For those getting started with neural networks, autoencoders can look and sound intimidating. But in fact, they are a conceptually simple and elegant approach that will open many doors to an ML practitioner. They can be used for anomaly detection and missing value imputation or help in building better classifiers or clusters. In any case, what makes them unique is that they provide you with a mechanism for leveraging your unlabelled data, which often is much easier to get than labeled data. For instance, it's a lot easier to get a collection of images than it is to get a collection of images where each one is labeled to tell you what's in it.