Interpretable Machine Learning Applications: Part 2
Completed by Kavin Chandrasekar
April 12, 2026
1 hours (approximately)
Kavin Chandrasekar's account is verified. Coursera certifies their successful completion of Interpretable Machine Learning Applications: Part 2
What you will learn
Apply Local Interpretable Model-agnostic Explanations (LIME) as a machine learning interpretation
Explain individual predictions being made by a trained machine learning model.
Add aspects for individual predictions in your Machine Learning applications.
Skills you will gain
- Category: Classification Algorithms
- Category: Data Preprocessing
- Category: Decision Tree Learning
- Category: Model Evaluation
- Category: Machine Learning Methods
- Category: Machine Learning
- Category: Data Transformation
- Category: Random Forest Algorithm
- Category: Decision Intelligence
- Category: Regression Analysis
- Category: Predictive Modeling
- Category: Model Training
