- Applied Machine Learning
- Model Evaluation
- Responsible AI
- Machine Learning
- Data Import/Export
- Feature Engineering
- Random Forest Algorithm
- Classification Algorithms
- Decision Tree Learning
Interpretable Machine Learning Applications: Part 1
Completed by Roberta Besseghini
August 18, 2021
1 hours (approximately)
Roberta Besseghini's account is verified. Coursera certifies their successful completion of Interpretable Machine Learning Applications: Part 1
What you will learn
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.
Skills you will gain
