Interpretable Machine Learning Applications: Part 1
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.
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- Level Beginner
- Duration 3 hours
- Course by Coursera Project Network
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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.Modules
Interpretable Machine Learning Applications: Part 1
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Readings
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Assignment
- Test your knowledge!
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Labs
- Interpretable machine learning applications: Part 1
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Readings
- Interpretable machine learning applications: Part 1
Auto Summary
Explore the essentials of interpretable machine learning with this engaging 1-hour project-based course. Learn to create and explain classification regression models like decision trees and random forest classifiers, focusing on key features influencing predictions. Ideal for ML developers, modelers, executives, and consultants, this beginner-level course by Coursera offers valuable insights into deploying trusted and accountable ML applications. Available for free, it's a perfect opportunity to enhance your data science and AI skills.
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