equinor/ml-pitfalls
Material for a short course on pitfalls in machine learning
This project provides practical guidance and examples for anyone building machine learning models. It helps you identify, understand, and avoid common issues that can lead to unreliable or unsafe model predictions. You'll gain insights into preventing problems from poor data to improper evaluation, ensuring your models are robust and trustworthy.
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Use this if you are developing or managing machine learning projects and want to ensure the models you create are accurate, reliable, and free from common, often hidden, errors.
Not ideal if you are looking for an introduction to the very basics of machine learning algorithms or a highly technical deep dive into specific model architectures.
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Jupyter Notebook
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CC-BY-4.0
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Last pushed
Aug 18, 2025
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