rohitg00

ai-engineering-from-scratch

AIMachine Learning Deep LearningLLMsPythonTransformers

+340

// summary

This comprehensive course provides over 260 lessons across 20 phases to help developers master AI engineering from foundational mathematics to advanced autonomous agent swarms. It utilizes an AI-native learning approach where students work alongside coding agents to build, test, and deploy reusable tools. The curriculum spans multiple programming languages including Python, TypeScript, Rust, and Julia to ensure practical, production-ready skills.

// technical analysis

This project offers a comprehensive, AI-native curriculum designed to bridge the gap between theoretical knowledge and professional AI engineering. By emphasizing a 'build-from-scratch' philosophy, it forces students to implement core algorithms and frameworks before utilizing high-level libraries, ensuring a deep understanding of the underlying mechanics. The curriculum is uniquely structured to be used alongside AI coding agents, providing a hands-on, iterative learning experience that results in a portfolio of reusable tools, prompts, and agents rather than just passive knowledge.

// key highlights

Provides a massive, structured curriculum of over 260 lessons across 20 phases, covering everything from linear algebra to autonomous agent swarms.

Features an AI-native learning approach that integrates directly with coding agents like Claude Code for real-time testing and skill verification.

Ensures every lesson results in a tangible, reusable artifact such as custom prompts, specialized skills, or deployable MCP servers.

Supports a multi-language development environment including Python, TypeScript, Rust, and Julia to reflect real-world engineering diversity.

Includes built-in diagnostic tools like /find-your-level and /check-understanding to personalize the learning path and validate progress.

Focuses on production-grade engineering by teaching essential skills like GPU setup, Docker, distributed training, and inference optimization.

// use cases

Building a professional portfolio of reusable AI tools, prompts, and agents

Mastering end-to-end AI development from math foundations to LLM deployment

Learning to integrate AI-native workflows using coding agents like Claude Code

// getting started

To begin, visit the project website to browse the full lesson catalog and explore the roadmap. You can start by running the /find-your-level command within your AI coding agent to receive a personalized learning path based on your current expertise. From there, you can navigate to the specific phase folders to access the code, documentation, and build instructions for each lesson.