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R Programming – Advanced
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R Programming – Advanced
  • Overview
  • Objectives & Outline
  • Methodology
  • Participant Profile
  • Trainer
  • Overview
    PROGRAMME DETAILS

    Duration

    2 days

    TIME

    9:00 AM – 5:00 PM

    VENUE

    Asian Banking School
    16
    AICB
    CPD HOURS
    This programme is an advanced level course in using R for machine learning and data modelling. It has been designed for participants to grasp the concept of machine learning for supervised and un-supervised learning, as well as understand the machine learning model evaluation work flow and participate in the model fitting exercise.
    LEARNING LEVEL
    Advanced
    PROGRAMME FEE

    AICB MEMBER

    MYR

    2,200

    / PAX

    NON-MEMBER

    MYR

    2,600

    / PAX

  • Objectives & Outline
    LEARNING OBJECTIVES
    By the end of this programme, participants will be able to:

    • Understand the concept of machine learning
    • Able to differentiate between supervised versus un-supervised learning
    • Understand machine learning model evaluation work flow
    • How to fit and use a machine learning model

    PROGRAMME OUTLINE
    Introduction to R in Machine Learning
    • Introduction to machine learning concepts
    • Types of machine learning, supervised and un-supervised learning
    • Use case sharing

    Machine Learning in R
    • Types of machine learning algorithm for continuous data and categorical data
    • What is decision tree and its application
    • Unsupervised learning, data clustering and its application
    • Model validation and testing with ‘caret’ modules
    • Workshop in fitting and evaluating a machine learning model

  • Methodology

    Slides presentation, interactive discussions, programming workshop (own computer), experience sharing

  • Participant Profile
    Data analysts, associate data analysts and business analysts / market researchers. Participants should already have intermediate level knowledge in R Programming.
  • Trainer

    Xavier Leong Foo Hoong

    Xavier is a data scientist providing data intelligent solutions in areas covering data pattern analysis and machine learning modelling for business insights and process automation deployed in carrier service providers and enterprises to manage daily business operations. Prior to this, he was a software architect providing IT solutions for the business operations and business intelligence sectors particularly in data mediation, business support and operations support systems.

    Having spent over 18 years in the software industry, Xavier’s experience includes business operations optimisation, IT software solutions for the digital economy and business automation via data driven intelligence software. He has extensive exposure in big data technology, manufacturing, machine learning and artificial intelligence in business process optimisation. He also has vast experience in software design and development through his career at a multinational company providing solutions to customers world-wide, together with a recent five years spent in providing software solutions covering big data distributed computing for data analytics and modelling.
    Xavier has a Master of Science degree in Mathematics and Statistics (with Distinction) from the University of Malaya, a Bachelor of Engineering degree (with Honours) from the University of Science Malaysia and is also a Certified IT Architect (Associates Level) (CITA-A) from the International Association of Software Architects (IASA). He is one of the pioneer graduates of the Malaysian MDEC Data Scientist Certification programme.

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