Machine Learning with Apache Mahout
Friday 11 July, 9:00 AM 2014 to 5:00 PM 2014
116-120 Goswell Rd, London, England, EC1V 7DP (View Map)
Machine Learning with Apache Mahout:

Introduction to scalable ML for Developers

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.


1 Day (9am-5:00pm)

Course Objectives

This transformational class will unlock your understanding of:

  • 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
Course Outline


  •      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
Target Audience

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 at scale

Useful, but not required:

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

This course is a high-intensity 1-day accelerator designed to teach the basics of machine learning with a strong focus specifically on Recommender Systems.  The practical part of the course revolves around building a hybrid real-time Recommender.  Prior machine learning or Recommender System experience is not necessary since this course is aimed at the introductory level.


The course has been jointly designed and written by Big Data Partnership and Ted Dunning and Ellen Friedman, world renowned experts in Recommender Systems and co-authors of Mahout in Action (Manning) . All delegates will receive a copy of this book when they attend the class.





 Unlimited refreshments provided.


Cancellation & Reschedule Policy

You must provide a written notice to Big Data Partnership at least 2 weeks' prior to the start of the class if you cannot attend this class. Big Data Partnership will transfer your registration to a future class of equal or lesser value.

Students who fail to cancel within 2 weeks' and/or do not attend the class, will not receive a refund and will be charged the full amount.

Big Data Partnership can cancel or reschedule at any time at our discretion. In the event that the class is cancelled or rescheduled, we will work with you to apply your registration to another date or refund your fee in full. Big Data Partnership is not responsible for non-refundable travel or other expenses incurrred by the student.

Contact Information

If you have any questions concerning this class, please do not hesitate to contact training@bigdatapartnership.com.

Skills Matter
116-120 Goswell Rd, London, England, EC1V 7DP (View Map)
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