Yes — this is a strong 36-class AI Automation curriculum. I’d structure it into 12 modules × 3 classes, so students learn one concept, practice it, and then build something with it. 36-Class AI Automation Course Module Classes Topics 1. AI Foundations 1–3 AI Basics, ML vs AI, Generative AI, LLMs, AI Agents, real-world automation examples 2. Automation Foundations 4–6 What is automation, workflows, triggers/actions, automation architecture, practical mini workflows 3. APIs & Data 7–9 APIs, HTTP methods, API authentication, JSON, JSON objects/arrays, reading API responses 4. n8n Fundamentals 10–12 n8n introduction, installation/cloud options, nodes, triggers, workflow creation, first automation 5. Webhooks & HTTP 13–15 Webhooks, GET/POST, HTTP Request node, API integration, sending/receiving data 6. n8n Logic & Coding 16–18 Expressions, IF/Switch, Code Node, JavaScript basics for n8n 7. Prompt Engineering 19–21 Prompt structure, system/user prompts, variables, AI instructions, Structured Output 8. Local AI + n8n 22–24 Local AI concepts, Ollama, local models, connecting AI with n8n, privacy/offline workflows 9. AI + Business Automation 25–27 Google Sheets, AI data processing, lead automation, email/notification workflows 10. AI Agents 28–30 Agents, tools, tool calling, memory, agent workflow design 11. Production Concepts 31–33 Human Approval, MySQL, error handling, validation, debugging, reliable workflows 12. Final Project 34–36 Project planning, development, testing, presentation/demo Practical progression Classes 1–9: Understand the foundation ↓ Classes 10–15: Build your first n8n automations ↓ Classes 16–21: Add logic + AI ↓ Classes 22–27: Connect AI to real applications ↓ Classes 28–33: Build proper AI Agents ↓ Classes 34–36: Final project Suggested student projects Class 6: Simple automation workflow Class 9: API + JSON weather/data workflow Class 12: First complete n8n workflow Class 15: Webhook → API → Response automation Class 18: Conditional AI workflow Class 21: AI text-processing assistant Class 24: Local AI chatbot workflow Class 27: AI lead-management system with Google Sheets Class 30: AI Agent with tools + memory Class 33: Human-approved business automation Classes 34–36: Final AI Automation Agent Free-first tool stack For the course, I'd make the default stack: n8n — workflow automation Ollama — local AI Google Sheets — simple database/data storage MySQL — relational database Webhook / REST APIs — integrations JavaScript — Code Node JSON — data exchange Free/public APIs where available That keeps the curriculum practical without making students dependent on paid AI APIs. If this is going to be your actual 3-month batch, the next step should be turning these 36 classes into a teacher-ready syllabus: each class → theory → live practical → homework → mini task → expected outcome, with the exact free tools required.
Yes — this is a strong 36-class AI Automation curriculum. I’d structure it into 12 modules × 3 classes, so students learn one concept, practice it, and then build something with it. 36-Class AI Automation Course Module Classes Topics 1. AI Foundations 1–3 AI Basics, ML vs AI, Generative AI, LLMs, AI Agents, real-world automation examples 2. Automation Foundations 4–6 What is automation, workflows, triggers/actions, automation architecture, practical mini workflows 3. APIs & Data 7–9 APIs, HTTP methods, API authentication, JSON, JSON objects/arrays, reading API responses 4. n8n Fundamentals 10–12 n8n introduction, installation/cloud options, nodes, triggers, workflow creation, first automation 5. Webhooks & HTTP 13–15 Webhooks, GET/POST, HTTP Request node, API integration, sending/receiving data 6. n8n Logic & Coding 16–18 Expressions, IF/Switch, Code Node, JavaScript basics for n8n 7. Prompt Engineering 19–21 Prompt structure, system/user prompts, variables, AI instructions, Structured Output 8. Local AI + n8n 22–24 Local AI concepts, Ollama, local models, connecting AI with n8n, privacy/offline workflows 9. AI + Business Automation 25–27 Google Sheets, AI data processing, lead automation, email/notification workflows 10. AI Agents 28–30 Agents, tools, tool calling, memory, agent workflow design 11. Production Concepts 31–33 Human Approval, MySQL, error handling, validation, debugging, reliable workflows 12. Final Project 34–36 Project planning, development, testing, presentation/demo Practical progression Classes 1–9: Understand the foundation ↓ Classes 10–15: Build your first n8n automations ↓ Classes 16–21: Add logic + AI ↓ Classes 22–27: Connect AI to real applications ↓ Classes 28–33: Build proper AI Agents ↓ Classes 34–36: Final project Suggested student projects Class 6: Simple automation workflow Class 9: API + JSON weather/data workflow Class 12: First complete n8n workflow Class 15: Webhook → API → Response automation Class 18: Conditional AI workflow Class 21: AI text-processing assistant Class 24: Local AI chatbot workflow Class 27: AI lead-management system with Google Sheets Class 30: AI Agent with tools + memory Class 33: Human-approved business automation Classes 34–36: Final AI Automation Agent Free-first tool stack For the course, I'd make the default stack: n8n — workflow automation Ollama — local AI Google Sheets — simple database/data storage MySQL — relational database Webhook / REST APIs — integrations JavaScript — Code Node JSON — data exchange Free/public APIs where available That keeps the curriculum practical without making students dependent on paid AI APIs. If this is going to be your actual 3-month batch, the next step should be turning these 36 classes into a teacher-ready syllabus: each class → theory → live practical → homework → mini task → expected outcome, with the exact free tools required.
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This program focuses on building a strong foundation in automation through AI and LLMs. Participants will learn essential concepts, APIs, and workflows by creating a weather-data API mini project. The course progresses to practical AI workflows, mastering n8n nodes and integrating various data sources. Finally, learners will design reliable AI solutions, incorporating tools, memory, and error handling, culminating in the presentation of a final automation agent.