A Unified Tidy Interface to R's Machine Learning Ecosystem // Provides a unified tidyverse-compatible interface to R's machine learning ecosystem - from data ingestion to model publishing. The tl_read() family reads data from files ('CSV', 'Excel', 'Parquet', 'JSON'), databases ('SQLite', 'PostgreSQL', 'MySQL', 'BigQuery'), and cloud sources ('S3', 'GitHub', 'Kaggle'). The tl_model() function wraps established implementations from 'glmnet', 'randomForest', 'xgboost', 'e1071', 'rpart', 'gbm', 'nnet', 'cluster', 'dbscan', and others with consistent function signatures and tidy tibble output. Results flow into unified 'ggplot2'-based visualization and optional formatted 'gt' tables via the tl_table() family. The underlying algorithms are unchanged; 'tidylearn' simply makes them easier to use together. Access raw model objects via the $fit slot for package-specific functionality. Methods include random forests Breiman (2001) doi:10.1023/A:1010933404324, LASSO regression Tibshirani (1996) doi:10.1111/j.2517-6161.1996.tb02080.x, elastic net Zou and Hastie (2005) doi:10.1111/j.1467-9868.2005.00503.x, support vector machines Cortes and Vapnik (1995) doi:10.1007/BF00994018, and gradient boosting Friedman (2001) doi:10.1214/aos/1013203451.