AI Workflow Automation with Python and n8n
Course Overview
Key Points
Design AI-powered automation workflows from scratch
Integrate OpenAI and open-source LLMs into pipelines
Build visual workflows with n8n and Python scripts
Connect REST APIs and automate data extraction
Schedule, monitor, and debug automation pipelines
Deploy workflows to cloud environments
Handle errors, retries, and edge cases in automation
Build a capstone portfolio automation project
Course Outline
Module 1: Automation Fundamentals and AI Basics
- What is AI automation and where it fits in 2026
- Python environment setup and automation libraries
- Introduction to LLMs and prompt engineering
- Automation use cases and project planning
Module 2: Python Automation Scripts
- File, email, and data automation with Python
- Web scraping and API calls using requests and BeautifulSoup
- Scheduled tasks with cron and APScheduler
- Error handling and logging best practices
Module 3: n8n Visual Workflow Engine
- Setting up n8n locally and on cloud
- Building triggers, nodes, and conditions
- HTTP request nodes and API integrations
- Combining Python scripts with n8n workflows
Module 4: LLM Integration in Pipelines
- OpenAI API and open-source alternatives
- Prompt chaining and structured outputs
- RAG basics — retrieval-augmented generation pipelines
- AI-powered email, report, and content automation
Module 5: Cloud Deployment and Monitoring
- Deploying n8n on VPS or cloud platforms
- Environment variables and secrets management
- Monitoring, alerts, and pipeline health checks
- Scaling automation workflows
Module 6: Capstone Project and Portfolio
- End-to-end real-world automation project
- Code review and optimization session
- Portfolio documentation and GitHub setup
- Career paths in AI automation engineering
Ahsan Akhtar
Senior Test Automation Engineer & AI Trainer
This Course Includes
Duration 6-8 weeks (Weekend Classes Only)
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