Guides · Learn in order
Build understanding one step at a time. Each path connects a sequence of practical guides to a capstone project, with prerequisites and outcomes to help you choose where to start.
Browse individual guides →6 lessons + capstone · Self-paced
Learn model APIs, embeddings, retrieval, RAG, evaluation and reliability through six focused lessons and a reproducible document-assistant capstone.
People who can write basic Python functions, read files and use a terminal, and want to build applications around existing AI models. This is an introductory applied-engineering path, not a full machine-learning training curriculum.
Explore the path →6 lessons + capstone · Self-paced
Build the classical-ML engineering core through six focused lessons and a reproducible classifier capstone: data contracts, splits, pipelines, evaluation, local serving and monitoring.
People comfortable with basic Python functions, modules, files and command-line use who want a practical introduction to training and operating machine learning models.
Explore the path →Read at your own pace, practice each concept, and use the capstone to bring the pieces together. Reading times describe the articles; allow additional time for exercises and project work.