This one-day course is designed to help Software Engineers and Data Scientists understand the high-level concepts and classifications of machine learning systems, with a strong focus on building Recommender Systems.

You will gain an understanding of the tools and high-level conceptual ideas needed to understand what a machine learning solution is (and is not) capable of, and how to identify a suitable use case. You will learn how to construct an example solution at the conceptual level using pre-provided building blocks in order to get a feel for the general design patterns. You will learn hands-on how to build a scalable hybrid real-time Recommender System based on Apache Hadoop, Apache Mahout, and Apache Solr, and how to optimise the system to deliver real business value.

The course has been jointly designed and written by Big Data Partnership and Ted Dunning, world renowned expert in Recommender Systems and co-author of Mahout in Action (Manning) which is also recommended as a companion book to the course.

This masterclass will enable you to:

> Classes and categories of machine learning systems
> Capabilities and limitations of end solutions, in business terms
> Capabilities and limitations of technology, in solution capability terms
> Use case identification and structure
> How to structure and plan a machine learning project for your business