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AI & Data • Step-by-Step Curriculum

AI Tools & Coding with AI Setup (Everything Before Coding & Debugging)

Everything a beginner or builder must learn first before coding and debugging with AI: IDE setup, terminal & Git safety, context prompting, diff review, and test-driven verification.

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Practical Equipment & Study Requirements
What laptop, phone, internet, and free tools you need for this field—plus cloud options if your device is low-spec.
Laptop / PC Specs

Minimum:

Recommended:

Free Cloud Option:

Phone & Internet

Laptop Essential

Internet: Low-to-moderate (~250 MB/week) for IDE extensions, docs, and AI chat.

Free Software & Hardware Kit

Hardware / Kit: None required

Beginner Stage

Zero prior experience required — core concepts & first hands-on builds.

Step 1 beginner ~6 hrs

Setting Up Your AI Workspace (VS Code, AI Assistants & Terminal)

Install and configure VS Code, terminal shells, browser DevTools, and AI coding assistants cleanly.

What You Will Learn
  • VS Code extensions, workspaces & integrated terminal
  • ChatGPT / Gemini / Copilot / Antigravity roles
Hands-On Milestone Project: Set up VS Code with integrated terminal, Git, and an AI assistant on a sample repo.
Step 2 beginner ~6 hrs

Prerequisites Before AI Coding: Files, Git & Secret Hygiene

Never paste .env files, passwords, or API keys into AI chats — learn Git branches and safety first.

What You Will Learn
  • git status, diff, commit & rollback safety
  • .env, .gitignore & credential protection
Hands-On Milestone Project: Initialize a Git repo with .gitignore and make 3 clean checkpoint commits.
Intermediate Stage

Build real multi-part projects, tooling fluency, and problem-solving speed.

Step 3 intermediate ~8 hrs

Context-First Prompting: Specs, File Paths & Constraints

Stop getting broken guesses from AI by providing exact error messages, file paths, and clear goals.

What You Will Learn
  • Spec-before-code prompting
  • Scoping changes to specific files & functions
Hands-On Milestone Project: Write a structured feature prompt that produces a clean, minimal diff.
Step 4 intermediate ~8 hrs

Reviewing AI Code Safely (Diffs, Hallucinations & Security)

Spot invented packages, SQL injection, broken auth checks, and duplicated logic before accepting code.

What You Will Learn
  • Reading git diff line-by-line
  • Verifying package names & framework docs
Hands-On Milestone Project: Audit an AI-generated pull request and fix 3 planted security/logic issues.
Advanced Stage

Production patterns, architecture, automation, and security rigor.

Step 5 advanced ~10 hrs

Debugging with AI: Stack Traces, Logs & Minimal Repros

Use browser DevTools, Laravel/Python logs, and minimal reproductions so AI pinpoints root causes fast.

What You Will Learn
  • Reading stack traces top-to-bottom
  • Network tab, 422/500 inspection & log tailing
Hands-On Milestone Project: Diagnose and fix a failing API + UI bug using DevTools and server logs.
Expert Stage

Scale, observability, testing, leadership, and real-world deployment.

Step 6 expert ~10 hrs

Test-First AI Workflow: Automated Verification Before Shipping

Require automated tests (PHPUnit/Pytest/Playwright) after every AI edit so regressions never slip in.

What You Will Learn
  • Writing failing repro tests first
  • Running full test suites after every AI patch
Hands-On Milestone Project: Build a small feature end-to-end with AI where every change is verified by passing tests.
Field Mastery Assessment

AI Tools & Coding with AI Setup (Everything Before Coding & Debugging) — Practical Mastery Assessment

Score 80% or higher (5 scenario questions) + submit your real practical capstone project after at least 7 days of study to earn a Verified MESHLUMA Certificate.

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Tests are given after studying all 6 steps to prove real practical mastery.

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