AI & Automation — Private Tutor Track
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.
Module 1 · AI Foundations
BeginnerWhat is Artificial Intelligence?
🎯 Objective: 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.
How Machines Learn (Data, Models, Training)
🎯 Objective: 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.
Module 2 · Prompt Engineering & LLMs
BeginnerTalking to LLMs Effectively
🎯 Objective: 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.
Building with the LLM API
🎯 Objective: 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.
Module 3 · Automation Fundamentals
IntermediateWorkflow Automation Concepts
🎯 Objective: 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.
Connecting APIs & Webhooks
🎯 Objective: 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.
Module 4 · Building AI Agents
AdvancedAgent Architecture (Tools, Memory, Planning)
🎯 Objective: 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.
Retrieval-Augmented Generation (RAG)
🎯 Objective: 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.
Module 5 · Deployment & MLOps
ProfessionalShipping to Production
🎯 Objective: 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.
Observability, Cost & Safety
🎯 Objective: 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.