Machine Learning at Scale with H2O

Section 1 – Introduction to the H2O Machine Learning Platform for Data at Scale

This section provides a general background of machine learning (ML) at scale with H2O. We will define ML at scale, focus on its challenges, and then see how H2O overcomes these challenges. We will then overview each H2O component to better understand its purpose and how it works from a technical standpoint. We will then put the components to work by implementing a minimal workflow. After this section, we will be ready to dive into advanced topics and techniques.

This section comprises the following chapters:

• Chapter 1, Opportunities and Challenges
• Chapter 2, Platform Components and Key Concepts
• Chapter 3, Fundamental Workflow – Data to Deployable Model