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Interpretable Machine Learning Applications: Part 4 に戻る

Coursera Project Network による Interpretable Machine Learning Applications: Part 4 の受講者のレビューおよびフィードバック

コースについて

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. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions....
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Interpretable Machine Learning Applications: Part 4: 1 - 1 / 1 レビュー

by Pascal U E

2021年7月3日

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