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ML/AI Engineering Curriculum

Learn ML/AI engineering. Build things that actually work.

brewYourAgent is a structured learning platform for anyone going deep on ML and AI. Follow a curated path matched to your background, or explore 190+ technical lessons on any topic, from fundamentals to production-grade systems.

  • Structured paths so you always know what to learn next
  • Real technical depth: code, systems, and working examples
  • Covers RAG, agents, fine-tuning, MLOps, and more
Pick your pathBrowse the library

Which describes you?

No programming background

Start with AI concepts, tool judgment, gentle Python, and a first low-code AI project.

Open your roadmap →Student or early programmer

Build ML foundations, applied AI projects, MLOps habits, and a portfolio you can explain.

Open your roadmap →Experienced software engineer

Use your software engineering background to learn ML systems, LLM apps, evaluations, and AI system design.

Open your roadmap →

Pick a path, read modules in order, and use the library when a topic needs a slower pass.

94 curriculum modules3 learning paths~9 hrs of guided reading

How it works

  1. 1
    Pick a path

    Choose the learning path that matches your background: ML fundamentals, AI engineering, or LLM systems.

  2. 2
    Work through modules

    Each path is broken into ordered modules with deep-dive lessons, code examples, and practical exercises.

  3. 3
    Ship ML/AI systems

    Graduate from theory to building real pipelines, agents, and production-grade AI applications.

Recommended Track

Software Engineer to ML/AI Engineer

34 modules
  1. 1
    3-4 hrs

    ML for Software Engineers: Mental Model Reset

    Transitioning engineers often treat ML like deterministic backend logic. This module corrects that early.

    Read →
  2. 2
    4-5 hrs

    Python for Experienced Engineers

    Experienced engineers should move quickly, but not pick up weak Python habits while doing it.

    Read →
  3. 3
    4-6 hrs

    Agentic Coding: Working With Claude Code, Codex, and Cursor

    Coding agents are now the default way software is written, and every later module goes faster if you can delegate mechanical work to one and review it well. Placed in Phase 1 so the skill compounds across the path.

    Read →
  4. 4
    6-8 hrs

    Math and Statistics for Practical ML Judgment

    This gives enough linear algebra, probability, and optimization intuition to hold technical interviews and design discussions.

    Read →
View full path →

Featured Categories

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ML Foundations

Lessons and examples for getting more comfortable with ml foundations.

AI Engineering

Lessons and examples for getting more comfortable with ai engineering.

MLOps

Lessons and examples for getting more comfortable with mlops.

AI Literacy

Lessons and examples for getting more comfortable with ai literacy.