📊 Structure Overview

🗓 Total Duration: 9 Months (approx. 36 Weeks)
⏰ Weekly Commitment: ~20–25 hours/week
🧠 4 Pillars of Learning:
1. Theory (Courses)
2. Practice (Projects + Assignments)
3. Math Foundation
4. Career (Portfolio + GitHub + Networking)

🗓 Month 0 (Week 0) – Prep Week

Area	Tasks
Python Skills	Refresh NumPy, Pandas, matplotlib, OOP
Tools Setup	Install VS Code, GitHub, Anaconda/Jupyter, Docker (optional)
Accounts	Kaggle, GitHub, Coursera, HuggingFace, Google Colab, Weights & Biases
Reading	Blog: "How to learn ML the hard way" by Karpathy

Concepts: Python syntax, functions, loops, OOP basics
Math Focus: None
Tools & Libraries: Jupyter, VSCode, Google Colab, GitHub
Practice Tasks: Write 3 simple data-wrangling scripts; Create a class-based calculator
Mini Project: Student Grading System using CSV
Resources: W3Schools Python, Automate the Boring Stuff, Python OOP Tutorial