This document discusses machine learning and its use in business. It explains that machine learning algorithms are trained on data to make predictions, getting more accurate over time as more data is fed to the algorithms. Typical machine learning involves training algorithms on a dataset with known values, then testing the trained algorithms on "fresh" test data to measure prediction accuracy. While machine learning focuses on prediction rather than causality, data scientists aim to avoid overfitting to improve out-of-sample prediction accuracy. Competitions like Kaggle also use a standard training and test set approach to benchmark machine learning models.