Python Mastery Journal

From JavaScript/React developer to expert AI/ML engineer — one commit at a time.

This repository documents my 3-year transformation, following a rigorous daily roadmap.
It contains daily scripts, mini-projects, notes, and eventually fully deployed machine learning applications.

Why this exists

• Proof of work: A public, daily log of consistent learning.
• Personal reference: Searchable code snippets and cheat sheets.
• Portfolio: Every project that will be linked from my CV.
• Accountability: A daily commit habit that turns learning into a professional practice.

Repository structure

├── phase-0-launchpad/        # Days 1–2: Environment & syntax refresh
│   ├── day-1/
│   └── day-2/
├── phase-1-python-fluency/   # Weeks 1–4: Python deep dive & data wrangling
│   ├── day-3/
│   └── day-4/
├── phase-2-databases-apis/   # Weeks 5–6 (coming soon)
├── phase-3-ml-foundations/   # Weeks 7–12 (coming soon)
├── projects/                 # Standalone portfolio pieces
├── notes/                    # My cheat sheets & summaries
└── README.md

Roadmap at a glance

• Phase 0: Environment & syntax (Days 1–2)
• Phase 1: Python fluency & data wrangling (Weeks 1–4)
• Phase 2: Databases & APIs (Weeks 5–6)
• Phase 3: Machine Learning foundations (Weeks 7–12)
• Phase 4: Deep Learning with PyTorch (Weeks 13–18)
• Phase 5: LLMs & Agentic AI (Weeks 19–24)
• Phase 6: Specialization & scale (Months 7–12)
• Phase 7: Mastery & career elevation (Year 2–3)

Progress

Day 1 — Environment Setup

Set up Python environment: tested pyenv, settled on Miniconda. Created main conda env with Python 3.11, NumPy, Pandas, JupyterLab, FastAPI, Streamlit. Configured Git and SSH for GitHub.

• Day 1
• Environment

Day 2

Current status

Day 2 — Python syntax check, building small scripts to internalise the language.

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"The only way to eat an elephant is one bite at a time."