- Applied Machine Learning
- Predictive Modeling
- Regression Analysis
- Data Preprocessing
- Classification Algorithms
- Machine Learning
- Scikit Learn (Machine Learning Library)
- Decision Tree Learning
- Random Forest Algorithm
- Model Evaluation
- Exploratory Data Analysis
Interpretable Machine Learning Applications: Part 2
Completed by Roberta Besseghini
August 25, 2021
1 hours (approximately)
Roberta Besseghini'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
