solegalli/machine-learning-interpretability
Code repository for the online course Machine Learning Interpretability
When you need to understand why your machine learning models make certain predictions, this resource provides the techniques. It takes your trained models and helps you uncover which features drive their decisions, and to what extent. This is for data scientists, machine learning engineers, and analysts who need to explain complex model behaviors to stakeholders or for regulatory compliance.
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Use this if you need to explain how a machine learning model arrives at its predictions, both for individual cases and across its entire behavior.
Not ideal if you are looking for a pre-built, production-ready monitoring system for model drift or ethical AI, rather than foundational interpretability techniques.
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Jupyter Notebook
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Last pushed
Oct 12, 2024
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