Machine learning has finally come of age.

With H2O software, you can solve machine learning and data analytics problems using an easy-to-use, open source framework that supports a wide variety of operating systems and programming languages, and also scales to process big data. This practical guide will teach you how to use the machine learning algorithms implemented in H2O, focusing on the aspects that matter most to your productivity.

If you can program in R or Python, have at least some knowledge of statistics, and have experience in data science , this book by Darren Cook will introduce you to the basics of using H2O and help you experiment with machine learning on data sets of different sizes. You will learn several modern machine learning algorithms: deep learning, random forest, learning from unlabeled data, and model ensembles.

After reading this book, you will:

learn how to import data into H2O, transform them and export them from H2O; Learn basic machine learning concepts such as cross-validation and validation datasets; Work with three different datasets, solving regression, binary and multi-class classification problems; Use H2O to analyze each dataset using four machine learning algorithms; understand how cluster analysis and other learning algorithms on unlabeled data work. Understanding the process of building models, dead ends and experiments ending in failure is no less important than learning code!