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Back to Interpretable Machine Learning Applications: Part 4

Learner Reviews & Feedback for Interpretable Machine Learning Applications: Part 4 by Coursera Project Network

4.7
stars
12 ratings

About the Course

In this 1-hour long guided project, you will learn how to use the "What-If" Tool (WIT) in the context of training and testing machine learning prediction models. In particular, you will learn a) how to set up a machine learning application in Python by using interactive Python notebook(s) on Google's Colab(oratory) environment, a.k.a. "zero configuration" environment, b) import and prepare the data, c) train and test classifiers as prediction models, d) analyze the behavior of the trained prediction models by using WIT for specific data points (individual basis), e) moving on to the analysis of the behavior of the trained prediction models by using WIT global basis, i.e., all test data considered....
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1 - 3 of 3 Reviews for Interpretable Machine Learning Applications: Part 4

By Smally .

•

Jan 21, 2025

nice

By Srinivas G

•

Dec 15, 2024

Good content

By Pascal U E

•

Jul 3, 2021

It seems like there is a lot more to do about what-if and It would be good to have some in the project