Pathway 18: "I Want to Teach AI to Others" (AI Educator / Trainer)

Target audience: Teachers, corporate trainers, and curriculum designers who need to build AI literacy programs or AI-augmented learning experiences

Goal: Develop broad understanding across all major LLM topics, with enough depth to explain concepts clearly, design exercises, and help students build practical projects. This pathway prioritizes breadth over depth.

Chapter Guide

• Focus Ch 00: ML and PyTorch Foundations (core teaching material) core teaching material for ML foundations
• Skim Ch 01: NLP and Text Representation background for text processing lectures
• Skim Ch 02: Tokenization (great live demo topic) great live demo topic for students
• Skim Ch 03: Sequence Models and Attention context for explaining attention visually
• Focus Ch 04: The Transformer Architecture (key concept for all students) key concept every student should understand
• Skim Ch 05: Decoding and Text Generation interactive demo: watch generation happen
• Skim Ch 06: Pre-training and Scaling Laws context for discussing model capabilities
• Skim Ch 07: The Modern LLM Landscape help students navigate the model landscape
• Skim Ch 08: Reasoning Models emerging topic students will ask about
• Focus Ch 10: Working with LLM APIs (hands-on starting point for workshops) hands-on starting point for workshops
• Focus Ch 11: Prompt Engineering (most-requested workshop topic) most-requested workshop topic by far
• Skim Ch 12: Hybrid ML+LLM Architectures teach when to combine approaches
• Skim Ch 19: Embeddings and Vector Databases background for RAG workshop projects
• Focus Ch 20: RAG (popular workshop project) popular and engaging workshop project
• Skim Ch 21: Conversational AI chatbot demos students love to build
• Focus Ch 22: AI Agents (engaging demo material) engaging demo material for live sessions
• Skim Ch 27: Multimodal Models wow-factor demos for student engagement
• Skim Ch 28: LLM Applications project ideas students can build on
• Focus Ch 29: Evaluation (teaching students to evaluate their own work) teach students to evaluate their own work
• Focus Ch 32: Safety, Ethics and Regulation (essential for responsible teaching) essential for responsible AI teaching
• Skim Ch 34: Emerging Architectures keep current on where the field is heading
• Skim Ch 35: AI and Society discussion material for ethics modules