Quick Start Guide
Get the environment running in about 5 minutes. For full navigation, read START-HERE.md first. Follow stages, not folder numbers 00→25.
Note: The week-by-week schedule below is an accelerated path for learners who already study full-time. The main README estimates 15–22 months full-time for the complete curriculum.
Step 1: Clone the Repository
git clone https://github.com/NabidAlam/road-to-machine-learning.git
cd road-to-machine-learning
If you already use this folder as a local checkout beside the Study Hub, run commands from inside road-to-machine-learning/.
Step 2: Set Up Environment
Option A: Using Anaconda
conda create -n ml-env python=3.11
conda activate ml-env
pip install -r requirements.txt
Option B: Using Python venv
python -m venv ml-env
ml-env\Scripts\activate # Windows
# source ml-env/bin/activate # Mac/Linux
pip install -r requirements.txt
Step 3: Install Jupyter
pip install jupyter notebook
Step 4: Start Learning
| Your background | Start here |
|---|---|
| Complete beginner | GETTING_STARTED.md → Iris project |
| Know Python | Module 01 → Module 02 |
| Know ML basics | Pick modules you need → projects in module 16–18 |
Step 5: First Project
- Go to
16-projects-beginner/project-02-iris-classification/ - Follow the README or run
python iris_classification.py - Move to house price prediction when ready
Accelerated schedule (optional)
| Weeks | Focus |
|---|---|
| 1–2 | Modules 00–01; start Module 19 SQL in parallel (Stage 1.5) |
| 3–4 | Modules 02–04 |
| 5–6 | Modules 05–07 |
| 7–8 | 2–3 beginner projects |
Then continue with deep learning (modules 09–12), GenAI (module 25), essential skills (20–21), and deployment (modules 13–14). See LEARNING_ROADMAP.md.
Need Help?
- START-HERE.md: pick your path
- README.md: full curriculum hub
- LEARNING_ROADMAP.md: visual stage map
Ready? Open GETTING_STARTED.md or 00-prerequisites/README.md.