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Machine Learning Interpretable: SHAP, PDP y permutacion
  • Level Intermediate
  • Duration 2 hours
  • Offered by

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Este proyecto es un curso práctico y efectivo para aprender a generar modelos de Machine Learning interpretables. Se explican en profundidad diferentes técnicas de interpretabilidad de modelos como: SHAP, Partial Dependence Plot, Permutation importance, etc que nos permitirá entender el porqué de las predicciones. Gracias a esto, aprenderás a entrenar modelos Glassbox que puedas entender el porqué de sus decisiones.

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Enhance your Data Science & AI skills with this practical course on interpretable Machine Learning models. Led by Coursera, you'll dive into SHAP, PDP, and permutation techniques over 120 minutes. Ideal for intermediate learners, and available for free. Perfect for those looking to make their ML models more transparent and understandable.