rohitg00 / ai-engineering-from-scratch

2026-03-18 · Source: Github Trending: All languages · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Robotics & Autonomous Systems · Depth: Advanced, extended

Summary

AI Engineering from Scratch is an open-source, MIT-licensed curriculum comprising 435 lessons across 20 phases, designed to bridge the gap between AI tool usage and professional readiness. Requiring approximately 320 hours, it covers foundational math to autonomous systems, emphasizing building algorithms from raw math before introducing frameworks like PyTorch. The curriculum supports Python, TypeScript, Rust, and Julia, with each lesson producing a reusable artifact such as a prompt, skill, agent, or MCP server. It aims to equip learners with a deep, end-to-end understanding of AI system construction, contrasting with scattered learning approaches. The project, maintained by Rohit Ghumare, also offers built-in agent skills for personalized learning paths and progress checks.

Key takeaway

For AI Engineers or ML Students aiming for a deep, practical understanding of AI systems, this curriculum offers a structured, hands-on path. You should consider adopting its "build from scratch then use frameworks" methodology to truly grasp underlying mechanics, rather than just API calls. Leverage the provided reusable artifacts and agent skills to accelerate your learning and build a robust portfolio of functional tools, ensuring you are professionally prepared for end-to-end AI development.

Key insights

Master AI by building core algorithms from scratch before applying production frameworks.

Principles

• Derive math, then code.
• Every lesson yields a reusable artifact.
• Understand "under the hood" before frameworks.

Method

The curriculum follows a "read problem, derive math, write code, run test, keep artifact" loop, progressing from mathematical foundations to advanced agent and production topics.

In practice

• Install 378 skills and 99 prompts via SkillKit.
• Scaffold an Agent Workbench into your own repo.
• Utilize the /find-your-level agent skill for a personalized learning path.

Topics

• AI Engineering
• Machine Learning Curriculum
• Large Language Models
• Agent Systems
• Deep Learning
• Multimodal AI
• Reinforcement Learning

Code references

• rohitg00/ai-engineering-from-scratch
• rohitg00/ai-engineering-from-scratch
• rohitg00/skillkit
• sponsors/rohitg00

Best for: AI Engineer, Machine Learning Engineer, AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by Github Trending: All languages.