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My personal collection on how to approach ML projects, a compilation of questions to ask, steps to take, factors to consider - derived from books, online courses and tutorials and my own ideas. A list of books is included!

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ML-Project-Guide

This document is dedicated to centralizing, organizing and documenting ideas that particularly stuck with me - from various great books, online courses and tutorials, some of which are:

  • Becoming a Data Head by Gutman/Goldmeier
  • Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurelien Géron
  • Einführung in Data Science by Grus (O'Reilly)

This guide and the sources will be continously updated.

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My personal collection on how to approach ML projects, a compilation of questions to ask, steps to take, factors to consider - derived from books, online courses and tutorials and my own ideas. A list of books is included!

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