AI Expert Roadmap

Structured as an interactive diagram, it covers foundational areas including mathematics (linear algebra, statistics, calculus), classical machine learning algorithms, deep learning frameworks, natural language processing, computer vision, reinforcement learning, and MLOps practices for production deployment.

The roadmap is technology-opinionated where consensus exists recommending Python as the primary language, NumPy/pandas for data manipulation, scikit-learn for classical ML, and PyTorch/TensorFlow for deep learning while acknowledging multiple valid paths for specialization.

Each node in the diagram links to curated learning resources: textbooks, online courses, research papers, and hands-on projects. The visual format makes it easy to identify skill gaps and plan a structured learning trajectory rather than consuming resources randomly.

The project is open-source on GitHub and accepts community contributions to keep resources current as the AI field evolves.

It is particularly useful for software engineers transitioning into ML roles, university students planning their coursework, and self-taught practitioners who want to validate the completeness of their knowledge.

The roadmap has been translated into multiple languages and adapted into specialized versions for data science, MLOps, and applied AI engineering.