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Short Course

Machine Learning in Python

Programme ScheduleDuration (days)Mode of DeliveryProgramme Code
To be advised3Classroom LearningTGS-2022011025

Course Overview

Machine learning is a branch of Artificial Intelligence which makes use of sample data known as training data to build a model for making predictions or decisions without being explicitly programmed to do so. It has been applied in many applications such as email filtering, text analysis, sales forecasting, digital marketing, computer vision, healthcare, etc. where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks. In this course, participants will learn the fundamental concepts of Machine Learning. These include Supervised and Unsupervised Learning methods such as Linear Regression, Logistics Regression, Support Vector Machines, K-Nearest Neighbours, K-Means and Hierarchical Clustering. This course teaches how to use Python to create Machine Learning applications.

Course Objectives

This course aims to:

  • Provide participants with a strong foundation in advanced analytics methods.
  • Provide participants with the necessary knowledge to appreciate the proper application of advanced analytics methods in business applications.
  • Teach participants the use of Python for creating Machine Learning applications.

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

  • Understand various machine learning methods for classifying and clustering data.
  • Use Python to implement Machine Learning methods in business applications.

Job Role Readiness

Managers, research analysts and software engineers.

Prerequisites

  • Diploma.
  • Basic Python programming skills. Click to view the course detail of Python Programming.
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This programme is part of

  • Professional Certificate in Machine Learning Using Python

    • Types of Available Fundings

  • Enhanced Training Support
  • SFEC
  • UTAP
  • PSEA

    • Testimonials


    Great delivery! Lecturer was effective and engaging! Well established learning materials and very engaging instructors.

    It is a useful course for those with fundamental knowledge in Python.

    Dr Yeo is able to deliver the course in a clear and concise manner This course went over and above in teaching us not just machine learning, but the history and reasoning behind it. It answers the very fundamental question of "why we do what we do?". This made the coding so much easier to understand, and made the class very entertaining. Both the educator and the admin support are exceptionally well planned out. Amongst all the other courses I have taken, this is by far one of the best course I took.

    Dr Ai Xin teaches the subject extremely well. She explains the concepts in detail., allowing students from different backgrounds and experiences to be able to follow the content. Her materials are well prepared (the Jupyter notebooks) allow us to quickly follow and appreciate how the models can be built on python.

    Great course in understanding ML. The teacher explained it well.

    Professor Ai Xin teaches very well and goes through the content very thoroughly! Good job.

    Really enjoyed the prof teaching.

    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 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.

    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$2,850.00 S$2,850.00 S$2,850.00 S$2,850.00 S$2,850.00
    Less: SSG Grant Amount S$1,995.00 S$1,995.00 S$1,995.00 S$1,995.00 -
    Nett Programme Fee S$855.00 S$855.00 S$855.00 S$855.00 S$2,850.00
    9% GST on Nett Programme Fee S$76.95 S$76.95 S$76.95 S$76.95 S$256.50
    Total Nett Programme Fee Payable, Including GST S$931.95 S$931.95 S$931.95 S$931.95 S$3,106.50
    Less Additional Funding - S$570.00 - S$570.00 -
    Total Nett Programme Fee Payable, Including GST, after additional funding from the various funding schemes S$931.95 S$361.95 S$931.95 S$361.95 S$3,106.50
    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 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.

    Other Information

    This course is eligible for Union Training Assistance Programme (UTAP). NTUC members can enjoy up to 50% funding (capped at $250 per year) under UTAP. NTUC members aged 40 and above can enjoy higher funding support up to $500 per individual each year, capped at 50% of unfunded course fees, for courses attended between 1 July 2020 to 31 December 2025. Please click here for more information.

    Advanced Computing for Executives

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

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