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Short Course

Business Intelligentization Using Machine Learning and Rapidminer

Programme ScheduleDuration (days)Mode of DeliveryProgramme Code
To be advised2Classroom LearningTGS-2022011495
To be advised2Synchronous e-LearningTGS-2022011576
Duration: 2 Days

Course Overview

Machine Learning (ML) operates on the basis of algorithms that enable computers to learn, mostly from data. ML algorithms attempt to be trained directly by data samples without relying vastly on a predetermined design and coding. ML systems can also adapt to the new situation and enhance their performance over time. The course will introduce some of the algorithms in the field of ML, such as neural networks, deep learning, support vector machines, K nearest neighbours, and c-means clustering to name but a few. Rapidminer is a popular data mining and machine learning software platform that provides an integrated environment for the realization of almost all known machine learning algorithms, and is accessible to the public as a free desktop application. Organisations can leverage Rapidminer for many real-world ML applications including data preparation, results visualisation, model validation and optimisation.

Course Objectives

At the end of this course, participants will be able to:

  • Understand a wide spectrum of Machine Learning (ML) algorithms, their parameters, and applications.
  • Know how to use Rapidminer to implement ML algorithms.
  • Understand how to develop machine learning models and how to evaluate them.
  • Represent the machine learning results using Rapidminer visualization facilities.

Job Role Readiness

Machine Learning and Python developers.

Prerequisites

Basic AI and Machine Learning knowledge

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This programme is part of

  • Professional Certificate in Applied Machine Learning

    • Types of Available Fundings

  • SkillsFuture Funding
  • SkillsFuture Credit
  • Enhanced Training Support
  • SFEC
  • UTAP
  • PSEA

    • Testimonials


    It was a great introduction to the rapidminer platform.

    A very enriching experience.

    Very much useful for applying Machine Learning and Deep Learning using Rapidminer.

    Instructors


    Dr Amirhassan Monajemi

    Dr Amirhassan Monajemi

    Dr Amirhassan Monajemi is a Senior Lecturer in AI and Machine Learning with the School of Computing (SoC) at the National University of Singapore (NUS). Prior to SoC, he was a Senior Lecturer in NUS School of Continuing and Lifelong Education (SCALE) teaching AI and Data Science to adult learners. Before joining NUS, he was with the Faculty of Computer Engineering, University of Isfahan, Iran, where he was serving as a professor of AI, Machine Learning, and Data Science. He was born in Isfahan, Iran. He studied towards BSc and MSc in Computer Engineering at Isfahan University of Technology (IUT), and Shiraz University respectively. He got his PhD in computer engineering, pattern recognition and image processing, from the University of Bristol, Bristol, England, in 2005. His research interests include AI, Machine Learning, Machine Vision, IoT, Data Science, and their applications. He has taught the artificial intelligence courses, including AI, Advanced AI, Expert Systems, Decision Support Systems, Neural Networks, and Cognitive Science since 2005 at both undergraduate and postgraduate levels. He was awarded the best university teacher of the province in 2012. He also has studied Learning Management Systems, E-Learning, and E-Learning for workplaces since 2007. Dr Monajemi has registered a few patents in the fields of AI, Machine Vision, and Signal Processing applications, including an AI and machine vision-based driver drowsiness detection system and a low power consuming spherical robot. He also has published more than a hundred research papers in peer-reviewed, indexed journals and international conferences (IEEE, Elsevier, Springer, and so on), and supervised several Data Science, IoT, and AI industrial projects in various scales, including Isfahan intelligent traffic system delivery and testing, and red light runners detection. He is experienced in different sub-domains of Artificial Intelligence and Machine Learning, from theory to practice, including Deep Learning, Logic, and Optimisation.

    Programme Fees


    Singapore Citizens Singapore PRs Enhanced Training Support for SMEs International Participants
    39 years old
    or younger    		 40 years old
    or older
    Full Programme Fee S$1,900.00 S$1,900.00 S$1,900.00 S$1,900.00 S$1,900.00
    Less: SSG Grant Amount S$1,330.00 S$1,330.00 S$1,330.00 S$1,330.00 -
    Nett Programme Fee S$570.00 S$570.00 S$570.00 S$570.00 S$1,900.00
    9% GST on Nett Programme Fee S$51.30 S$51.30 S$51.30 S$51.30 S$171.00
    Total Nett Programme Fee Payable, Including GST S$621.30 S$621.30 S$621.30 S$621.30 S$2,071.00
    Less Additional Funding - S$380.00 - S$380.00 -
    Total Nett Programme Fee Payable, Including GST, after additional funding from the various funding schemes S$621.30 S$241.30 S$621.30 S$241.30 S$2,071.00
    1. Total Nett Programme Fee Payable, including GST, after additional funding from various funding schemes.
    2. Participants must fulfill at least 75% attendance and pass all assessment components to be eligible for SSG funding.
    3. Please note that all external funding for courses is limited in duration and subject to eligibility and availability.

    Advanced Computing for Executives

    11 Research Link
    COM 3, #B1-07
    Singapore 119391
    Email: soc-ace@nus.edu.sg

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