hemansnation/ai-engineer-headquarters

This repository is a self-study curriculum for people who want to build serious skills in data and AI engineering. The author frames it as a structured drill rather than a tutorial collection, aimed at anyone who wants to reach a high level of competence in the field, whether they are currently a student, a working professional, or in a leadership role. The learning path is divided into eight stages. It starts with foundational toolkit setup, then moves into machine learning and the practices around deploying ML systems to production, then covers large language models (the technology behind AI chat tools), retrieval-augmented generation (a technique for building AI systems that can answer questions from a specific knowledge base), fine-tuning (adapting a pre-trained AI model to a specialized domain), building autonomous AI agents, and finally career guidance. There are also bonus masterclass sections. The README describes a daily work routine the author recommends: four hours of distraction-free deep focus plus two hours of lighter work that includes sharing progress publicly. It emphasizes that the learning requires consistent effort and that there are no shortcuts. Content is available in two forms: video sessions and written text notes in the repository. The author suggests watching the videos as the primary learning path and using the written materials as reference notes. The repository is sparse in the README itself, with most of the detailed content presumably inside the Jupyter Notebooks and folders contained in the project rather than described in the top-level document.