AI Engineering from Scratch: Guide to Building Intelligent Systems
Overview
Learn AI engineering from scratch with this comprehensive guide. Master tools, frameworks, and best practices for building intelligent systems.
S
Saiyp Editorial
Jun 06, 2026
In an era where 84% of students already use AI tools but only 18% feel professionally prepared to build them, a new open-source curriculum has emerged to close the gap. AI Engineering from Scratch is a rigorous, hands-on manual designed to take learners from the raw fundamentals of mathematics to the cutting edge of autonomous agent swarms.
This is not a collection of passive video lectures. It is a battle-tested reference manual—MIT licensed, community-driven, and built for the engineer who wants to understand what happens under the hood of PyTorch, Transformers, and LLMs.
π The Philosophy: Learn It. Build It. Ship It.
Most AI courses teach in scattered pieces: a paper here, a fine-tuning tutorial there. This curriculum provides the spine. Created by Rohit Ghumare and maintained by the community, it is structured around a simple premise:
"You don't just learn AI. You build it. End-to-end. By hand."
The methodology follows a strict loop for every single lesson:
- Read the Problem: Understand the concrete pain point.
- Derive the Math: Get the intuition and diagrams.
- Write the Code: Implement algorithms from raw math, without frameworks.
- Run the Test: Validate your understanding.
- Keep the Artifact: Ship a reusable tool (Prompt, Skill, Agent, or MCP Server).
πΊοΈ The Structure: 20 Phases, 503 Lessons
Spanning approximately 320 hours, the curriculum is divided into 20 stacked phases. You progress from Linear Algebra to Autonomous Systems, ensuring you never skip a foundational layer only to struggle at the top.
The journey covers four core programming languages: Python, TypeScript, Rust, and Julia.
Here is a snapshot of the curriculum's spine:
θ‘¨ζ Ό
| Phase Group | Key Focus Areas |
|---|---|
| The Foundation | Phase 0-2: Setup, Math Foundations (Linear Algebra, Calculus), ML Fundamentals (Regression, Trees). |
| Deep Learning Core | Phase 3-9: Neural Networks from scratch, Computer Vision (YOLO, Diffusion), NLP (Tokenization, Attention), Speech, and Reinforcement Learning. |
| The Frontier | Phase 10-12: LLMs from Scratch (Training, RLHF), LLM Engineering (RAG, Fine-tuning), and Multimodal AI (VLMs, Audio). |
| Agents & Production | Phase 13-17: Tools & Protocols (MCP), Agent Engineering (Loops, Memory), Autonomous Systems, Multi-Agent Swarms, and Infrastructure (vLLM, Kubernetes). |
| Ethics & Capstone | Phase 18-19: Safety, Alignment, and 17 End-to-End Capstone projects (e.g., Coding Agents, Research Agents, RAG systems). |
π οΈ The Toolkit: Reusable Artifacts
Unlike traditional courses that end with a certificate, this curriculum ends with a portfolio. Every lesson produces a reusable artifact that you can plug into your daily workflow or deploy as a service.
The repository ships with:
- 388+ Skills: Ready-to-use
SKILL.mdfiles for agents like Claude, Cursor, and Codex. - 99+ Prompts: Expert-level prompt templates for specific tasks.
- MCP Servers: Model Context Protocol integrations.
- The Agent Workbench: A complete capstone pack for running autonomous agents.
You can install the entire toolkit into your agent's directory with a single command:
bash
npx skills add rohitg00/ai-engineering-from-scratchπ Who Is This For?
Whether you are a beginner looking to break into AI or a senior engineer wanting to master Agent Engineering, the curriculum offers a tailored path:
- New to Programming: Start at Phase 0 (~306 hours).
- Know Python/ML: Jump to Phase 1 or 3 (~200-270 hours).
- Senior Engineers: Focus on Phase 10 (LLMs) or Phase 14 (Agents) for a ~60-hour deep dive.
π‘ Why It Matters
As industry leaders like Andrej Karpathy note, "The hottest new programming language is English." However, the skill that compounds is knowing what to build.
AI Engineering from Scratch removes the magic. It demystifies the black box, teaching you not just how to call an API, but how to build the model, train the loop, and deploy the agent. It covers foundational papers like Attention Is All You Need and Denoising Diffusion Probabilistic Models not just in theory, but in code.
If you are ready to move beyond copy-paste tutorials and truly understand the technology shaping our future, this is the manual. It is free, open source, and designed to be the last AI curriculum you will ever need.
Get started today:
- Repository: github.com/rohitg00/ai-engineering-from-scratch
- Website: aiengineeringfromscratch.com