Home > Courses > Applied Machine Learning - Part of Post Diploma Certificate in Data Preparation for Analytics with AI and Applied Machine Learning (SDAAD)

Applied Machine Learning - Part of Post Diploma Certificate in Data Preparation for Analytics with AI and Applied Machine Learning (SDAAD)

Name: Applied Machine Learning - Part of Post Diploma Certificate in Data Preparation for Analytics with AI and Applied Machine Learning (SDAAD)
Price: 2527.50 SGD

Training Provider: Ngee Ann Polytechnic

Course Reference: TGS-2026066158

S$2,528

About This Course

This module introduces the fundamentals of Machine Learning (ML) and its applications. Learners will receive the essential context and background knowledge of machine learning.

Learners will gain exposure to both supervised and unsupervised learning models such as Linear & Logistic Regression, Decision Tree, K-means Clustering, and more. Using industry-standard software platforms and associated libraries, learners will implement, train, and evaluate machine learning models. Learners will also be guided to evaluate model outputs, validate AI-supported explanations, and assess the suitability of machine learning models for organisational use cases.

The module adopts a hands-on approach, enabling learners to develop practical experience in model experimentation, optimisation, and interpretation for real-world applications.

Where appropriate, learners will apply introductory machine learning techniques within an integrated capstone-style project, allowing them to connect data analysis techniques such as data preparation and visualization with machine learning into a coherent end-to-end workflow.

What You'll Learn

On completion of this module, learners will have a solid grounding in the principles and applications of machine learning in organisations, and be able to apply common algorithms such as linear and logistic regression, decision trees and k-means clustering to real-world datasets. They will be able to select, train and fine-tune appropriate models using relevant software tools and libraries, and interpret model results to generate actionable, data-driven insights for business decision making.

Entry Requirements

Applicants must possess a diploma or higher qualification from local Polytechnics/Universities in any discipline or at least 1 year of relevant work experience.

Applicants with other equivalent qualifications will be considered case-by-case based on relevant work experience.

Other requirements:
•Learners enrolled into the course are expected to bring along their personal laptop
•Basic IT literacy, including operating system navigation, digital file management (creating, saving, organizing), and the use of standard productivity software (e.g., Microsoft Office, Google Workspace), is expected of participants.
•In addition, learners must be adept at using web browsers for research and communicating through email and video conferencing technologies (MS Teams).