Applied Machine Learning

Dates: 22 Jun, 23 Jun 2023 | 9am – 5.30pm | Online

Duration: 2 Days

Course Overview

This course will introduce participants to machine learning, focusing more on the practical and applied aspects rather than theory. The course will discuss machine learning concepts, and briefly introduce Python, PyCharm environment, Scikit-learn, Numpy, Anaconda, and Keras toolkits.

Regression as a basic machine learning method will be discussed and practised. Different models and examples of regression will be reviewed. Support Vector Machines (SVM) along with their applications in function estimation and classification will also be introduced. We will also discuss artificial neural networks and introduce deep learning.

This module is part of Professional Certificate in Applied Machine Learning.

Course Objectives

Participants will learn how to implement machine learning to solve real-life problems more productively and efficiently.

Learning Outcomes

At the end of this course, participants will be able to:
• Understand the way regression, support vector machines (SVM), and artificial neural networks (ANN) work
• Recognise the applications, advantages and disadvantages of regression, SVM, and ANN methods
• Design and implement basic regression, SVM-based, and ANN-based algorithms in clustering, classification, and function estimation applications


  • Overview
    • Getting familiar with the course
    • A brief review of AI
    • Machine Learnings definitions and terms
    • Machine Learning applications
    • Introduction to Python programming
  • Regression
    • Linear regression
    • Non-linear regression
    • How to implement regression in Python?
  • SVM
    • What are Support Vector Machines (SVM)?
    • SVM implementation
  • Artificial Neural Networks
    • What are Artificial Neural Networks (ANNs)?
    • History
    • Basic ANN models: Perceptron
    • Training
    • Multilayer Perceptrons (MLP) and non-linear mapping
    • Supervised and unsupervised schemes
    • How to implement and train an MLP in Python?
    • Generality
    • Evaluation and Performance Measurement
  • Deep Learning Introduction
    • What is Deep Learning?
    • Why Deep Learning is Important and Effective?
    • State of the Art Instances
    • Basic Deep Learning Models
    • Deep Learning Environments

Who Should Attend

Data Analysts, IT Experts, Chief Technology Officers (CTOs), Technical Advisors, Intermediate-level Managers


Basic AI knowledge and basic Python programming skills. Click to view the course detail of Python Programming.

Course Conveners

(Click their photos to view their short biographies)

ddddd Ai Xin

Dr Ai XinDr Ai Xin

ddddd Amirhassan Monajemi

Dr Amirhassan MonajemiDr Amirhassan Monajemi

ddddd Edmund Low

Dr Edmund LowDr Edmund Low

ddddd Manoranjan Dash

Dr Manoranjan DashDr Manoranjan Dash

Course Fees

Singapore Citizens
39 years old or younger
40 years old or older
Singapore PRs
Enhanced Training Support for SMEs
International Participants

Total Nett Programme Fee Payable, Including GST, after additional funding from the various funding schemes

Participants must fulfill at least 75% attendance and pass all assessment components to be eligible for SSG funding.

NTUC members enjoy 50% unfunded course fee support for up to S$250 each year (or up to S$500 for NTUC members aged 40 years old and above) when you sign up for courses supported under UTAP (Union Training Assistance Programme). Please visit e2i’s website to find out more.

To enquire, email

To register, click Register

Course Code:

Select Short Course / Modular Course -> Apply for Myself -> Browse Academic Modules / Short Courses-> Module/Course Category -> Short Courses -> Browse Courses-> Advanced Computing for Exe (Faculty/Department / Unit)

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Course Fee Breakdown

Singapore Citizens

Singapore Citizens

39 years old or younger

Singapore Citizen

40 years old or older
Singapore PRs
Enhanced Training Support for SMEs
International Participants

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