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Make Machine Learning work in the real world

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I wrote a small ebook about applying validation techniques to different types of real-world datasets. Going into short examples how different data types have to be treated to avoid overfitting.

I touch on the topics of:

  • overfitting
  • train-test splits
  • cross-validation
  • stratification
  • spatial validation
  • temporal validation
  • production models
  • data drift

I made it complementary with a newsletter subscription to my weekly machine learning newsletter.

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Book Cover of ML Validation e-book by Jesper Dramsch

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