Learn Machine Learning with JavaScript

You already think in arrays, state, and components. Machine learning uses the same patterns — just with different names. Tensorcraft bridges the gap with 50+ analogies that map your frontend knowledge to ML concepts.

Why Frontend Developers Make Great ML Engineers

If you understand useState(), you understand model weights. If you can write array.map(), you can do tensor operations. If you've used fetch(), you already know how inference APIs work. The leap from frontend to ML is smaller than you think.

How Tensorcraft Works

• 1.Pick a theme — each teaches a different ML specialization through a unique story
• 2.Learn through bridges — every ML concept is mapped to a frontend concept you know
• 3.Code in the browser — write real TensorFlow.js code in interactive exercises
• 4.Build a real project — each theme culminates in a capstone ML model that runs at 60fps

No Python Required

Everything runs in the browser using TensorFlow.js. You write TypeScript, test in real-time, and deploy models that work on any device with a web browser. No Jupyter notebooks, no pip install, no GPU setup.

Start Free

Module 1 of Deep Orbit is completely free — no account, no credit card. Just open a lesson and start learning.

Available Now

• Deep Orbit — Time-Series & Signals

Coming Soon

Four more themes ship in waves. Each links to a stub page where you can join a per-theme waitlist:

• Neon Protocol — Computer Vision (coming soon)
• Signal Ward — NLP & Text Intelligence (coming soon)
• Nova Canvas — Multimodal & Generative AI (coming soon)
• Terra Grid — Edge AI & Production ML (coming soon)

Browse our ML Glossary for quick definitions of every concept, or check out our pricing to see what each plan includes.