ML-For-Beginners

Integrate with external ML model serving platforms by exporting models, using deployment wrappers, and calling managed endpoints. Learn how with ML-For-Beginners.

Understand the ML-For-Beginners project folder hierarchy. Explore how docs notebooks labs solutions and resources create a clear learning path from theory to practice in this Microsoft repo.

Learn to use the AGENTS.md automation file in ML-For-Beginners. Effortlessly configure agents for knowledge-base generation and automate content extraction processing and publishing.

Understand the ML-For-Beginners lesson navigation flow. Explore the dual system combining a global table of contents and linear next lesson badges for guided learning.

Easily swap between ML frameworks like TensorFlow PyTorch and scikit-learn in the ML-For-Beginners curriculum. Learn how with framework-specific notebooks.

Explore the quiz data JSON structure in ML-For-Beginners. Understand how each file organizes module titles and question objects for easy learning.

Learn how to contribute pull requests to the Microsoft ML-For-Beginners repository. Follow our guide to fork, branch, set up, edit, and submit your contributions easily.

Learn how to configure Docsify for offline documentation viewing with the ML-For-Beginners guide. Enable caching with simple config changes.

Resolve Python package dependency conflicts in ML-For-Beginners. Use virtual environments and lock files like requirements.txt or environment.yml to prevent version issues and ensure smooth setup.

Master pickle model serialization in Flask apps. Train offline, load at startup, and serve predictions securely. Optimize your production ML deployment.

Learn to modify ML-For-Beginners curriculum lesson order and groupings by editing the curriculum.yml file. Customize modules and lesson sequences easily.

Expand the ML-For-Beginners curriculum! Learn how to add new languages by registering translations, creating dictionaries, and running the translate script.