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AI Foundations
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Understand AI, machine learning, deep learning, LLMs, RAG, agents, and AI product thinking in plain English. (Beginner)
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Start with plain-English concepts, then move toward Python, Math, data, machine learning, LLMs, RAG, agents, and production systems.
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Stage 00
Not started
Understand AI, machine learning, deep learning, LLMs, RAG, agents, and AI product thinking in plain English. (Beginner)
Stage 01
Not started
Learn the Python basics you need for AI tooling, data handling, and small production APIs. (Beginner)
Stage 02
Not started
Enter the Math Forge and learn the practical, visual math AI uses to measure, compare, and improve. (Beginner)
Stage 03
Not started
Learn how to prepare and validate data so AI systems receive reliable inputs. (Beginner)
Stage 04
Not started
Build baseline models by understanding features, training loops, and evaluation. (Beginner)
Stage 05
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Learn neural networks through practical architecture and training intuition. (Intermediate)
Stage 06
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Build reliable language-model applications with prompts, structured output, and tools. (Intermediate)
Stage 07
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Build retrieval-augmented systems that ground model outputs with evidence. (Intermediate)
Stage 08
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Build AI systems that plan, use tools, and execute safely through controlled workflows. (Advanced)
Stage 09
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Customize model behavior with data and training strategies before deploying at scale. (Advanced)
Stage 10
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Ship reliable AI products with architecture, monitoring, and safe operations. (Advanced)
Curated roadmap
AI Foundry organizes and contextualizes public AI learning resources into one practical project-based roadmap. We summarize and connect, while keeping external content linked to its original source.
Curated Resources
Harvard / CS50
Why this matters: Build a sturdy Python foundation before moving into AI engineering practices.
Use this: Primary foundation
When to use: Start here before entering AI-heavy modules.
Last checked: Jun 20, 2026
DeepLearning.AI
Why this matters: Shows practical AI workflows built directly on Python fundamentals.
Use this: Bridge resource
When to use: Use after Python basics before Math and Data modules.
Last checked: Jun 20, 2026
3Blue1Brown
Why this matters: Helps connect geometric concepts to AI learning and representation.
Use this: Visual intuition track
When to use: Use when model intuition feels too abstract.
Last checked: Jun 20, 2026
Andrej Karpathy
Why this matters: A practical sequence for building intuition and implementations from first principles.
Use this: Advanced builder path
When to use: Use after Deep Learning for hands-on implementation depth.
Last checked: Jun 20, 2026
Anthropic
Why this matters: Defines composable patterns for safe, effective autonomous workflows.
Use this: AI Agents guidance
When to use: Use during AI Agents module planning and design.
Last checked: Jun 20, 2026
patchy631
Why this matters: A project library for practical AI engineering example ideas.
Use this: Portfolio exploration
When to use: Use after module projects to compare practical implementation styles.
Last checked: Jun 20, 2026
Chip Huyen
Why this matters: A practical long-form guide for shipping reliable AI systems.
Use this: Long-form production reference
When to use: Use through late-stage production and deployment modules.
Last checked: Jun 20, 2026
Hugging Face
Why this matters: Provides practical building blocks for modern LLM tooling and workflows.
Use this: Applied LLM path
When to use: Use alongside LLM Engineering for practical ecosystem exposure.
Last checked: Jun 20, 2026
Curated Resources Disclaimer
AI Foundry curates public learning resources from respected educators and organizations. All external content belongs to its original creators. AI Foundry is not affiliated with or endorsed by those organizations unless explicitly stated.
Region / Stage 00
Understand AI, machine learning, deep learning, LLMs, RAG, agents, and AI product thinking in plain English.
Region / Stage 01
Learn the Python basics you need for AI tooling, data handling, and small production APIs.
Region / Stage 02
Enter the Math Forge and learn the practical, visual math AI uses to measure, compare, and improve.
Region / Stage 03
Learn how to prepare and validate data so AI systems receive reliable inputs.
Region / Stage 04
Build baseline models by understanding features, training loops, and evaluation.
Region / Stage 05
Learn neural networks through practical architecture and training intuition.
Region / Stage 06
Build reliable language-model applications with prompts, structured output, and tools.
Region / Stage 07
Build retrieval-augmented systems that ground model outputs with evidence.
Region / Stage 08
Build AI systems that plan, use tools, and execute safely through controlled workflows.
Region / Stage 09
Customize model behavior with data and training strategies before deploying at scale.
Region / Stage 10
Ship reliable AI products with architecture, monitoring, and safe operations.