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Professional Certificate

Professional Certificate in Applied Machine Learning

Programme Structure


Programme NameProgramme ScheduleMode of DeliveryProgramme Code
Business Intelligentization Using Machine Learning and RapidminerTo be advisedClassroom LearningTGS-2022011495
 To be advisedSynchronous e-LearningTGS-2022011576
Python Programming6, 7 and 9 January 2025 | 9 am - 5.30 pmClassroom LearningTGS-2022011018
Applied Machine Learning10 and 11 February 2025 | 9 am - 5.30 pmSynchronous e-LearningTGS-2024044671
Duration 7 days

Programme Overview

The Professional Certificate in Applied Machine Learning equips learners with comprehensive knowledge of a wide spectrum of machine learning algorithms, their hyperparameters, parameters, optimization, and practical applications. Participants will master the use of Rapidminer to implement these algorithms, develop and evaluate machine learning models, and represent results through Rapidminer’s visualization facilities. The programme also emphasises foundational and advanced machine learning programming skills in Python, ensuring a deep understanding of various machine learning approaches and their implementation using Python programming.

Programme Objectives

This PC will equip learners with the following competencies:

  • Understanding a wide spectrum of ML algorithms, their parameters, and their applications.
  • Knowing how to use Rapidminer to implement ML algorithms.
  • Understand how to develop machine learning models and how to evaluate them.
  • Gathering basic and machine learning Python programming skills.
  • Practicing how to represent the machine learning results using Rapidminer visualization facilities.
  • Understanding different machine learning approaches and how to implement them.

Job Role Readiness

It will prepare learners in the following job roles to perform their responsibilities more effectively/ It will prepare learners for the following job roles:

  • ICT engineers and technicians
  • ICT executives
  • Data analysts / business analysts
  • Advisors and data service providers

Who Should Attend

Mid-career PMETs looking to pivot into roles requiring machine learning competencies.

Prerequisites

At least a polytechnic diploma

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This programme is part of

  • Advanced Professional Certificate in Applied Artificial Intelligence and Machine Learning

    • Types of Available Fundings

  • Enhanced Training Support
  • SFEC
  • PSEA

    • Instructors


    A/Prof Danny Poo

    A/Prof Danny Poo

    A/Prof Danny Poo brings with him 35 years of Software Engineering and Information Technology and Management experience. A graduate from the University of Manchester Institute of Science and Technology (UMIST), England, Dr Poo is currently an Associate Professor at the Department of Information Systems and Analytics, National University of Singapore. Prior to joining the University, Dr Poo was with the System Operations at DBSBank, Singapore. A Steering Committee member of the Asia-Pacific Software Engineering Conference, Dr Poo is actively involved in Information Management and Healthcare Analytics research. A well-known speaker in seminars, Dr Poo has conducted numerous in-house training and consultancy for organizations, both locally and regionally. Dr Poo is the author of five books on Object-Oriented Software Engineering, Java Programming language and Enterprise JavaBeans.

    Dr Poo notable teaching credentials include:

    • Data Strategy
    • Data StoryTelling
    • Data Visualisation
    • Data Analytics
    • Machine Learning
    • Data Management
    • Data Governance
    • Data Architecture
    • Capstone Projects for Business Analytics
    • Software Engineering
    • Server-side Systems Design and Development
    • Information Technology Project Management
    • Health Informatics
    • Healthcare Analytics
    • Health Informatics Leadership.
    Industry Credentials
    • Deutsche Bank
    • Gemplus
    • Micron
    • NCR
    • PIL
    • PSA
    • Rhode-Schwarz
    • Standard Chartered Bank
    • ST Electronic
    • Monetary Authority of Singapore
    • Infocomm Development Authority
    • National Library Board
    • Ministry of Manpower
    • Nanyang Technological University
    • Nanyang Polytechnic
    • National University Hospital.

    Dr Ai Xin

    Dr Ai Xin

    Dr Ai Xin is currently a Lecturer with the School of Computing at the National University of Singapore (NUS). She has many years’ experience teaching Artificial Intelligence and Data Science courses such as machine learning, deep learning and data mining. She graduated from NUS with a PhD degree in Electrical and Computer Engineering. Her research focused on Game Theoretical Modelling, Optimization Methods, Algorithm Design and Wireless Networks. She worked at BHP Billiton Marketing Asia for eight years, gaining extensive industry experience in various functions such as risk management, supply chain management, and sales and marketing planning.

    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.

    A/Prof Tan Wee Kek

    A/Prof Tan Wee Kek

    A/Prof Tan Wee Kek is currently an Associate Professor in the Department of Information Systems and Analytics at the School of Computing, National University of Singapore. He is also currently serving as an Assistant Dean (Student Life) in the School of Computing, and a Fellow and EXCO member of the NUS Teaching Academy. He graduated with a Doctor of Philosophy in Information Systems in July 2013 and a Bachelor of Computing in Information Systems (1st Class Honours) in July 2007, both from the National University of Singapore. Prior to this, he attended Singapore Polytechnic and graduated with a Diploma in Computer Information Systems with Merit in July 2001. His current primary research interests focus on consumer-based information technology (e.g., online decision aids, social computing, virtual worlds and consumer cloud services). Most of his research is based on design science, a well-established problem-solving paradigm that has been widely adopted in information systems research. His current secondary research interests focus on information systems education. His work has been published or is forthcoming in journals such as Journal of the American Society for Information Science and Technology (JASIST), Decision Support Systems (DSS), Communications of the Association for Information Systems (CAIS), and Journal of Information Systems Education (JISE). His work has also been presented or is forthcoming in conferences such as ACM SIGMIS Computer Personnel Research Conference (SIGMIS-CPR), IFIP Working Group 8.2 Working Conference (IFIP WG8.2), European Conference on Information Systems (ECIS), Americas Conference on Information Systems (AMCIS), and International Conference on Human-Computer Interaction (ICHCI). He has won the faculty level Research Achievement Award in 2013 and was a nominee for the ACM SIGMIS 2009 Magid Igbaria Outstanding Conference Paper of the Year. His current teaching interests include imparting senior undergraduate students with knowledge and skills to develop enterprise information systems as well as teaching them principles of information security management, and concepts of mobile and ubiquitous commerce. He has won the University level Annual Teaching Excellence Award for AY 2009 to 2012. In addition to this, he has also won the Faculty Teaching Excellence Award for AY 2008 to 2011 as well as the Faculty Best Teaching Assistant Award for AY 2007/2008. For his sustained commitment to teaching excellence, he has been placed on the Faculty Teaching Excellence Award Honour Roll for AY 2010/2011 and University level Annual Teaching Excellence Award Honour Roll for AY 2012/2013. He is presently the lead faculty mentor of the NUS BiZiT Society, a student special interest group on business and information technology.

    Dr Leong Wai Kay

    Dr Leong Wai Kay

    Dr Leong Wai Kay is a lecturer with the Department of Computer Science at National University of Singapore (NUS). He graduated with Bachelor of Computing and PhD in Computer Science from National University of Singapore. He is currently teaching undergraduate courses in programming methodology, and software engineering on iOS platform.

    Dr Yeo Wee Kiang

    Dr Yeo Wee Kiang

    Dr Yeo Wee Kiang is a Senior Lecturer at the Department of Information Systems and Analytics in the NUS School of Computing, where he is dedicated to researching the use of technologies like Adaptive Learning and Large Language Models (LLMs) to create personalized and engaging computing education experiences. With a PhD from NUS focused on machine learning and data mining for pharmaceutical drug discovery, Dr Yeo brings practical industry experience as the former Director of Data Science at a food tech startup, where he led a team applying AI and machine learning to enhance nutritional value. His diverse background includes being an Adjunct Lecturer in the Department of Economics at NUS Faculty of Arts and Social Sciences, helming the Big Data Analytics module in the Master of Economics programme from 2019 to 2023, and teaching Python programming and data analytics at Nanyang Business School. Prior to his academic appointments, Dr Yeo was the Lead Instructor for Data Science at a New York-based commercial Continuing Education and Training (CET) provider, teaching aspiring adult learners in Hong Kong, Sydney, and Singapore. He also holds the WSQ Advanced Certificate in Training and Assessment (ACTA) from the Institute for Adult Learning Singapore. As part of the NUS ACE (Advanced Computing for Executives) teaching team, Dr Yeo specializes in technical training and practical computing education focused on data analytics, machine learning, and Python, aiming to equip professionals with in-demand Data Science skills.

    Dr Edmund Low

    Dr Edmund Low

    Dr Edmund Low is currently Senior Lecturer with the NUS College at the National University of Singapore. He has nearly 20 years of academic and professional experience in the use of data-driven tools to answer questions in public health and the environment. His past projects include applying AI techniques and machine learning models for environmental modelling and impact assessment. He currently heads the quantitative reasoning domain at USP, and teaches courses on statistical methods, data science and machine learning. As an educator, Edmund is a multiple recipient of both the USP Teaching Excellence Award, as well as the NUS Annual Teaching Excellence Award. Edmund holds a PhD in Environmental Engineering from Yale University.

    Programme Fees


    Singapore Citizens Singapore PRs Enhanced Training Support for SMEs International Participants
    39 years old or younger 40 years old or older
    S$2,076.45 S$806.45 S$2,076.45 S$806.45 S$6,921.50
    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 for all short courses within a professional certificate in order 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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