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In this 1-hour long project-based course, you will learn how to create interpretable machine learning applications on the example of two classification regression models, decision tree and random forestc classifiers. You will also learn how to explain such prediction models by extracting the most important features and their values, which mostly impact these prediction models. In this sense, the project will boost your career as Machine Learning (ML) developer and modeler in that you will be able to get a deeper insight into the behaviour of your ML model. The project will also benefit your career as a decision maker in an executive position, or consultant, interested in deploying trusted and accountable ML applications.
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1 - 2 von 2 Bewertungen für Interpretable Machine Learning Applications: Part 1
von Pascal U E
•1. Juli 2021
I was looking for this content for very long time, I will finish all the series. Keep doing great guided projects.
von Venkataramana M
•7. Aug. 2022
Pretty Informative and crisp to the point. Great hands on course.
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