Interpretable Machine Learning Applications: Part 4
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.
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- Level Intermediate
- Duration 3 hours
- Course by Coursera Project Network
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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.Modules
Practical Application via Rhyme
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- Practice Quiz: Test your project understanding
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Labs
- Interpretable Machine Learning Applications: Part 4
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- Project-based Course Overview
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Discover how to leverage the "What-If" Tool in machine learning with this 1-hour guided project focused on training and testing prediction models. Ideal for intermediate learners in Data Science & AI, this Coursera course, led by expert instructors, utilizes Python in Google's Colab environment. You'll import and prepare data, train classifiers, and analyze model behaviors both individually and globally. Enjoy free access to this comprehensive and practical learning experience.
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