This chapter closes Part I presenting advanced topics, including dealing with variable length feature vectors, Feature Engineering and Deep Learning and automatic Feature Engineering (either supervised or unsupervised). It starts bridging the pure domain independent techniques to start drilling into problems of domain-specific importance. Variable length feature vectors has been a problem for fixed-size vector ML ever since. In general, techniques involve truncation, computing the most general tree and encoding paths on it or just destructive projection into a smaller plane. The chapter briefly delve into some Deep Learning concepts and what it entails for feature engineering. Automated Feature Learning using FeatureTools (the DataScience Machine) and genetic programming is covered, also Instance Engineering and Unsupervised Feature Engineering (in the form of autoencoders).