Metalearning is the study of principled methods that exploit metaknowledge to obtain efficient models and solutions by adapting machine learning and data mining processes. The related area of automated machine learning (AutoML) is concerned with automating the machine learning processes. While the variety of machine learning and data mining techniques now available can, in principle, provide good model solutions, searching for the most appropriate model may be a difficult task. Metalearning and AutoML provide a methodology that allows systems to become more effective through experience. This book discusses approaches that exploit knowledge concerning machine learning and data mining algorithms, or more complex solutions (ensembles, workflows etc.) that include these algorithms as elements. It presents many current approaches how this knowledge can be reused to select, combine, compose, adapt and configure both algorithms and models to yield faster and better solutions to data mining and data science problems. It can thus help developers to develop systems that can improve themselves through experience. This book is a substantial update of the first edition published in 2009. It includes 18 chapters, more than twice as much as the previous version. This enabled the autors to cover the most relevant topics in more depth and incorporate the overview of recent research in the respective area. The book will be of interest to researchers and graduate students in the areas of machine learning, data mining, data science and artificial intelligence.