marvelefe/machinelearning101
marvelefe/machinelearning101 has 10 GitHub stars and 3 forks. GitHot measured 0 stars in 24 hours and -20 in 7 days. Last checked 2026-11-12 16:24 UTC. Last push 2026-11-01.
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An interactive, browser-based machine learning learning platform designed for beginners. Learn ML concepts through hands-on experimentation with zero setup required
What you can do
- 🎯 Interactive learning — Real-time parameter adjustments with immediate visual feedback
- 📊 Rich visualizations — Interactive 2D/3D charts powered by Chart.js, D3.js, and Plotly.js
- 🧪 Hands-on experiments — Train models, tune parameters, and inspect results instantly
- 📚 Guided content — Clear explanations, interactive quizzes, and progress tracking
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README.md
OriginalMachine Learning 101
Learn machine learning by doing — right in your browser.
An interactive, open-source learning platform for beginners. Adjust parameters, watch algorithms run, and build real intuition — with zero setup required.
👉 Try it live at machinelearning101.com
🌟 What is this?
Machine Learning 101 is a free, browser-based platform for building intuition about machine learning. Instead of just reading theory, you experiment: tweak a learning rate and watch gradient descent converge, drag points and see a decision boundary move, add clusters and watch K-means settle.
Everything runs client-side — no installs, no sign-up required, no backend needed. Your progress is saved locally in your browser (with optional cloud sync if you sign in).
Zero setup · Visual-first · Learn by doing · Open source
✨ Features
- 🎯 Interactive learning — Real-time parameter adjustments with immediate visual feedback
- 📊 Rich visualizations — Interactive 2D/3D charts powered by Chart.js, D3.js, and Plotly.js
- 🧪 Hands-on experiments — Train models, tune parameters, and inspect results instantly
- 📚 Guided content — Clear explanations, interactive quizzes, and progress tracking
- 🔒 Privacy-focused — Optional, opt-in analytics with full user control and GDPR compliance
- 📱 Responsive — Works on desktop, tablet, and mobile
- ⚡ Zero setup — No installations, no backend dependencies, no barriers to learning
- 🌐 Fully static — Deployable to any static host (Netlify, Vercel, GitHub Pages)
- 🔐 Optional cloud sync — Sign in to keep progress across devices (Cloudflare Workers + D1)
Learning domains
| Domain | Topics |
|---|---|
| Regression | Linear regression, logistic regression |
| Classification | KNN, decision trees, SVM |
| Clustering | K-means, hierarchical clustering |
| Preprocessing | Feature scaling, encoding, imputation, feature selection |
| Optimization | Gradient descent, momentum, RMSProp, Adam |
| Advanced | PCA, t-SNE, neural networks, CNNs, hyperparameter tuning |
🚀 Getting Started
Prerequisites
Run Locally
# Clone the repository
git clone https://github.com/marvelefe/machinelearning101.git
cd machinelearning101
# Install dependencies
npm install
# Start the dev server
npm run dev
Open http://localhost:3000 🎉
Useful Commands
| Command | Description |
|---|---|
npm run dev |
Start the development server |
npm run build |
Build for production |
npm run preview |
Preview the production build locally |
npm run generate |
Generate the fully static site |
npm run test |
Run unit tests (Vitest) |
npm run type-check |
Run TypeScript type checking |
🏗️ Project Structure
.
├── algorithms/ # ML algorithm implementations (extend BaseAlgorithm)
│ ├── advanced/ # Neural networks, PCA, t-SNE
│ ├── classification/ # KNN, decision trees, SVM
│ ├── clustering/ # K-means, hierarchical
│ ├── optimization/ # Gradient descent variants
│ ├── preprocessing/ # Scaling, encoding, imputation
│ └── regression/ # Linear, logistic regression
├── components/ # Vue components
│ ├── layout/ # Header, footer, navigation
│ ├── modules/ # Module-specific UI
│ ├── shared/ # Reusable components
│ └── visualizations/ # Chart wrappers
├── composables/ # Vue composables (reusable logic)
├── content/ # Learning content (markdown + JSON)
│ └── modules/ # Organized by domain
├── pages/ # Nuxt pages (auto-routing)
├── stores/ # Pinia state management
├── types/ # TypeScript definitions
├── utils/ # Utility functions
├── tests/ # Unit tests (Vitest)
├── worker/ # Optional Cloudflare Worker (auth backend)
└── service-workers/ # Web Workers (heavy computation)
🛠️ Tech Stack
- Framework: Nuxt 4 (Vue 3 + TypeScript, strict mode)
- State: Pinia with persistence
- Styling: Tailwind CSS
- Visualizations: Chart.js, D3.js, Plotly.js
- Backend (optional): Cloudflare Workers + D1
- Testing: Vitest
- Build: Vite, with static site generation
📚 Documentation
| Guide | Description |
|---|---|
| User Guide | How to use the platform |
| Developer Guide | Development setup and workflow |
| Module Creation Guide | Add a new learning module |
| State Management | How Pinia stores are organized |
| Quiz Integration | Adding quizzes to modules |
| Bookmark Integration | Bookmarking support |
| Core Web Vitals | Performance notes |
| Deployment Guide | Deploying the site & backend |
| Worker README | Optional auth backend |
🔐 Optional Authentication Backend
Signing in enables cross-device progress sync via Cloudflare Workers + D1 and Google OAuth. This is entirely optional — the site works fully without it. See the Worker README and the Deployment Guide to set it up.
🤝 Contributing
Contributions are warmly welcomed — code, content, docs, design, and bug reports alike! 💚
- 📖 Read the Contributing Guide to get started
- 🙋 Browse good first issues — perfect for newcomers
- 🤝 Check out help wanted issues for meatier tasks
- 💬 Ask questions in Discussions
- 🐛 Report a bug · 💡 Request a feature
Please note that this project is released with a Contributor Code of Conduct. By participating you agree to abide by its terms.
🔒 Security
Found a vulnerability? Please report it privately — see our Security Policy. Do not open a public issue for security concerns.
📄 License
This project is licensed under the MIT License — see the LICENSE file for details.
⭐ Support the Project
If Machine Learning 101 helped you learn something, consider giving it a star ⭐ — it helps others discover the project. And if you'd like to contribute, we'd love to have you!
Made with ❤️ for learners everywhere.
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