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
Continue where you left off
Curious to AI-Fluent
Next up · Module 14: Reading and Exploring a Dataset in Python
94 curriculum modules3 learning paths~9 hrs of guided reading
How it works
- 1Pick a path
Choose the learning path that matches your background: ML fundamentals, AI engineering, or LLM systems.
- 2Work through modules
Each path is broken into ordered modules with deep-dive lessons, code examples, and practical exercises.
- 3Ship ML/AI systems
Graduate from theory to building real pipelines, agents, and production-grade AI applications.
Your Track
Curious to AI-Fluent
- 12-3 hours reading + reflectionRead →
What Is AI, Really? Stripping Away the Hype
The first barrier to AI literacy is not technical - it is conceptual. Most people carry a mix of science fiction imagery and tech-marketing claims. Neither is useful. This module replaces those with an accurate, honest picture of what AI systems are.
- 22-3 hoursRead →
How Machine Learning Differs from Normal Software
The fundamental difference between ML and traditional software determines almost everything about how AI systems are built, debugged, evaluated, and operated. Without this mental model, every downstream concept is harder to absorb.
- 33-4 hoursRead →
What Neural Networks Are (and Aren't)
Neural networks are the architecture behind most modern AI systems. The sci-fi framing ("artificial brains") is misleading and causes people to over-anthropomorphize AI behavior. The accurate model - a mathematical function approximator - leads to better intuitions.
- 43-4 hoursRead →
Large Language Models: Under the Hood, in Plain English
LLMs are the AI systems most people interact with daily. The "autocomplete on steroids" mental model is accurate enough to reason about failure modes, calibration issues, and appropriate use cases - without requiring any math.
Featured Categories
View allML 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.
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