Learn Machine Learning from absolute zero — no math background, no coding experience required.
🔰 Status: Days 1-2 Available | Python Basics & Math Foundations
week01-linear-regression– What is ML + build your first model
By the end of Week 1, you'll understand linear regression and build your first ML model from scratch.
Current Content:
- Day 1: Python basics (variables, lists, loops, functions, NumPy)
- Day 2: Math foundations (lines, slope, predictions, error)
- Day 3+: Coming soon...
How to use this course:
This allows you to save your work and submit solutions!
# 1. Fork this repository on GitHub (click "Fork" button)
# 2. Clone YOUR fork
git clone https://github.com/YOUR-USERNAME/zero-to-ml.git
cd zero-to-ml
# 3. Create your student folder
mkdir -p students/YOUR-NAME/week01| Method | Best For | Setup |
|---|---|---|
| VS Code Local ⭐ | Regular practice, offline work | Instructions |
| GitHub Codespaces | VS Code in browser | Instructions |
| Google Colab | Quick experiments | Instructions |
# Install Python 3.8+ from python.org
# Install VS Code from code.visualstudio.com
# In your cloned folder:
pip install numpy matplotlib jupyter ipykernel
# Open in VS Code
code .
# Open week01-linear-regression/01-python-primer.ipynb- On YOUR forked repo, click
Code→Codespaces→Create codespace - Wait for VS Code to load in browser
- Open
week01-linear-regression/01-python-primer.ipynb
- Go to colab.research.google.com
File→Open notebook→GitHub→ Enter YOUR fork URL⚠️ Note: Save copies to Drive, then manually copy to your repo later
📖 Detailed setup instructions: SETUP_GUIDE.md
Submission instructions have been consolidated into SUBMISSION_WORKFLOW.md. Please follow that document for step-by-step guidance, PR templates, naming conventions, and grading criteria.
Refer to that file before preparing your students/<your-name>/ folder and opening a Pull Request.
week01-linear-regression/
├── 01-python-primer.ipynb # Day 1: Python basics
└── 02-math-foundations.ipynb # Day 2: Understanding lines & predictions
Learning Path:
- Start with Day 1 (even if you know some Python)
- Complete exercises in each notebook
- Move to Day 2
- Check Week 1 README for external resources
Designed for absolute beginners:
- ✅ No math beyond high school algebra
- ✅ Every concept explained visually
- ✅ Real examples (study hours, sleep) not abstract math
- ✅ Hands-on exercises in every section
- ✅ Completely free and open source
What makes it different:
- Starts at true zero
- Visual-first approach
- Builds confidence through small wins
- Community-driven improvements
See SUBMISSION_WORKFLOW.md for the official submission workflow, examples, and PR templates. That document covers forking, folder structure, committing, and submitting Pull Requests.
File: SUBMISSION_WORKFLOW.md — ./SUBMISSION_WORKFLOW.md
Want to make this better? Here's how:
- 📝 Add new exercises or examples
- 🎨 Create visualizations or diagrams
- 🌍 Translate content to other languages
- 🐛 Fix typos or bugs
- 💡 Improve explanations
- 📹 Create video tutorials
Open an Issue to discuss ideas before making big changes!
- Stuck? Open an Issue
- Questions? Use Discussions
- Bug found? Report it in Issues
No question is too basic — this is for beginners!
Now Available:
- ✅ Day 1: Python Primer
- ✅ Day 2: Math Foundations
Coming Soon:
- 🔜 Day 3-5: Building Linear Regression
- 🔜 More coming soon...
- SETUP_GUIDE.md - Detailed setup for all platforms
- Week 1 README - Learning resources & tips
- Contributing Guidelines - How to contribute (coming soon)
Open source for educational use. (License to be added)
Ready to start? → Week 1, Day 1 🚀
Questions? Open an Issue | Want updates? Star the repo ⭐