AI & Automation — from zero to production.
A complete beginner-to-pro curriculum teaching students to understand, build, and deploy AI and automation systems. Self-paced study guide with hands-on labs.
The most valuable skill of this decade
AI literacy is becoming a hiring filter in every field — business, health, and trades alike. This track takes a complete beginner to someone who can build, deploy, and operate real AI automations. Every module ends with labs where you build something real.
Modules & lessons
Module 1 · AI Foundations Beginner 2 lessons ›
What is Artificial Intelligence?
🎯 Explain AI vs ML vs DL; identify real-world AI.Artificial Intelligence is the field of making machines perform tasks that need human intelligence. Machine Learning (ML) is a subset where systems learn patterns from data. Deep Learning uses neural networks with many layers. We meet AI daily in spam filters, recommendations, and voice assistants. Key idea: AI is a tool, not magic — it is only as good as its data and the problem framing.
✍️ Self-check question
Give one example where a rule-based system beats ML, and one where ML is essential.
How Machines Learn (Data, Models, Training)
🎯 Describe the supervised learning loop.A model is trained on labelled examples: it adjusts internal parameters to minimize error. Training = optimization (gradient descent). Inference = using the trained model on new data. Key terms: features (inputs), labels (targets), loss function, epoch, overfitting (memorizing instead of generalizing). The golden rule: never test on your training data.
✍️ Self-check question
Why is a 100% accurate model on training data still suspicious?
Module 2 · Prompt Engineering & LLMs Beginner 2 lessons ›
Talking to LLMs Effectively
🎯 Write zero-shot, few-shot, and role prompts.Large Language Models respond to well-structured prompts. Core techniques: clear instruction, context, examples (few-shot), role assignment ('You are a tutor...'), and chain-of-thought ('think step by step'). Always specify the output format (JSON, bullet list, tone). Small prompt changes can swing results dramatically — prompt engineering is the new literacy.
✍️ Self-check question
When would few-shot prompting beat a long instruction?
Building with the LLM API
🎯 Call an LLM via code safely.Use the chat-completions pattern: system message (sets role/rules) + user message (the ask), with temperature (creativity) and max_tokens (length) controls. Keep API keys secret (environment variables, never in code). Batch requests with rate-limit handling. Prefer local/free models (Ollama) to avoid per-call cost at scale.
✍️ Self-check question
Why should temperature be near 0 for a math tutor but higher for a brainstorming bot?
Module 3 · Automation Fundamentals Intermediate 2 lessons ›
Workflow Automation Concepts
🎯 Map a manual process to an automated one.Automation = trigger + action + conditions. First document the manual flow (inputs, steps, outputs, exceptions), then pick a tool: n8n/Make/Zapier for no-code, or Python for full control. Always build for failure: what happens when a step errors? The best automations have clear logging and a human fallback.
✍️ Self-check question
Name three manual tasks in your study life you could automate this week.
Connecting APIs & Webhooks
🎯 Use REST APIs and webhooks.REST = request/response over HTTP (GET reads, POST creates). Webhooks = a server pushes events to your URL when something happens. Authenticate with API keys or OAuth. Parse JSON. Handle errors with retries and exponential backoff. Rate limits are real — respect them or get blocked.
✍️ Self-check question
What is the difference between polling an API and receiving a webhook?
Module 4 · Building AI Agents Advanced 2 lessons ›
Agent Architecture (Tools, Memory, Planning)
🎯 Design a tool-using agent loop.An agent = LLM + tools + memory. The loop: perceive -> think -> act (call a tool) -> observe -> repeat until done. Give the model functions it can call (search, calculator, your API). Keep a conversation memory for context. Guard every tool call with input validation — an agent that can send email must be constrained.
✍️ Self-check question
What could go wrong if an agent has unrestricted access to send messages?
Retrieval-Augmented Generation (RAG)
🎯 Build a RAG pipeline over your docs.RAG = retrieve relevant chunks, then generate with that context. Steps: chunk documents, embed them, store in a vector database, query by similarity, rerank, then prompt the LLM with the retrieved text. This keeps answers grounded in YOUR data and current — no hallucination about facts you provided.
✍️ Self-check question
Why does chunk size matter in a RAG system?
Module 5 · Deployment & MLOps Professional 2 lessons ›
Shipping to Production
🎯 Containerize and serve an AI app.Package with Docker, expose via an API (FastAPI), add a reverse proxy (Caddy/Nginx) with TLS. Monitor latency and errors. Use systemd timers for scheduled jobs. Keep secrets out of code (env vars / secret managers). A working demo behind HTTPS beats a perfect notebook on localhost.
✍️ Self-check question
Why is 'it works on my machine' not good enough for production?
Observability, Cost & Safety
🎯 Run AI safely and cheaply.Log every call, cap token spend, cache repeated responses, and prefer local models for high-volume tasks. Add guardrails: input validation, output filtering, and human-in-the-loop for risky actions. Audit monthly. The cheapest token is the one you didn't need to send.
✍️ Self-check question
List three guardrails you would add before letting an agent act on a real bank account.
Track quiz
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The tutor knows this entire track — ask it to explain any lesson, debug your lab, or plan your learning path.
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