Practicing Machine Learning Interview Questions in R

Data preprocessing and visualization -This chapter discusses important topics related to data processing such as data normalization, handling missing data and identifying outliers. Supervised learning -This chapter discusses important topics within supervised learning such as model interpretability, regularization, the bias-variance tradeoff and model ensembling. Unsupervised learning -This chapter revolves around the most common types of unsupervised learning methods: clustering and dimensionality reduction via unsupervised feature selection and feature extraction. Model selection and evaluation -This chapter covers topics related to model selection and evaluation, imbalanced classification and hyperparameter tuning . It also sheds light on the commonalities and differences between two top-performing ensemble models: Random Forests and Gradient Boosted Trees.