Advanced Topics & Specializations
13
Post-Hoc Interpretability for Black-Box Models
Hands-On Machine Learning with R (2e)
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Preface
Foundations of the Machine Learning Process
1
The Machine Learning Landscape
2
A Tidy Modeling Workflow
3
Feature & Target Engineering
Supervised Learning Algorithms
4
Linear Models
5
Regularized Regression
6
Interpretable Glass-Box Models
7
Non-Linear Classics: SVM & KNN
8
Tree-Based Methods: From Single Trees to Random Forests
9
Gradient Boosting
10
Stacked Ensembles
11
Neural Networks & Deep Learning
Advanced Topics & Specializations
12
Unsupervised Learning
13
Post-Hoc Interpretability for Black-Box Models
14
Causal Inference with Machine Learning
15
Machine Learning with Text
16
Time Series Forecasting with ML
17
Machine Learning with Censored Data: Survival Analysis
From Model to Production
18
MLOps: Deploying and Monitoring Models
19
Ethical & Responsible AI
Appendices
R and RStudio Setup
A tidymodels Quick-Start Guide
References
Advanced Topics & Specializations
13
Post-Hoc Interpretability for Black-Box Models
13
Post-Hoc Interpretability for Black-Box Models
12
Unsupervised Learning
14
Causal Inference with Machine Learning