A comprehensive beginner's guide to machine learning concepts, popular R packages like caret and tidymodels, and how to build your first ML model.
Tutorial series
R Machine Learning
13 tutorials — follow in order for the best learning path.
- Introduction to Machine Learning in R
- Introduction to Supervised Learning in R
Learn regression vs classification, then build your first models with the tidymodels framework in R. R has no shortage of machine learning packages.
- Hyperparameter Tuning in R
Optimize ML models in R with grid search, random search, and Bayesian hyperparameter tuning using caret and tidymodels.
- Caret Classification in R: Train, Tune, and Evaluate Models
Caret classification in R: train models, tune hyperparameters, evaluate with confusion matrices, and deploy classifiers using the unified caret framework.
- Random Forests in R, Train random forests in R with the
Train random forests in R with the randomForest package and tidymodels, tune key hyperparameters, and interpret feature importance scores.
- Cross-Validation in R
Learn how to evaluate machine learning models reliably using k-fold cross-validation, caret, and tidymodels in R.
- Tidymodels Regression: Build, Tune, and Evaluate Models in R
Build, tune, and evaluate Tidymodels regression models in R. Covers data splitting, preprocessing, model fitting, cross-validation, and hyperparameter tuning.
- Random Forests in R — Build, Tune, and Interpret
Build random forests in R with tidymodels — from data preparation through hyperparameter tuning to model evaluation and prediction, using the Titanic dataset.
- Gradient Boosting with xgboost
Learn how to build powerful gradient boosting models in R. Covers basics, hyperparameters, tuning, and best practices for classification and regression.
- Model Evaluation and Cross-Validation
Learn to evaluate ML models in R with train/test splits, k-fold cross-validation, and metrics like accuracy, precision, recall, F1, and ROC AUC.
- Feature Engineering in R: Transform Raw Data for ML Models
Feature engineering in R: numeric scaling, categorical encoding, date extraction, and missing value imputation using tidyverse recipes and tidymodels.
- Model Evaluation Metrics in R
Learn how to evaluate regression and classification models in R using caret, yardstick, and tidymodels with practical code examples.
- Building Classification Models with tidymodels in R
Building classification models with tidymodels in R: logistic regression, random forests, accuracy, ROC AUC, and confusion matrices.