and technical report draft.
Best for: Students building serious robotics, embedded, Arduino, Raspberry Pi, computer vision, edge AI, or intelligent physical-system projects. Phase 2 of the 8-month customized project, robotics track.
Prototyping, hardware/software debugging, test plans, engineering iteration, and documentation under weekly mentor + monthly senior review.
A working robotics prototype, hardware architecture, sensor integration, code documentation, testing videos, engineering notebook, and technical report draft.
Best for: Phase 3 of the standard 8-month customized project. Closes the loop from project work to a presentable outcome.
How to write a strong final report, polish a GitHub README, design slides and a poster, record a demo video, and prepare for judge Q&A.
A final report, polished GitHub README, final slide deck, poster, demo video, judge Q&A prep, competition submission package (when appropriate), and a college application project story.
Apply to competitions, science fairs, or use as a college-application portfolio piece.
Not sure which modules above your child needs? The 4-week Bootcamp produces a written Project Readiness Report with a personalized learning roadmap. Standard pricing from $780; $500 credit toward mentorship if continued within 14 days.
Foundation and Track courses are typically $600–$900 per course and run 8–12 weeks at 2–6 hours per week. Mentorship pricing is tailored after a free consultation. For the full pricing table, see Timeline & Investment on the Programs page.
See Open SessionsMost students take only 3-5 of these modules. Book a free Project Path Consultation and we'll recommend the shortest path through this catalog for your child.
Book a Free Project Path Consultation