Skip Ribbon Commands
Skip to main content
Sign In
Advance Your Career
NGEE ANN 
Learn More
Learn all you need to know about machine learning, deep learning, & AI and see how they add intelligence to engineering systems and processes.
NGEE ANN 
Apply Skills
Acquire practical coding skills and knowledge for implementing AI-enabled engineering systems and processes across different engineering domains.
NGEE ANN 
Get Hired
Gain an edge and boost your employability by adding to your engineering expertise an AI dimension that is future-proof as the economy transforms and becomes highly automated and digitalised
Course Information At A Glance
Course Date April 2020
Class Time 6:30pm – 9:30pm, Mon, Wed & Thu
Application Period17 Jan to   8 Mar 2020
Application Outcome:   24 Mar 2020
Course Fees For more information, please click here.
SkillsFuture Credit Eligible. For more details, please click here.
Venue Ngee Ann Polytechnic
Assessment(s) Quizzes, Projects and Continuous Assessments


Ngee Ann Polytechnic reserves the right to reschedule/ cancel any programme, modify the fees and amend information without prior notice.

For more information, please clickhere.
For more information, please clickhere.
For more information, please clickhere.
For more information, please clickhere.
For more information, please clickhere.
For more information, please clickhere.
Eligible. For more details, please click here.
Eligible. For more details, please click here.
Eligible. For more details, please click here.
Eligible. For more details, please click here.
Eligible. For more details, please click here.
Eligible. For more details, please click here.
Eligible. For more details, please click here.
Quizzes, Projects, Continuous Assessments and Final Examination
Quizzes, Projects, Continuous Assessments and Final Examination
Quizzes, Projects, Continuous Assessments and Final Examination
What You Will Learn
You are required to complete the following modules within a two-year validity period to be awarded the Specialist Diploma qualification.
Data Handling and Information Visualization

This module covers data importing, cleaning, interpreting and visualizing. Participants will learn advanced methods with Tableau software. This module deals with methods used to manage and analyze real world large datasets and present the results in visually understandable way. It helps to solve the complex engineering challenges present in the data. In this module, participants will be learning new techniques and tools in Tableau to become more resourceful for the engineering industry.

Machine Learning Systems for Engineers

This course will help participants to apply AI techniques to their fields in order to get accurate solutions. This module covers the basics of artificial intelligence, machine learning and deep learning. It deals with different types of learning techniques like: supervised, unsupervised and reinforced. The concept of features, data handling, training and testing will be explained. It focuses more on the theory, design and applications of artificial neural networks (ANN).

Artificial Intelligence Real-World Project 1

In this module, various data mining and data analysis methods will be used to carry out the project. Each participant will do hands-on project by applying what he (or she) has learned related to Data Science and Machine Learning.

Machine Learning Algorithm Development

The framework for automated detection system is explained. This course deals with the working principle of various classifiers like support vector machine, decision tree, k-nearest neighbor, probabilistic neural network and k-means clustering. The techniques to develop the robust classification models and fine-tuning of classifiers will be discussed.

Deep Learning Application in Engineering

This module covers fundamentals of deep learning and various DL modelling methods, including convolutional neural network (CNN), long short-term memory (LSTM),autoencoders, Tensorflow framework, Optimization techniques and validation techniques.

Artificial Intelligence Real-World Project 2

In this module, various data mining algorithms and deep learning solutions will be implemented for an industry problem. Each participant will do hands-on project using Python/Tensorflow by applying what he (or she) has learned related to DL and ML algorithms.



Entry Requirements

Applicants with any of the following qualifications are invited to apply for the course:

  •  A recognised local polytechnic Diploma or a Degree in any engineering/IT discipline.


Recognition of Prior Learning (RPL)

Applicants who do not meet the entry requirements may be considered for admission to the course based on evidence of at least 5 years of relevant working experience or supporting evidence of competency readiness. Suitable applicants who are shortlisted may have to go through an interview and/or entrance test. The Polytechnic reserves the right to shortlist and admit applicants.

For more information about Recognition of Prior Learning, please click here.