Make Machine Learning Work in the Real World e-Book

I wrote a small ebook about applying validation techniques to different types of real-world datasets. Going into short examples of 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

This is a mini e-book as a reference guide for those that need quick insight to get an overview of the different pitfalls in real-worl machine learning.