Predictive Analytics and Machine Learning Module 2: Predictive Modelling for Categorical Data
- Analytics & Tech
- Artificial Intelligence
- Innovation & Business Improvement
This programme is conducted online.
3 days
Weeknights (7pm - 10:30pm)
Saturday (9am - 6:00pm)
Who Should Attend
- Participants who wish to advance their expertise in predictive models and machine learning, along with productivity tools for data science (e.g., creating and maintaining github and interactive dashboards), beyond the cornerstone knowledge and expertise on data analytics and visualisation, will find it a great fit and will learn the most on this course.
- Targeted Job Roles: Data analyst, Statisticians, Data scientist, Data Architect, Quantitative Analysis with R, System Intelligence Manager, Trading analyst
PREREQUISITES
Completion in R programming (equivalent to that attained in Certified Data Analytics (R) Specialist programme)
Overview
Participants will learn the differences between solving regression and classification problems in the workflow of predictive models like performance metrics. They will learn how to run and draw insights from both binary classification and multi-class classification tasks.
This module is part of a sequential programme and is not available on a standalone basis.
Learning Objectives
At the end of the 2-day module, participants will be able to:
- Understand core concepts of predictive models for classification
- Internalize a “workflow” of supervised machine learning for numerical outcomes.
- Grasp the XG Boost Algorithm
- Execute binary class and multi-class predictive models using tidymodels framework
Topic/Structure
- Running Classification Predictive Model with Random Forest
- Understanding Performance Metrics of Classification Model
- Understanding XG Boost Algorithm
- Running Classification Predictive Model with XG Boost
- Running Multiclass classification
- Learning How to Improve Performance of Classification Predictive Models
- Group Assignment
- Deployment of Supervised Machine Learning Algorithms
Assessment
- Group Assignment
CERTIFICATION
Upon meeting the attendance and assessment criteria, participants will be awarded a digital certificate for participating in each module. Please refer to our course policies to view the attendance and assessment criteria.
Upon completion of all modules required for this programme within a maximum duration of 3 years, participants will be awarded a digital certificate.
Calculate Programme Fee
Fee Table
| COMPANY-SPONSORED | |||
|
PARTICIPANT PROFILE |
SELF-SPONSORED |
SME |
NON-SME |
|
Singapore Citizen < 40 years old Permanent Resident LTVP+
|
$523.20 (After SSG Funding 70%) |
$203.20 (After SSG Funding 70% |
$523.20 (After SSG Funding 70%) |
|
Singapore Citizen ≥ 40 years old |
$203.20 (After SSG Funding 70% |
$203.20 (After SSG Funding 70% |
$203.20 (After SSG Funding 70% |
|
International Participant |
$1,744 (No Funding) |
$1,744 (No Funding) |
$1,744 (No Funding) |
All prices include 9% GST
Please note that the programme fees are subject to change without prior notice.
Post Secondary Education Account (PSEA)
PSEA can be utilised for subsidised programmes eligible for SkillsFuture Credit support. Click here to find out more.
Self Sponsored
SkillsFuture Credit
Singapore Citizens aged 25 and above may use their SkillsFuture Credits to pay for the course fees. The credits may be used on top of existing course fee funding.
This is only applicable to self-sponsored participants. Application to utilise SkillsFuture Credits can be submitted when making payment for the course via the SMU Academy TMS Portal, and can only be made within 60 days of course start date.
Please click here for more information on the SkillsFuture Credit. For help in submitting an SFC claim, you may wish to refer to our step-by-step guide on claiming SkillsFuture Credits (Individual).Workfare Skills Support Scheme
From 1 July 2023, the Workfare Skills Support (WSS) scheme has been enhanced. Please click here for more details.
Company Sponsored
Enhanced Training Support for SMEs (ETSS)
- Organisation must be registered or incorporated in Singapore
- Employment size of not more than 200 or with annual sales turnover of not more than $100 million
- Trainees must be hired in accordance with the Employment Act and fully sponsored by their employers for the course
- Trainees must be Singapore Citizens or Singapore Permanent Residents
- Trainees must not be a full-time national serviceman
- Trainees are eligible for ETSS funding only if their company's SME status is approved prior to the course commencement date. To verify your SME's status, please click here.
Please click here for more information on ETSS.
Absentee Payroll
Companies who sponsor their employees for the course may apply for Absentee Payroll here. For more information, please refer to:
AP Guide (Non-SME Companies)
Declaration Guide (SME Companies)
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