[D] Looking for good refreshers on stats / ML to go back to the ML engineer interview game after 2 years doing mostly Software.

This page summarizes the projects mentioned and recommended in the original post on reddit.com/r/MachineLearning

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  • MLflow

    Open source platform for the machine learning lifecycle

    Any CI/CD experience you had working as a conventional SDE should translate well to "MLOps". Here are some resources to help you review what kinds of considerations might be important for productionizing ML projects: https://mlflow.org/, https://docs.microsoft.com/en-us/azure/machine-learning/concept-model-management-and-deployment

  • handson-ml2

    A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

    Much of the material from that book is publicly available in this repo maintained by the author.

  • SonarQube

    Static code analysis for 29 languages.. Your projects are multi-language. So is SonarQube analysis. Find Bugs, Vulnerabilities, Security Hotspots, and Code Smells so you can release quality code every time. Get started analyzing your projects today for free.

  • tests-as-linear

    Common statistical tests are linear models (or: how to teach stats)

    Don't know about any cheat sheet but perhaps you'd find this pretty stimulating to read: https://lindeloev.github.io/tests-as-linear/

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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