Chapters (19)
- 0:00AI Engineering Roadmap Introduction
- 3:59What is AI Engineering
- 5:47AI Engineering Applications
- 8:31Must-Have Skills for an AI Engineer
- 9:05Mathematical Foundations
- 17:38Statistics Essentials
- 23:37Data Science Skills
- 28:05Traditional Machine Learning
- 32:36Deep Learning Foundations
- 38:36Practical Implementation in Python
- 40:54Generative AI Fundamentals
- 46:39Large Language Models (LLMs)
- 51:07Fine-tuning Large Language Models
- 52:05Reinforcement Learning with Human Feedback (RLHF)
- 53:05Retrieval-Augmented Generation (RAG)
- 53:28Evaluating & Optimizing LLMs
- 54:14AI Engineering Ethics & Safety
- 56:44Additional Resources & Career Paths
- 57:34Outro & Final Remarks
Show the creator's full description
This comprehensive AI Engineering roadmap will walk you through the essential skills and techniques every aspiring AI Engineer should master by 2025. From fundamental mathematics and key machine learning algorithms to deep learning, AI Engineering best practices, and large language models—you’ll get hands-on AI Engineering experience and invaluable career insights that will set you on the fast track to success in this rapidly evolving field.
Course developed by Tatev Aslanyan from @LunarTech_ai
Apply to AI Engineering Bootcamp Here: https://www.lunartech.ai/bootcamp/ai-engineering-bootcamp
❤️ Try interactive AI courses we love, right in your browser: https://scrimba.com/freeCodeCamp-AI (Made possible by a grant from our friends at Scrimba)
⭐️ Contents ⭐️
(0:00:00) AI Engineering Roadmap Introduction
(0:03:59) What is AI Engineering
(0:05:47) AI Engineering Applications
(0:08:31) Must-Have Skills for an AI Engineer
(0:09:05) Mathematical Foundations
(0:17:38) Statistics Essentials
(0:23:37) Data Science Skills
(0:28:05) Traditional Machine Learning
(0:32:36) Deep Learning Foundations
(0:38:36) Practical Implementation in Python
(0:40:54) Generative AI Fundamentals
(0:46:39) Large Language Models (LLMs)
(0:51:07) Fine-tuning Large Language Models
(0:52:05) Reinforcement Learning with Human Feedback (RLHF)
(0:53:05) Retrieval-Augmented Generation (RAG)
(0:53:28) Evaluating & Optimizing LLMs
(0:54:14) AI Engineering Ethics & Safety
(0:56:44) Additional Resources & Career Paths
(0:57:34) Outro & Final Remarks
🎉 Thanks to our Champion and Sponsor supporters:
👾 Drake Milly
👾 Ulises Moralez
👾 Goddard Tan
👾 David MG
👾 Matthew Springman
👾 Claudio
👾 Oscar R.
👾 jedi-or-sith
👾 Nattira Maneerat
👾 Justin Hual
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