MACHINE LEARNING FOR SOFTWARE ENGINEERS
This curriculum is built around shipping real ML systems with Python: data pipelines, scikit-learn, FastAPI, testing, experiment tracking, deployment, monitoring, and modern model APIs. It deliberately treats machine learning as a software engineering tool instead of a proof-heavy math course.
Python tooling, data workflows, and your first supervised model.
Pipelines, APIs, testing, and reproducible experimentation.
Deep learning APIs, system design, deployment, and observability.
Ship a prediction API, a retrieval feature, and a monitored service.
Learn the APIs the way a production engineer uses them.
Use math selectively
Only when it explains an implementation tradeoff like regularization, calibration, or vector similarity.
Prefer shippable artifacts
Every stage ends with code, a service, a test, or a repeatable workflow you can keep.
Treat ML as software
Version data, test interfaces, monitor outputs, and define rollback paths before production.
Foundations
Install the stack, move comfortably through Python data code, and build your first model without hand-waving.
Core Systems
Turn experiments into repeatable training flows, APIs, tests, and tracked runs.
Advanced Delivery
Know when to reach for PyTorch, embeddings, deployment patterns, and platform concerns.
Prediction API
Train a churn or pricing model, serialize it, wrap it in FastAPI, and write request/response tests.
Retrieval Feature
Build an embeddings-backed internal search or support classifier with evaluation samples and failure analysis.
Monitored Service
Containerize your model service, log inputs and outputs, track drift signals, and define a rollback path.
No passive reading only
Treat every module as a build. If you cannot run the example, modify it, and test it, the lesson is not complete.
Step-by-step roadmap
Dashboard
Finish the foundational setup before optimizing anything.