ai-engineering-from-scratch — What is it?

rohitg00/ai-engineering-from-scratch is an open-source, comprehensive curriculum for learning and building AI applications from scratch, addressing the gap between AI knowledge and practical application skills.

⭐ 25,369 Stars 🍴 4,112 Forks Python MIT Author: rohitg00
Source: README View on GitHub →

Why it matters

This project is gaining attention due to its unique approach of providing a structured, hands-on learning experience in AI engineering, covering a wide range of topics from math foundations to autonomous systems. It addresses the pain point of students and professionals feeling unprepared to use AI tools professionally. The project stands out for its focus on building reusable artifacts and its use of multiple programming languages.

Source: Synthesis of README and project traits

Core Features

Structured Curriculum

The curriculum is divided into 20 phases and 473 lessons, covering a broad spectrum of AI topics from linear algebra to autonomous systems. Each lesson is designed to build from the ground up, starting with raw math and progressing to implementation in various programming languages.

Source: README
Hands-on Learning

Every lesson includes a 'Build It / Use It' approach, where learners first implement algorithms from scratch and then use them through production libraries like PyTorch or sklearn. This method ensures a deep understanding of AI concepts.

Source: README
Reusable Artifacts

Each lesson results in a reusable artifact such as prompts, skills, agents, or MCP servers, which learners can integrate into their daily workflows.

Source: README
Multi-Language Support

The curriculum supports Python, TypeScript, Rust, and Julia, catering to a diverse set of learners and allowing for a more practical understanding of AI across different programming paradigms.

Source: README

Architecture

The project follows a modular architecture with distinct phases and lessons. Each lesson is structured with code, documentation, and outputs. The code is organized into folders based on phase and lesson names, with separate directories for different programming languages. Dependencies are managed through a requirements.txt file.

Source: Code tree + dependency files

Project Knowledge Graph

Knowledge graph: project (center) + core features (inner hexagons) + key dependencies (outer chips) numpy matplotlib jupyter torch torchvision Structured CurriculumStructured Curricul… Hands-on Learning Reusable Artifacts Multi-Language SupportMulti-Language Supp… ai-engineering-from-… Project Core feature Key dependency

Center: project; inner ring: core feature modules; outer ring: key dependencies. Auto-generated from core_features and tech_stack.key_deps.

Tech Stack

LanguagePythonFrameworkPyTorch, sklearn, Jupyter
numpymatplotlibjupytertorchtorchvisiontorchaudiotransformersdatasetstokenizersacceleratescikit-learnpandaspillowlibrosasoundfiletiktokenanthropicopenai
Not enough information.
Source: Dependency files + code tree

Quick Start

git clone https://github.com/rohitg00/ai-engineering-from-scratch.git cd ai-engineering-from-scratch python phases/01-math-foundations/01-linear-algebra-intuition/code/vectors.py
Source: README Installation/Quick Start

Use Cases

This project is suitable for students, professionals, and anyone interested in learning AI engineering. It is useful for those who want to gain a deep understanding of AI concepts and build practical AI applications. Specific scenarios include learning AI from scratch, enhancing professional AI skills, and developing AI projects.

Source: README

Strengths & Limitations

Strengths

  • Strength 1: Comprehensive curriculum covering a wide range of AI topics.
  • Strength 2: Hands-on learning approach with reusable artifacts.
  • Strength 3: Multi-language support for a diverse set of learners.

Limitations

  • Limitation 1: Requires a strong foundation in programming and mathematics.
  • Limitation 2: The project is still under development, with some features and lessons yet to be completed.
Source: Synthesis of README, code structure and dependencies

Latest Release

No release records available.

Source: GitHub Releases

Verdict

rohitg00/ai-engineering-from-scratch is a valuable resource for anyone serious about learning AI engineering. Its comprehensive curriculum, hands-on approach, and reusable artifacts make it a standout project for individuals and teams looking to build a strong foundation in AI and develop practical applications.

Transparency Notice
This page is auto-generated by AI (a large language model) from the following public materials: GitHub README, code tree, dependency files and release notes. Analyzed at: 2026-05-31 18:33. Quality score: 85/100.

Data sources: README, GitHub API, dependency files