The four basics of machine learning are data preparation, model selection, model training, and model evaluation. Data preparation involves collecting, cleaning, and preparing data for use in training models. Model selection involves choosing the appropriate algorithm for a given task. Model training involves optimizing the chosen algorithm to achieve the desired outcome. Model evaluation consists of assessing the performance of the trained model on new data.