We consider a canonical machine learning problem: classifying spam. This directory contains three tutorials, described below:
01_spam_tutorial: This tutorial dives deep into how we can create, analyze, and use labeling functions for the spam classification task.02_spam_data_augmentation_tutorial: This tutorial demonstrates how to write, combine and apply transformation functions for performing data augmentation.03_spam_data_slicing_tutorial: This tutorial shows how we can use slicing functions to identify important slices of the data, for monitoring and improved performance.