• Authored Course

About this Course

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.

Target Audience

Software Engineers, Data Scientists, or Technologists with a background in Java programming or a similar modern programming language.

Learn from the UK's leading Data trainer

  • Unrivaled curriculum

    We offer a wide selection of courses for all levels of data experience.

  • Leading certifications

    We offer tracks to professional capabilities.

  • Tailored for business

    We offer courses for business leaders - as well as specialists.

Why people choose QA


There are over 20 QA learning centres and many other sites spread across the UK, providing a convenient choice of learning locations and ensuring that over 90% of the population is within 45 minutes of a training destination. Learn more

  • London


    International House

  • Manchester


    Oxford Street

Delegate portal

Booking courses with QA has always been easy, but now we've made it even easier. With myQA you can book, administer and manage all your bookings online, in one place. Login / sign-up

Course Information


  • Machine learning system classifications
  • Capabilities and limitations

Use Cases

  • Top level use case categorisations
  • Identifying and categorising your own use case
  • Deep-dive use case example


  • Technology landscape
  • Capabilities and limitations
  • Selecting the right tools for the job
  • Implementation choices
  • Optimisation
  • Performance and scalability
  • Integration


  • Programming skills in Java (or similar modern programming language)
  • Basic understanding of Hadoop architecture
  • Basic understanding of Hadoop MapReduce for data processing at sca

Useful, but not required:

  • Apache Pig programming
  • Prior experience with Apache Solr search engine
  • Matrix algebra

At the end of this course you will be able to understand:

  • 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
  • How to use case identification and structure
  • How to structure and plan a machine learning project for your business