10 Machine Learning Applications + (Real-World Examples)
February 6, 2025
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Instructor: Epaminondas Kapetanios
4,174 already enrolled
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(45 reviews)
Recommended experience
Beginner level
Molnar, C.: Interpretable Machine Learning: A Guide for Making Black Box Models Explainable, https://christophm.github.io/interpretable-ml-book/
(45 reviews)
Recommended experience
Beginner level
Molnar, C.: Interpretable Machine Learning: A Guide for Making Black Box Models Explainable, https://christophm.github.io/interpretable-ml-book/
How to select and compare different prediction models (classification regressors) for a real world dataset (FIFA 2018 Soccer World Cup Statistics).
How to extract the most important features, which impact the classifiers, in a model-agnostic approach, together with caveats.
How to get an insight into the way values of the most important features impact the predictions made by the classifiers.
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Only available on desktop
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.
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Setting the stage (Python Jupyter Lab web-based Server environment, importing the dataset and file to train and test the designated classification regressors as prediction models).
Train, test and estimate the accuracy (confusion matrix) of a Decision Tree classifier.
Train, test and estimate the accuracy (confusion matrix) of a Random Tree classifier as an alternative to the previous one.
Extract a ranking list of the features, which are most important for each one of our prediction models.
Extract and plot the impact of the values of selected important features on predictions being made by each one of our prediction models.
Molnar, C.: Interpretable Machine Learning: A Guide for Making Black Box Models Explainable, https://christophm.github.io/interpretable-ml-book/
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Practice new skills by completing job-related tasks.
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Follow along with pre-recorded videos from experts using a unique side-by-side interface.
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Access the tools and resources you need in a pre-configured cloud workspace.
Available only on desktop
This Guided Project is designed for laptops or desktop computers with a reliable Internet connection, not mobile devices.
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Reviewed on Aug 6, 2022
Pretty Informative and crisp to the point. Great hands on course.
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