AI Engineer Roadmap – How to Learn AI in 2025

The video serves as an excellent high-level overview of the AI engineering field, effectively structuring the vast landscape of required knowledge and skills. The presenter, Tatev Aslanyan, demonstrates strong pedagogical skills, breaking down complex topics into digestible segments and providing a clear progression from fundamentals to advanced concepts. The roadmap is well-organized, covering mathematics, statistics, data science, machine learning, deep learning, and large language models, which accurately reflects the core competencies of an AI engineer. The emphasis on practical implementation and real-world applications is commendable, as it grounds the theoretical concepts in tangible outcomes. However, the video’s primary limitation is its breadth over depth. Each topic is covered at a surface level, providing a roadmap rather than in-depth instruction. This is appropriate for an introductory overview but may leave viewers wanting more detailed explanations. The presenter’s promotion of her own bootcamp introduces a potential conflict of interest, as she may be incentivized to present the field as requiring extensive training. Nevertheless, the content itself is accurate and aligns with industry standards. The video does not cite specific academic sources, but it references well-known concepts and tools. The adéquation between title and content is strong, as the video indeed provides a roadmap for learning AI in 2025. Overall, the video is a valuable resource for individuals seeking a structured path into AI engineering, offering a solid foundation and clear direction. However, viewers should supplement it with more detailed courses and hands-on projects to gain true proficiency.

The title accurately reflects the content: a roadmap for learning AI engineering in 2025.

8/10

The video provides a structured, comprehensive roadmap for AI engineering, covering mathematical foundations, machine learning, deep learning, and LLMs. The content is well-organized and presented by an experienced educator. However, it is a high-level overview without deep technical depth, and the presenter promotes her own bootcamp, which introduces a potential bias.

Markers derived by PSI from the transcript: the creator did not define chapters.

• 00:00Introduction to the AI Engineering Roadmap
• 03:59Definition of AI Engineering and its role in the tech ecosystem
• 05:47Real-world applications of AI Engineering across industries
• 08:29Must-have skills for an AI Engineer
• 09:05Mathematical foundations: high school math and linear algebra
• 17:38Statistics essentials for AI
• 23:37Data science skills required
• 28:05Traditional machine learning algorithms
• 32:36Deep learning foundations
• 38:36Practical implementation in Python
• 40:54Generative AI fundamentals
• 46:39Large Language Models (LLMs) and their importance
• 51:07Fine-tuning LLMs
• 52:05Reinforcement Learning with Human Feedback (RLHF)
• 53:05Retrieval-Augmented Generation (RAG)
• 53:28Evaluating and optimizing LLMs
• 54:14AI Engineering ethics and safety
• 56:44Additional resources and career paths

• freeCodeCamp News — Referenced as a resource for learning to code and reading articles on programming.
• Scrimba AI Courses — Mentioned as interactive AI courses to try in the browser.
• freeCodeCamp — Main platform for learning to code for free.
• LunarTech AI Engineering Bootcamp — Promoted as a resource for applying to an AI engineering bootcamp.

• freeCodeCamp — The channel and platform are known for providing high-quality educational content, and this video aligns with that reputation.