Machine Learning with Apache Mahout

Learn via : Virtual Classroom / Online
Duration : 1 Day
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    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.


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



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



    ·       Programming skills in Java (or similar modern programming language)

    ·       Basic understanding of Hadoop architecture

    ·       Basic understanding of Hadoop MapReduce for data processing



  • 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