wehub-resource-sync
214 lines
6.3 KiB
Markdown
214 lines
6.3 KiB
Markdown
# 🚀 AI Engineering Roadmap
|
|
|
|
A comprehensive guide to becoming an AI Engineer, starting from Python fundamentals to building production-ready AI applications.
|
|
|
|
|
|
|
|
---
|
|
|
|
## 📚 Table of Contents
|
|
|
|
- [1️⃣ Master Python](#1️⃣-master-python)
|
|
- [2️⃣ AI with Python](#2️⃣-ai-with-python)
|
|
- [3️⃣ Maths for ML](#3️⃣-maths-for-ml)
|
|
- [4️⃣ Understanding LLMs](#4️⃣-understanding-llms)
|
|
- [5️⃣ LLM Research](#5️⃣-llm-research)
|
|
- [6️⃣ AI Agents](#6️⃣-ai-agents)
|
|
- [7️⃣ Applied AI](#7️⃣-applied-ai)
|
|
- [8️⃣ AI Protocols (MCP)](#8️⃣-ai-protocols-mcp)
|
|
- [9️⃣ Project-based Learning](#9️⃣-project-based-learning)
|
|
- [🔟 Books](#🔟-books)
|
|
|
|
---
|
|
|
|
## 1️⃣ Master Python
|
|
|
|
**Strong coding fundamentals are important.**
|
|
|
|
Start with Python, and Harvard's CS50p is the best place to learn it.
|
|
|
|
|
|
|
|
**🔗 [Harvard CS50's Introduction to Programming with Python](https://pll.harvard.edu/course/cs50s-introduction-programming-python)**
|
|
|
|
- **Duration:** 9 weeks
|
|
- **Time Commitment:** 3-9 hours per week
|
|
- **Difficulty:** Introductory
|
|
- **Platform:** edX
|
|
|
|
---
|
|
|
|
## 2️⃣ AI with Python
|
|
|
|
**Next, learn how Python is used in AI.**
|
|
|
|
This 4-hour course by Andrew Ng is a great starting point.
|
|
|
|
|
|
|
|
**🔗 [AI Python for Beginners - DeepLearning.AI](https://deeplearning.ai/short-courses/ai-python-for-beginners/)**
|
|
|
|
- **Duration:** 4 hours 15 minutes
|
|
- **Instructor:** Andrew Ng
|
|
- **Lessons:** 35 video lessons
|
|
- **Code Examples:** 27 code examples
|
|
|
|
---
|
|
|
|
## 3️⃣ Maths for ML
|
|
|
|
**Fundamentals of Linear Algebra, Probability, and Statistics are important, especially in AI research.**
|
|
|
|
These playlists by Khan Academy are the perfect place to learn it:
|
|
|
|
|
|
|
|
**🔗 Essential Math Playlists:**
|
|
- [Linear Algebra](https://www.youtube.com/playlist?list=PLFD0EB975BA0CC1E0)
|
|
- [Probability](https://www.youtube.com/playlist?list=PLC58778F28211FA19)
|
|
- [Statistics](https://www.youtube.com/playlist?list=PL1328115D3D8A2566)
|
|
|
|
---
|
|
|
|
## 4️⃣ Understanding LLMs
|
|
|
|
**These three videos by 3Blue1Brown are the best visual explainers of LLMs and their internal workings.**
|
|
|
|
|
|
|
|
**🔗 [Neural Networks Playlist - 3Blue1Brown](https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi)**
|
|
|
|
**Key Topics:**
|
|
- How LLMs work
|
|
- Transformers Deep-dive
|
|
- Attention in transformers
|
|
- How LLMs store facts
|
|
|
|
---
|
|
|
|
## 5️⃣ LLM Research
|
|
|
|
**Now that you understand what LLMs are, it's time to learn how to build them yourself.**
|
|
|
|
Neural Nets zero-to-hero by Andrej Karpathy is the greatest series to do so.
|
|
|
|
|
|
|
|
**🔗 [Neural Networks: Zero to Hero - Andrej Karpathy](https://youtube.com/playlist?list=PLAqhIrjkxbuWI23v9cThsA9GvCAUhRvKZ)**
|
|
|
|
- **Videos:** 10 videos
|
|
- **Total Views:** 2M+ views
|
|
- **Focus:** Building neural networks from scratch
|
|
|
|
---
|
|
|
|
## 6️⃣ AI Agents
|
|
|
|
**Before even jumping into the Agents, you should first read Anthropic AI's guide on building effective agents.**
|
|
|
|
> *"To build an agent, you don't need complex frameworks or libraries, but rather composable patterns."*
|
|
|
|
|
|
|
|
**🔗 [Building Effective Agents - Anthropic](https://anthropic.com/engineering/building-effective-agents)**
|
|
|
|
- **Published:** December 19, 2024
|
|
- **Focus:** Simple, composable patterns for LLM agents
|
|
- **Industry Insights:** Real-world implementation patterns
|
|
|
|
---
|
|
|
|
## 7️⃣ Applied AI
|
|
|
|
**I don't recommend chasing frameworks, but I took this course on CrewAI when I started.**
|
|
|
|
João Moura precisely teaches how to think of agents like humans working together in a clear and practical manner.
|
|
|
|
|
|
|
|
**🔗 [Multi AI Agent Systems with CrewAI - Coursera](https://coursera.org/projects/multi-ai-agent-systems-with-crewai)**
|
|
|
|
- **Duration:** 2 hours 41 minutes
|
|
- **Instructor:** João Moura
|
|
- **Lessons:** 18 video lessons
|
|
- **Code Examples:** 7 code examples
|
|
|
|
---
|
|
|
|
## 8️⃣ AI Protocols (MCP)
|
|
|
|
**Now that you understand what agents are, it's time to connect them to external tools, APIs, and databases.**
|
|
|
|
This free hands-on guide on MCP has 10+ projects.
|
|
|
|
|
|
|
|
**🔗 [MCP: The Illustrated Guidebook](https://mcp.dailydoseofds.com)**
|
|
|
|
- **Edition:** 2025 Edition
|
|
- **Status:** FREE
|
|
- **Projects:** 10+ hands-on projects
|
|
- **Focus:** Model Context Protocol implementation
|
|
|
|
---
|
|
|
|
## 9️⃣ Project-based Learning
|
|
|
|
**This GitHub repo contains 75+ projects on AI Engineering covering:**
|
|
|
|
- LLMs and RAGs
|
|
- Real-world AI agent applications
|
|
- Examples to implement, adapt, and scale in your projects
|
|
|
|
|
|
|
|
**🔗 [AI Engineering Hub - GitHub](https://github.com/patchy631/ai-engineering-hub)**
|
|
|
|
**What you'll find:**
|
|
- In-depth tutorials on LLMs and RAGs
|
|
- Real-world AI agent applications
|
|
- Examples to implement, adapt, and scale in your projects
|
|
- Resources for all skill levels
|
|
|
|
---
|
|
|
|
## 🔟 Books
|
|
|
|
**Every AI engineer building real-world applications should read this book.**
|
|
|
|
Chip Huyen's book is one of the best on AI Engineering.
|
|
|
|
|
|
|
|
**🔗 [AI Engineering Book - GitHub](https://github.com/chiphuyen/aie-book)**
|
|
|
|
**What you'll learn:**
|
|
- Understand what AI engineering is and how it differs from traditional ML engineering
|
|
- Learn the process for developing an AI application
|
|
- Explore various model adaptation techniques
|
|
- Examine bottlenecks for latency and cost when serving foundation models
|
|
- Choose the right model, metrics, data, and developmental patterns
|
|
|
|
---
|
|
|
|
## 🎯 Learning Path Summary
|
|
|
|
1. **Foundation** → Master Python programming
|
|
2. **AI Basics** → Learn Python for AI applications
|
|
3. **Mathematics** → Build strong math fundamentals
|
|
4. **Understanding** → Grasp how LLMs work internally
|
|
5. **Research** → Learn to build neural networks from scratch
|
|
6. **Agents** → Understand effective agent design patterns
|
|
7. **Application** → Build multi-agent systems
|
|
8. **Integration** → Connect agents to external tools and APIs
|
|
9. **Practice** → Work on real-world projects
|
|
10. **Mastery** → Deep dive into production AI engineering
|
|
|
|
---
|
|
|
|
## 🤝 Contributing
|
|
|
|
Found this roadmap helpful? Star ⭐ this repository and share it with others!
|
|
|
|
*Happy Learning! 🚀*
|