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Code](chap2/chapt02.Rmd)\n- [Chapter 03 Notes: Feature \u0026 Target Engineering](chap03/README.md)\n- [Chapter 04 Notes: Linear Regression](chap04/README.md)\n- [Chapter 05 Notes: Logistic Regression](chap05/README.md)\n- [Chapter 06 Notes: Regularized Regression](chap06/README.md)\n- [Chapter 07 Notes: Multivariate Adaptive Regression Splines](chap07/README.md)\n- [Chapter 08 Notes: K-Nearest Neighbors](chap08/README.md)\n- [Chapter 09 Notes: Decision Trees](chap09/README.md)\n- [Chapter 10 Notes: Bagging](chap10/README.md)\n- [Chapter 11 Notes: Random Forests](chap11/README.md)\n- [Chapter 12 Notes: Gradient Boosting](chap12/README.md)\n- [Chapter 13 Notes: Deep Learning](chap13/README.md)\n- [Chapter 14 Notes: Support Vector Machines](chap14/README.md)\n- [Chapter 15 Notes: Stacked Models](chap15/README.md)\n- [Chapter 16 Notes: Interpretable Machine Learning](chap16/README.md)\n- [Chapter 17 Notes: Principal Components Analysis](chap17/README.md)\n- [Chapter 18 Notes: Generalized Low Rank Models](chap18/README.md)\n- [Chapter 19 Notes: Autoencoders](chap19/README.md)\n- [Chapter 20 Notes: K-means Clustering](chap20/README.md)\n- [Chapter 21 Notes: Hierarchical Clustering](chap21/README.md)\n- [Chapter 22 Notes: Model-based Clustering](chap22/README.md)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmelling%2Fhandsonmachinelearningr","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmelling%2Fhandsonmachinelearningr","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmelling%2Fhandsonmachinelearningr/lists"}