This document discusses unsupervised machine learning approaches for detecting outliers and data defects, including clustering, PCA, and autoencoders. It proposes using an autoencoder model to learn hidden patterns in unlabeled data attributes and then detect invalid records that do not conform to the learned patterns. The approach involves preparing AutoML pipelines for clustering, PCA, decision trees, and autoencoders, then using them in an ensemble model to generate rules for detecting outliers and data quality issues, which would be presented to users for validation.