ai-engineering-from-scratch

rohitg00/ai-engineering-from-scratch

Learn it. Build it. Ship it for others.

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Python MITLast pushed Jun 25, 2026

Overview

A comprehensive curriculum with 503 lessons and 20 phases covering deep learning, generative AI, NLP, computer vision, reinforcement learning, and more. It's designed to help students build various AI components from scratch using multiple programming languages including Python.

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pip install ai-engineering-from-scratch

README

MIT License 503 lessons 20 phases GitHub stars Website

From the creator of Agent Memory - #1 Persistent memory ⭐ GitHub stars which naturally works with any agents or chat assistants.

β–‘β–‘β–‘β–’β–’β–’β–‘β–‘β–‘β–’β–’β–’β–‘β–‘β–‘β–’β–’β–’β–‘β–‘β–‘β–’β–’β–’β–‘β–‘β–‘β–’β–’β–’β–‘β–‘β–‘β–’β–’β–’β–‘β–‘β–‘β–’β–’β–’β–‘β–‘β–‘β–’β–’β–’β–‘β–‘β–‘β–’β–’β–’β–‘β–‘β–‘β–’β–’β–’β–‘β–‘β–‘β–’β–’β–’β–‘β–‘β–‘β–’β–’β–’β–‘β–‘β–‘β–’β–’β–’β–‘β–‘β–‘β–’β–’β–’β–‘β–‘β–‘β–’β–’β–’

84% of students already use AI tools. Only 18% feel prepared to use them professionally. This curriculum closes that gap.

503 lessons. 20 phases. ~320 hours. Python, TypeScript, Rust, Julia. Every lesson ships a reusable artifact: a prompt, a skill, an agent, an MCP server. Free, open source, MIT.

You don't just learn AI. You build it. End-to-end. By hand.

150,639 readers Β Β·Β  241,669 page views in the last 30 days Β Β·Β  as of 2026-06-07

How this works

Most AI material teaches in scattered pieces. A paper here, a fine-tuning post there, a flashy agent demo somewhere else. The pieces rarely line up. You ship a chatbot but can't explain its loss curve. You hook a function to an agent but can't say what attention does inside the model that's calling it.

This curriculum is the spine. 20 phases, 503 lessons, four languages: Python, TypeScript, Rust, Julia. Linear algebra at one end, autonomous swarms at the other. Every algorithm gets built from raw math first. Backprop. Tokenizer. Attention. Agent loop. By the time PyTorch shows up, you already know what it's doing under the hood.

Each lesson runs the same loop: read the problem, derive the math, write the code, run the test, keep the artifact. No five-minute videos, no copy-paste deploys, no hand-holding. Free, open source, and built to run on your own laptop.

β–‘β–‘β–‘β–’β–’β–’β–‘β–‘β–‘β–’β–’β–’β–‘β–‘β–‘β–’β–’β–’β–‘β–‘β–‘β–’β–’β–’β–‘β–‘β–‘β–’β–’β–’β–‘β–‘β–‘β–’β–’β–’β–‘β–‘β–‘β–’β–’β–’β–‘β–‘β–‘β–’β–’β–’β–‘β–‘β–‘β–’β–’β–’β–‘β–‘β–‘β–’β–’β–’β–‘β–‘β–‘β–’β–’β–’β–‘β–‘β–‘β–’β–’β–’β–‘β–‘β–‘β–’β–’β–’β–‘β–‘β–‘β–’β–’β–’β–‘β–‘β–‘β–’β–’β–’

The shape of the curriculum

Twenty phases stack on top of each other. Math is the floor. Agents and production are the roof. Skip ahead if you already know the lower layers, but don't skip and then wonder why something at the top is breaking.

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flowchart TB
  P0["Phase 0 β€” Setup & Tooling"] --> P1["Phase 1 β€” Math Foundations"]
  P1 --> P2["Phase 2 β€” ML Fundamentals"]
  P2 --> P3["Phase 3 β€” Deep Learning Core"]
  P3 --> P4["Phase 4 β€” Vision"]
  P3 --> P5["Phase 5 β€” NLP"]
  P3 --> P6["Phase 6 β€” Speech & Audio"]
  P3 --> P9["Phase 9 β€” RL"]
  P5 --> P7["Phase 7 β€” Transformers"]
  P7 --> P8["Phase 8 β€” GenAI"]
  P7 --> P10["Phase 10 β€” LLMs from Scratch"]
  P10 --> P11["Phase 11 β€” LLM Engineering"]
  P10 --> P12["Phase 12 β€” Multimodal"]
  P11 --> P13["Phase 13 β€” Tools & Protocols"]
  P13 --