Working through this book
https://bradleyboehmke.github.io/HOML/
- Chapter 01 Notes: Introduction
- Chapter 02 Notes: Model Process | R Code
- Chapter 03 Notes: Feature & Target Engineering
- Chapter 04 Notes: Linear Regression
- Chapter 05 Notes: Logistic Regression
- Chapter 06 Notes: Regularized Regression
- Chapter 07 Notes: Multivariate Adaptive Regression Splines
- Chapter 08 Notes: K-Nearest Neighbors
- Chapter 09 Notes: Decision Trees
- Chapter 10 Notes: Bagging
- Chapter 11 Notes: Random Forests
- Chapter 12 Notes: Gradient Boosting
- Chapter 13 Notes: Deep Learning
- Chapter 14 Notes: Support Vector Machines
- Chapter 15 Notes: Stacked Models
- Chapter 16 Notes: Interpretable Machine Learning
- Chapter 17 Notes: Principal Components Analysis
- Chapter 18 Notes: Generalized Low Rank Models
- Chapter 19 Notes: Autoencoders
- Chapter 20 Notes: K-means Clustering
- Chapter 21 Notes: Hierarchical Clustering
- Chapter 22 Notes: Model-based Clustering