Your Path to ML/AI Engineer

From fundamentals to production models.

Eight modules. Thousands of lines of code you'll write yourself. Real math, real models, real deployments. No toy datasets, no hand-waving.

Continue where you left off

Module 1 — Vectors & Matrices

Module 1 · Lesson 1 of 4 · ~45 min

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Curriculum

8 modules · ~130 hours total

Interactive Tools

Built for hands-on learning

Matrix Transformation Visualizer

Drag vectors. Apply matrices. See what linear algebra actually does.

Gradient Descent Playground

Watch optimization roll down a loss surface. Tune the learning rate.

Distribution Explorer

Slide the parameters. Watch the shapes. Build probabilistic intuition.

Every term in one searchable place. Filter by module, jump to the lesson that introduces it.

SM-2 spaced repetition over every glossary term. Per-module decks, progress saved locally.

The Engineering Context

📅 Timeline

1–2 hrs/day your pace
~4–6 months full curriculum
Module 1–3 ~5 weeks foundations
Module 4–8 ~3 months applied

💰 Cost

Free — everything in this repo
Free — Python, sklearn, PyTorch
Free tier — Colab GPU, AWS Lambda
Optional — ~$5–20/mo GPU rental later

🛠️ Stack

Python numpy, pandas, sklearn
PyTorch for deep learning
Hugging Face for modern AI
FastAPI + Lambda for deploy