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Learning path

Learn AI for students

A technical student path from AI literacy and research workflows through coding labs, deployment, and a portfolio capstone.

Intended learner
Career-track secondary, college, and university learners ready to build
Prerequisites
Independent study habits and comfort reading technical English; No coding required at the start; Python is introduced before advanced labs
Recommended pace
24 weeks · 4 hr/week · 45-minute sessions
Core spine
133 lessons · ~20.6 hrs
Electives
23 lessons · ~3.6 hrs
Difficulty
Intermediate
Outcome
Research with AI, understand the system stack, and build, evaluate, deploy, and operate a portfolio project.
Capstone project
Ship a tiny AI app — A deployed AI feature with a repository, evaluation evidence, and release notes.
Core progress0/133 · 0%
Electives progress0/23 · 0%
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In the wild

Catalog picks that match this path — explore products and vendors alongside the curriculum.

Tools

  • GradGPT — Admissions brainstorming — rewrite in your own voice.
  • Khanmigo — Guided tutoring practice tied to learning content.
  • ChatGPT — Explain errors and draft study notes — still verify.

Companies

  • OpenAI — Models many students meet first for study help.

All tools · All companies

Optional self-check

Find your best starting phase

Use this short self-check to skip familiar foundations without hiding the complete roadmap.

Check only what you can already do without step-by-step help.

0/4 signals · Start from the foundations

Recommended entry: AI literacy. This does not mark lessons complete; use it to challenge out of familiar material.

Start at this phase →

Core roadmap

Learn in phases, build as you go

Open a module when you are ready. The full lesson sequence is still preserved for resume and next-lesson navigation.

Phase 1

AI literacy

Recognize AI, its limits, and the safety decisions that come first.

Foundations and limitsBuild reliable judgment about where AI helps and where it fails.12 lessons
  1. 01AI you already use8m
  2. 02What is AI?8m
  3. 03History of AI10m
  4. 04AI vs human smarts9m
  5. 05What AI can and can't do9m
  6. 06Machine learning in plain English9m
  7. 07Generative AI vs older AI9m
  8. 08Speech Recognition9m
  9. 09Why AI makes mistakes10m
  10. 10Bias and fairness9m
  11. 11AI Copyright10m
  12. 12Deepfakes & synthetic media9m
Safety and societyBuild reliable judgment about where AI helps and where it fails.7 lessons
  1. 01AI regulation basics8m
  2. 02AI ethics scenarios8m
  3. 03Model governance basics8m
  4. 04Content safety basics8m
  5. 05Privacy and smart sharing9m
  6. 06Your AI learning map8m
  7. 07Data fuels AI8m
Phase 2

Practical AI use

Prompt, verify, and apply AI to useful work without outsourcing judgment.

Prompt foundationsPractice a repeatable workflow and keep a human verification step.12 lessons

Builds on: AI literacy

  1. 01Talk to AI for the first time8m
  2. 02Good prompts have a job9m
  3. 03Add context9m
  4. 04Iterate: bad to better9m
  5. 05Chain-of-Thought Prompting10m
  6. 06Prompt pattern library10m
  7. 07Study helper9m
  8. 08AI for writing & editing10m
  9. 09AI for Research10m
  10. 10AI for Teachers10m
  11. 11Home and life helper9m
  12. 12Work helper9m
Applied workflowsPractice a repeatable workflow and keep a human verification step.12 lessons

Builds on: AI literacy

  1. 01AI for meetings9m
  2. 02AI for email9m
  3. 03AI for customer support9m
  4. 04AI for Excel10m
  5. 05AI for data analysis10m
  6. 06AI for Presentations10m
  7. 07AI for coding10m
  8. 08Agentic coding tools11m
  9. 09Pictures and creative tools9m
  10. 10Image generation basics8m
  11. 11Spot wrong answers9m
  12. 12No-Code AI10m
Verification and habitsPractice a repeatable workflow and keep a human verification step.4 lessons

Builds on: AI literacy

  1. 01AI for automation10m
  2. 02Capstone roadmap8m
  3. 03Your weekly AI habit8m
  4. 04Careers in AI10m
Phase 3

How AI systems work

Build a working mental model of language models, retrieval, tools, and agents.

Model foundationsConnect system behavior to the technical ideas underneath it.12 lessons

Builds on: Practical AI use

  1. 01What is an LLM?7m
  2. 02Tokens — how AI reads text6m
  3. 03Context window suitcase6m
  4. 04Prompt caching8m
  5. 05Memory & conversation state8m
  6. 06Temperature — safe vs creative6m
  7. 07Embeddings — meaning as numbers7m
  8. 08Vectors & similarity search7m
  9. 09RAG — look it up, then answer8m
  10. 10Inside RAG — the pipeline8m
  11. 11Chunking for RAG quality8m
  12. 12Vector databases explained9m
Retrieval and toolsConnect system behavior to the technical ideas underneath it.12 lessons

Builds on: Practical AI use

  1. 01Hybrid search for RAG9m
  2. 02Tools — when AI takes action7m
  3. 03Structured outputs & JSON mode8m
  4. 04Agents — think, act, repeat8m
  5. 05Evals and benchmarks8m
  6. 06Human in the loop8m
  7. 07Reasoning models8m
  8. 08MCP — a standard plug for AI tools8m
  9. 09Training vs inference8m
  10. 10Fine-tuning vs RAG9m
  11. 11Diffusion models in plain English8m
  12. 12Prompt injection & AI security9m
Agents and evaluationConnect system behavior to the technical ideas underneath it.1 lessons

Builds on: Practical AI use

  1. 01AI Monitoring9m
Phase 4

Build and ship

Turn the concepts into tested projects and portfolio evidence.

Python foundationsComplete hands-on labs and keep evidence of what works.8 lessons

Builds on: How AI systems work

  1. 01Python only what you need8m
  2. 02Python dictionaries9m
  3. 03Python virtual environments9m
  4. 04Data: tables and simple plots9m
  5. 05NumPy and pandas basics12m
  6. 06File handling9m
  7. 07Git basics10m
  8. 08GitHub basics10m
Classical ML & visionComplete hands-on labs and keep evidence of what works.7 lessons

Builds on: How AI systems work

  1. 01Prediction: your first ML idea9m
  2. 02Clustering10m
  3. 03Decision trees10m
  4. 04Train a tiny model10m
  5. 05Loss functions10m
  6. 06Neural nets by building10m
  7. 07Deep learning10m
LLM apps & agents · Part 1Complete hands-on labs and keep evidence of what works.12 lessons

Builds on: How AI systems work

  1. 01Talk to an LLM from code10m
  2. 02Multimodal API lab10m
  3. 03Streaming responses9m
  4. 04Local model lab10m
  5. 05API errors & retries9m
  6. 06Semantic cache lab10m
  7. 07Build a mini RAG11m
  8. 08Embedding API lab10m
  9. 09Batch API lab10m
  10. 10Vector DB integration lab11m
  11. 11Re-ranking lab10m
  12. 12Fine-tuning lab11m
LLM apps & agents · Part 2Complete hands-on labs and keep evidence of what works.11 lessons

Builds on: How AI systems work

  1. 01Function calling in code11m
  2. 02Structured output lab10m
  3. 03Agents in code11m
  4. 04Multi-agent in code11m
  5. 05Eval gates in code10m
  6. 06Tools in code11m
  7. 07Workflow automation in code10m
  8. 08MCP in code10m
  9. 09Guardrails in code10m
  10. 10Prompt injection in code10m
  11. 11RAG quality audit10m
Ship & operations · Part 1Complete hands-on labs and keep evidence of what works.12 lessons

Builds on: How AI systems work

  1. 01LLM tracing lab10m
  2. 02Cost optimization lab10m
  3. 03Eval metrics lab10m
  4. 04Webhook lab10m
  5. 05Model deployment11m
  6. 06Canary deploy lab10m
  7. 07A/B test lab10m
  8. 08Blue-green deploy lab10m
  9. 09Deploy a RAG app11m
  10. 10Production monitoring lab10m
  11. 11Incident response lab10m
  12. 12Runbook lab9m
Ship & operations · Part 2Complete hands-on labs and keep evidence of what works.9 lessons

Builds on: How AI systems work

  1. 01Rate limiting lab10m
  2. 02SLO lab10m
  3. 03Feature flags lab10m
  4. 04Health check lab10m
  5. 05Secrets rotation lab10m
  6. 06Load testing lab10m
  7. 07Disaster recovery lab10m
  8. 08Postmortem lab9m
  9. 09On-call lab9m
CapstonesComplete hands-on labs and keep evidence of what works.2 lessons

Builds on: How AI systems work

  1. 01Capstone: ship a tiny AI app12m
  2. 02Capstone ops lab10m

Optional extras

  1. 01Capstone: support bot with RAG + tools14m
  2. 02Capstone: research bot with citations13m
  3. 03When computers see10m
  4. 04Classes and objects10m
  5. 05Random forests10m
  6. 06Local LLMs & Ollama8m
  7. 07Production AI Architecture10m
  8. 08Multi-Agent Systems9m
  9. 09Transformers in plain English9m
  10. 10Serving Large Language Models9m
  11. 11Choosing a model9m
  12. 12Multimodal prompts9m
  13. 13Pick the right AI tool8m
  14. 14AI for Marketing10m
  15. 15AI for legal basics9m
  16. 16AI for finance basics9m
  17. 17AI for product managers9m
  18. 18AI for HR basics9m
  19. 19AI Video Generation10m
  20. 20AI Voice Generation10m
  21. 21Overfitting playground6m
  22. 22Multimodal AI8m
  23. 23Alignment and RLHF9m