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Short Course

Introduction to Rapidminer

Course ScheduleDuration (days)Mode of DeliveryTGS Reference Number
31 October 2025 | 9 am - 5.30 pm1Classroom LearningTGS-2025056127

Course Overview

Rapidminer is one of the most popular Machine Learning and Data Analytics environments in the market. Thanks to its newly-introduced features, users can do basic to advanced machine learning in Rapidminer. Its free version also allows users to experience rather professional machine learning algorithms deployment. This course introduces Rapidminer and its basic features to learners. Participants will practice basic data analytics and machine learning functions of Rapidminer too.

Course Objectives

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

  • Introduce Rapidminer and its different features and options inside the Rapidminer environment.
  • Design and implement machine learning systems using Rapidminer
  • Evaluate and test Rapidminer systems.

Who Should Attend

Machine Learning Developers and Python Developers.

Prerequisites

Basic AI and Machine Learning Knowledge

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This programme is part of

  • Professional Certificate in AI and Machine Learning Basics
  • Professional Certificate in Machine Learning for Business

    • Funding Available

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

    • 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.

    Course Fees


    Singapore Citizens Singapore PRs Enhanced Training Support for SMEs International Participants
    39 years old
    or younger    		 40 years old
    or older
    Full Course Fee S$950.00 S$950.00 S$950.00 S$950.00 S$950.00
    9% GST on Full Course Fee S$85.50 S$85.50 S$85.50 S$85.50 S$85.50
    SSG Funding S$475.00 S$475.00 S$475.00 S$475.00 -
    Additional Funding if Eligible under Various Schemes - S$190.00 - S$190.00 -
    Total Nett Course Fee Payable, including GST S$560.50 S$370.50 S$560.50 S$370.50 S$1,035.50
    1. Total nett course 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. The applicable funding is for Singaporeans and Singapore PRs. This is subject to change and in accordance with SSG funding guidelines as of course commencement.