Introduction to Statistical Learning with MLJ

This is a sequence of tutorials adapted from the labs associated with An introduction to statistical learning which were originally written in R. These tutorials may be useful if you want a gentle intro to MLJ and other relevant tools in the Julia environment. If you're fairly new to Julia and ML, this is probably where you should start.

Note: the adaptation is fairly liberal, adding content when it helps highlights specificities with MLJ and removing content when it seems unnecessary. Also note that some of the things used in the ISL labs are not (yet) supported by MLJ.

• Lab 2, a very short intro to Julia for data analysis

• Lab 3, linear regression and metrics

• Lab 4, classification with LDA, QDA, KNN and metrics

• Lab 5, k-folds cross validation

• Lab 6b, Ridge and Lasso regression

• Lab 8, Tree-based models

• Lab 9, SVM (partial)

• Lab 10, PCA and clustering (partial)