Code and resources for the 2nd edition of "Hands-on Machine Learning with R: An applied book covering the fundamentals of machine learning with R" by Boehmke & Greenwell, which covers commonly applied supervised and unsupervised methods to include:
- Generalized low rank models
- Clustering algorithms
- Autoencoders
- Regularized models
- Random forests
- Gradient boosting machines
- Deep neural networks
- Stacking / super learners
- and more!