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Applied Machine Learning

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Acquire practical skills with this machine learning course in Singapore. Solve business problems by implementing machine learning concepts, models and tools.
Date
  • 17 Jan, 18 Jan 2022 (9am – 5.30pm) (Online)
Duration
2 Days
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.

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

Topics

  • 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

Prerequisites

Basic AI knowledge and basic Python programming skills

Course Fee

Singapore Citizens Singapore PRs Enhanced Training Support for SMEs International Participants
39 years old or younger 40 years old or older
Total Nett Programme Fee Payable, Including GST, after additional funding from the various funding schemes S$609.90 S$229.90 S$609.90 S$229.90 S$2,033.00

To enquire, email soc-ace@nus.edu.sg

To register click Register

Course Code: TGS-2020506040

Instructor

(Click their names to view their short biographies)

Dr Amirhassan Monajemi

Dr Manoranjan Dash

Dr Edmund Low

*Course Fee Breakdown

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
7% GST on Nett Programme Fee S$39.90 S$39.90 S$39.90 S$39.90 S$133.00
Total Nett Programme Fee Payable, Including GST S$609.90 S$609.90 S$609.90 S$609.90 S$2,033.00
Less Additional Funding if Eligible Under Various Schemes (S$380.00) (S$380.00)
Total Nett Programme Fee Payable, Including GST, after additional funding from the various funding schemes S$609.90 S$229.90 S$609.90 S$229.90 S$2,033.00