A Course on Machine Learning for Software Developers
summary
Course material built with four industry partners (Facebook, Fractal, Fidelity, IBM) teaches beginner developers end-to-end ML workflows with working Colab/Jupyter notebooks, a model zoo (ONNX-style) and deployed servers at UW‑Platteville. Examples include logistic regression and SVM implemented in scikit-learn, PyTorch, TensorFlow, showing that data wrangling (scaling, split, feature prep) dominates code while training can be one–two lines. Students get Power-tuned VMs and containers plus CI/CD guidance; demos and VM deployments are functional.
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chapters (9)
- 0:00Introduction to AI education initiative
- 1:45Current state of machine learning
- 4:57Learning outcomes for developers
- 11:38Institute’s collaborative efforts
- 13:46Course highlights and audience
- 17:59Coding demo and workflow
- 27:11Future goals and model zoo
- 29:38Conclusion and collaboration
- 30:45Q&A session
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