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AI Automation (n8n + LLMs)
Build automation workflows with n8n, integrate AI models, handle errors, and ship real automation projects with webhooks and APIs.
8 Weeks
4 Lessons
Certification on completion
3 Mentors
1 Project
Course overview
About this skill path.
This track teaches practical automation engineering using n8n. You’ll learn expressions and JSON transformations, integrate AI/LLMs for summarization and extraction, build modular workflows with sub-workflows, handle errors like a pro, and complete a capstone automation project.
What you'll learn.
Each track is carefully structured to take you from basics to real-world application.
- n8n fundamentals: triggers, actions, data flow
- Expressions and JavaScript inside n8n
- JSON vs Binary data handling
- Data transformation and notifications
- LLM integration: prompts, extraction, summarization, structured output
- Modular workflows: loops, sub-workflows, reusability (DRY)
- APIs, webhooks, error handling, retry patterns
- Capstone automation delivery + exported workflows
Curriculum
Course syllabus
2 units
·2 Assignment
What's included:
- n8n interface, nodes, connections, manual triggers
- Data flow between nodes; pinning data for testing
- Expression editor: {{ }} syntax; $json/$input/$prev
- Basic JS operations in expressions (concat, math)
- Logic and branching with If/Switch nodes
2 units
·2 Assignment
What's included:
- JSON vs Binary data and file handling
- Move Binary Data node (convert/rename files)
- Edit Fields/Set node for key renaming and field trimming
- String manipulation in expressions (.split, .replace, casing)
- Mini project: expense categorizer workflow
2 units
·2 Assignment
What's included:
- Chat model node setup and credentials
- System vs user prompts; dynamic expressions in prompts
- Prompt controls: temperature, token limits, robustness
- Summarization and extraction (names/dates/action items)
- Structured output / JSON mode for automation safety
2 units
·1 Assignment
What's included:
- Loops with Split In Batches; aggregation after iteration
- Execute Workflow node and DRY principles
- Create a reusable “tool workflow” (e.g., notifier)
- Practice project: Daily Briefing Bot (weather/news → AI summarize → email)
2 units
·2 Assignment
What's included:
- HTTP requests (GET/POST/PUT) and auth patterns
- Receiving data via webhooks
- Error Trigger workflows; Continue on Fail strategies
- Try/catch-style patterns via branching and fallbacks
- Project: resilient lead form with backup sheet on failure
2 units
·2 Assignment
What's included:
- Code node fundamentals: $input.all(), returning [{ json: {} }]
- Advanced transforms: merge arrays by ID, regex parsing
- Date handling and timezone conversions (Luxon concepts)
- Building robust outputs for downstream nodes
2 units
·1 Assignment
What's included:
- Choose a capstone option (repurposing engine, support drafts, lead enrichment, etc.)
- Architecture planning: nodes, data schema, sub-workflows, failure paths
- Implementation of core workflow chain + AI nodes
2 units
·1 Assignment
What's included:
- Add error workflows and final fallback paths
- Testing, pinning, and validation of outputs
- Deliverables: exported JSON workflow + demo + resilience proof
- Presentation-ready project handoff
We believe learning practical tech skills should be affordable.
We've stripped away the fluff and focused on what matters. This is the best value-for-money tech education you'll find.
Special Offer
One-time payment
₦10,000/track
- 8 weeks intensive training
- Live classes & Recordings
- Weekly mentorship sessions
- Real-world portfolio projects
- Certificate of Completion
- Community Access