AI Curriculum
Sheet 001 — Master Index · Codextroop

AI Engineer Curriculum

Every volume, module, and topic from the original course drafts — reorganized and indexed like a technical reference book. Nothing dropped; gaps are marked as new modules. Published by Codextroop.

–Volumes
–Modules
–Topics
60→∞Days → Program
No topics match that filter.
Sheet 002 — Appendix

Appendix

Design notes carried over from the original planning discussion, plus a true book-style alphabetical index of every topic in this curriculum.

A · Structural Model

  • Hierarchy: Volume → Module → Lecture → Demo → Assignment → Quiz → Project.
  • Current: covers MCP, agentic workflows, open-weight models, multimodal AI, reasoning models, and the live AI platform landscape.
  • Scalable: new models/tools slot into existing volumes without a redesign.
  • Practical: every concept is paired with hands-on implementation.

B · Scope Recommendation

  • Treat this as a full AI Engineer Curriculum, not just a 60-day internship.
  • Target scale: 20–25 volumes, 100–150 modules, 400–600 lectures.
  • 100+ hands-on labs and 50+ real-world projects.
  • 200+ tools, models, frameworks, libraries, and companies referenced.
  • Reusable as an internship track, LMS course, book series, or certification.

C · Gap-Review Classification

  • Group 1 — Fully covered: all foundational math, Python, ML, DL, CNN, RNN, NLP, embeddings, transformers, LLM, prompting, RAG, agents, diffusion, deployment, and ethics topics from the original list.
  • Group 2 — Added as new modules: Data Engineering, Model Training Internals, GPU Computing, deep-dive Tokenization, deep-dive Embeddings, Inference Optimization, deep-dive Fine-Tuning, Agent Memory types, and MCP internals.
  • Group 3 — Became an entire new volume: the AI Companies & Ecosystem volume (Vol. 22) — frontier labs, image/video/voice AI, dev platforms, IDEs, and automation tools.

D · Capstone Direction

  • Original list: 15 capstone projects (see Volume 23).
  • Recommendation: expand to 25–30 progressively challenging projects, starting from simple prompt-based apps and ending with production-ready systems.
  • Dedicated Volume 14 (RAG + Vector DB + LangChain) and Volume 15 (Agents) directly feed the capstone builds — PDF Chatbot, Research Assistant, Multi-Agent Travel Planner, etc.

F · Certification Exam

  • Two tracks: Foundation (40 Q · 45 min · Pass ≥60%) and Professional (60 Q · 75 min · Pass ≥70%).
  • Stratified sampling ensures coverage across all 23 volumes each attempt.
  • Grades: Excellent, Good, Pass — certificate for Pass and above.
  • Start the AI Engineer Certification Exam →

E · Alphabetical Topic Index