Appendices
Appendix 4: Bootstrapping Standard Errors & Confidence Intervals
Introduction to Applied Machine Learning
Course Syllabus
Course Materials
1
Overview of Machine Learning
2
Exploratory Data Analysis
3
Introduction to Regression Models
4
Introduction to Classification Models
5
Resampling Methods for Model Selection and Evaluation
6
Regularization and Penalized Models
Appendices
Review Midterm Concepts Exam
Key Terminology and Concepts
Novel Levels in Held-Out Set(s)
Principal Components Analysis
Installing Keras for Neural Networks
Appendix 3: Understanding Shapley Values from SHAP
Appendix 4: Bootstrapping Standard Errors & Confidence Intervals
7
Test
Appendices
Appendix 4: Bootstrapping Standard Errors & Confidence Intervals
Appendix 4: Bootstrapping Standard Errors & Confidence Intervals
Appendix 3: Understanding Shapley Values from SHAP
7
Test