Start simple, learn by building, and prove skills with a public portfolio—pair Python, math, and core ML with a small GenAI app, then add cloud deployment and one entry-level certification to unlock internships and junior roles.
What to learn first
- Foundations: Python, Git, Linux/CLI, SQL, and basic statistics (mean/variance, distributions, regression); these power all AI work.
- Core AI: supervised/unsupervised ML and a gentle intro to deep learning with TensorFlow or PyTorch; avoid advanced math until basics click.
Build real projects early
- Beginner projects: house‑price predictor, movie recommender, or a FAQ chatbot; focus on clean code, tests, and a clear README.
- GenAI starter: a retrieval‑augmented Q&A app on your notes; track latency, cost, and accuracy so you learn evaluation from day one.
Create a job‑ready portfolio
- Publish repos with unit tests, evaluation reports, and a short post‑mortem explaining trade‑offs; recruiters value proof over promises.
- Add one end‑to‑end project: containerize, deploy to a cloud free tier, and include a simple dashboard for monitoring.
Certifications that help beginners
- Pick one cloud‑aligned, beginner‑friendly path to signal skills fast: Google Cloud GenAI badge, IBM AI Engineering (Coursera), or Microsoft AI Fundamentals.
- If targeting software testing/QA, follow an AI‑in‑testing track that teaches smart test generation and failure prediction.
India‑friendly routes
- Many programs offer INR pricing and flexible schedules; local bootcamps in hubs like Hyderabad and Pune add live projects and placement support.
- Roadmaps tailored to India emphasize Python first, hands‑on projects, and community participation (meetups, hackathons) to build momentum.
A simple 30‑day starter plan
- Week 1: Python + Pandas basics; build a tiny dataset project; start a GitHub repo and daily commit habit.
- Week 2: Learn train/validate/test, fit a baseline ML model, and write 3–5 unit tests; read one beginner guide end to end.
- Week 3: Build a small RAG chatbot; document assumptions, costs, and limitations; share a demo link.
- Week 4: Containerize and deploy; add a README with metrics and a short post‑mortem; register for a beginner certification.
Study routine that compounds
- 90‑minute focused blocks, 5 days a week; one mini‑project per week; keep prompt/code provenance to learn from mistakes and show integrity.
- Join a local or online community for feedback and accountability; apply to internships once you have 2–3 solid repos.
Bottom line: start with Python and basic ML, learn by shipping small projects, and earn one entry‑level credential—this focused path builds momentum, confidence, and evidence that opens doors in AI‑enabled tech roles.
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