From HTML to AI: The Complete Roadmap for IT Beginners

Start with web and programming fundamentals, then layer databases, APIs, and deployment before moving into data, ML, and GenAI; build small, deployable projects at each stage with tests, docs, and a short demo to create a job‑ready portfolio.

Stage 0: Setup and habits

• Tools: install VS Code, Git, a browser devtools workflow, and a password manager; create a clean template repo with README, src/tests, formatter/linter, and a one‑command run script.
• Practice loop: “learn one concept → build a tiny feature → write 1–2 tests → commit with clear message”; keep a log of what broke and how you fixed it.

Stage 1: Web foundations (HTML/CSS/JS)

• HTML: semantic tags, forms, accessibility basics; CSS: flexbox/grid, responsive layouts, variables; JS: DOM, events, fetch, modules.
• Mini projects: landing page, accessible form with validation, and a small interactive widget (todo/timer) with error states and mobile responsiveness.

Stage 2: Programming essentials

• Pick one language (Python or JavaScript/TypeScript) and learn variables, control flow, functions, collections, I/O, modules, errors, and testing.
• Build a CLI utility (e.g., expense splitter or task tracker) with 5+ unit tests and a simple performance note (time a hot function and refactor).

Stage 3: Data and databases

• SQL: SELECT, WHERE, JOIN, GROUP BY, window functions, indexing; modeling: keys, normalization, transactions.
• Project: a small app with SQLite/PostgreSQL, migrations, seed data, and a README showing before/after query latency with and without an index.

Stage 4: APIs and web backends

• Concepts: REST design, status codes, pagination, validation, auth (sessions/JWT), rate limiting, and consistent error models.
• Project: build an API with two entities and relationships, add integration tests, OpenAPI docs, Docker, and deploy to a free tier with a rollback plan.

Stage 5: Frontend integration

• SPA basics or server‑rendered pages; forms, state, and accessibility; performance budgets and basic caching.
• Project: a minimal dashboard that consumes your API, includes form validation, and tracks First Contentful Paint/Lighthouse score with one fix.

Stage 6: Cloud and DevOps basics

• CI/CD: run tests and linters on push; containers/devcontainers for reproducible dev; intro Infrastructure as Code to provision a database/service.
• Observability: logs, metrics, simple p95 latency target; run a small failure drill and document a rollback or hotfix plan in the README.

Stage 7: Security by default

• Hygiene: .gitignore for secrets, env variables or secret stores, dependency updates/scans, parameterized queries, and basic input validation.
• Artifact: add a security section to your repo with a mini threat model, SBOM/dependency scan output, and steps to rotate a leaked key.

Stage 8: Data analysis and ML essentials

• Data literacy: cleaning, EDA, visualization, feature engineering; modeling: baseline vs improved model, proper validation, and metric reporting.
• Project: tabular ML pipeline with a FastAPI inference endpoint, a model card, and a simple drift check; include accuracy/latency targets.

Stage 9: GenAI and RAG foundations

• Concepts: embeddings, chunking, retrieval, prompting, evaluation, latency/cost trade‑offs, and refusal/safety filters.
• Project: a small RAG app over your docs; create an offline eval set, track latency/cost per query, and write a “failure modes and fixes” note.

Stage 10: System design thinking

• Patterns: caching, queues, idempotency, rate limits, backpressure, and rollout/rollback strategies; read/write paths and consistency trade‑offs.
• Exercise: write a 1–2 page design note for your flagship project, with alternatives considered and a diagram.

Portfolio blueprint

• 3–5 repos with tests, CI badges, Docker/devcontainer, one‑command setup, and clear READMEs; each includes a design doc and a 2–4 minute demo.
• At least one cloud‑deployed API and one data/ML or RAG project; include a dashboard screenshot, an SLO summary, and a brief postmortem from a small failure drill.

6‑month plan (example)

• Months 1–2: HTML/CSS/JS + programming essentials; ship a CLI and an accessible landing page; set up CI and tests.
• Month 3: SQL and backend API with auth, pagination, and integration tests; deploy with Docker and a rollback plan.
• Month 4: Frontend dashboard + observability; set a p95 latency target and fix one bottleneck; add secret/dependency scans.
• Month 5: Data/ML project with model card and inference API; track metrics and add a drift check; record a short demo.
• Month 6: GenAI RAG app with offline eval and safety notes; write a consolidated case study and practice two mock interviews.

Study routine and guardrails

• Two 60–90 minute deep‑work blocks on fixed days; end each session with a commit and next step; switch to maintenance during exam weeks.
• Avoid tool sprawl: adopt new tech only to solve a current blocker; keep a “later” list for shiny tools.
• Tests before trust: verify any AI‑assisted code with unit/integration tests; document assistance and validation in your README.

Following this roadmap—shipping small, measured projects from HTML to AI with security and reliability built in—creates a credible portfolio and compounding skills, positioning beginners for internships and entry‑level roles across web, data, cloud, and GenAI.