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Imbalanced-learn: modelos de ML con datos desequilibrados
  • Level Foundation
  • المدة 3 ساعات hours
  • Offered by

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Este proyecto es un curso práctico y efectivo para aprender que es el desbalanceo de clases en Machine leraning y como tratarlo. Aprenderemos las técnicas más avanzadas para trabajar con datos desbalanceados como: bSMOTE, ADASYN, SMOTEEN, etc. También aprenderemos a generar modelos capaces de trabajar con datos desbalanceados. Una gran parte de los problemas de clasificación utilizan datos debalanceadas. Si no se tratan estos casos estaremos generando modelos que no estén funcionando correctamente, pese a que a priori parezca que si. Por eso, en este curso aprenderemos a como tratar este tipo de datos.

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Dive into the world of Machine Learning with a specialized focus on handling imbalanced data through the "Imbalanced-learn: Modelos de ML con Datos Desequilibrados" course. Curated by Coursera, this beginner-friendly course offers a practical and effective approach to understanding and addressing class imbalance in Machine Learning models. Throughout this 180-minute course, learners will gain valuable insights into the challenges presented by imbalanced datasets and explore various techniques to manage and mitigate these issues effectively. With a free subscription option, this course is perfect for those starting their journey in IT and Computer Science, providing them with essential skills to enhance their Machine Learning projects and achieve more accurate and reliable results. Join now and master the art of balancing your data for better machine learning models.