📅 Weekly Study Plan
A consistent daily routine beats intensive cramming. 5–6 hours of focused learning per day, 6 days a week, completes this roadmap in 12 months.
Daily Routine Template
| Time Block | Activity | Duration |
|---|---|---|
| Morning (6–7am) | Review previous day notes + Anki flashcards | 1 hr |
| Core Block (7–9am) | Primary learning — course / reading / coding | 2 hrs |
| Lunch Break | Passive learning — YouTube, podcasts | 30 min |
| Afternoon (5–7pm) | Project work or DSA practice | 2 hrs |
| Evening (9–10pm) | Write daily learning log + commit to GitHub | 1 hr |
52-Week Phase Map
| Weeks | Phase | Primary Focus |
|---|---|---|
| 1–2 | Phase 0 | Environment setup, Git, AI landscape overview |
| 3–10 | Phase 1 | Python fundamentals + DSA patterns |
| 11–15 | Phase 2 | Linear algebra, calculus, stats, NumPy, Pandas |
| 16–19 | Phase 3 | Classical ML — regression, trees, ensembles, pipelines |
| 20–23 | Phase 4 | Deep learning — CNN, RNN, Transformer, PyTorch |
| 24–27 | Phase 5 | Generative AI — RAG, fine-tuning, LLMs |
| 28–29 | Phase 6 | Agentic AI — LangGraph, CrewAI, tool use |
| 30–31 | Phase 7 | MLOps — FastAPI, Docker, MLflow, monitoring |
| 32–36 | Phase 8 | Portfolio polish, job prep, mock interviews |
| 37–52 | Ongoing | Kaggle competitions, OSS contributions, networking |
Weekly Rhythm
Mon–Fri · Deep Work
- Primary learning block: course, book, or code-along
- Build or extend current phase project
- 2–3 DSA problems (during Phase 1)
- End-of-day GitHub commit + learning log entry
Saturday · Project Day
- Full project session — build a meaningful feature
- Write a short README or documentation update
- Review the week's notes and consolidate learnings
- Plan next week's learning targets