Advanced Diploma in Machine Learning

Course Information

Learning Outcomes

Course Schedule

Lesson Plan

Trainer Profile

Certification

Entry Requirements

Course Fees

Course Information

Machine Learning has become the buzzword today, and is considered as the brain behind businesses that leverage on its immense capabilities of making informed decisions without the need for human intervention. With industries seeking to apply this branch of Artificial Intelligence into their systems, there is no better time than now to learn Machine Learning.

Note: Entry to this programme is via our Specialist Diploma in Data Analytics programme. Please apply via the Specialist Diploma in Data Analytics programme.

Learning Outcomes

Develop your own Machine Learning and Deep Learning models to build value-driven business solutions. This programme helps you to acquire the fundamental competencies to effectively perform data analysis on business data. You will also have the opportunity to further broaden your knowledge and skills in machine learning through hands-on implementation of machine learning and deep learning models.

Course
Schedule

Monday, 6:00 pm to 10:30 pm
Thursday, 6:00 pm to 10:30 pm

Course Start Date: TBC
Application Period: Entry to this programme is via our Specialist Diploma in Data Analytics (SDDA) programme. Please apply via the Specialist Diploma in Data Analytics (SDDA) programme.
Application Outcome: One month after application closes
Class Day(s): Mon and Thu
Time: 6pm to 10:30pm
Delivery Mode: F2F & HBL (Sync)
Venue: Ngee Ann Polytechnic/Online

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

Lesson Plan

Post-Diploma Certificate in Data Visualisation and Programming for Analytics (covered under SDDA programme)(TGS-2018500100 )

Module

Data Visualisation and Storytelling

This module discusses the principles and techniques for creating effective visualisations based on graphic design and perceptual psychology. Using widely adopted tools, you will apply these principles and techniques to create rich visualisations for analysis and presentation. You will learn visual analysis techniques to grasp pertinent information, as well as apply exploratory techniques to further derive key insights. Data storytelling and information graphics best practices will also be explored to allow learners to present data effectively and eloquently.

Module

Programming for Analytics

The aim of this module is to equip you with sufficient mastery of a programming language to perform operations and analysis. This module is suitable for learners with little or no programming background. The programming language taught is Python, which is fast becoming the world's most popular coding language due to its simplicity and flexibility. Its popularity also stems largely due to its wide range of applications in areas such as machine learning, network automation, and internet of things. The module highlights the syntactical and algorithmic aspect of programming to participants. You will be able to code using Python, from basic to complex algorithms progressively.

Post-Diploma Certificate in Data Wrangling and Descriptive Analytics (covered under SDDA programme)(TGS-2018500101 )

Module

Data Wrangling

The aim of this module is to equip you with the tools and skills sets to handle, clean and prepare large curated data sets for data analytics purposes. Participants of this module should minimally have basic programming knowledge and be able to understand and decipher simple syntaxes. The processed data sets will allow for meaningful statistical analysis, data modelling, and machine learning to be easily performed. Emphasis will be placed on the Extraction, Transformation, and Loading (ETL) of data sets. The use of relevant programming libraries for Missing and Time Series Data will also be explored. Learners will experience the process of exploratory data analysis, normalization of data and data distribution, which will be crucial for subsequent understanding of machine learning concepts and models.

Module

Descriptive Analytics

In this module, you will first be exposed to Descriptive Statistics concepts, delving into topics such as central tendency, normal distribution, measures of variability, variance and standard deviation. Following which you will be able to perform univariate, multivariate and correlation analysis in order to identify inherent patterns and derive key insights from business data. You will also create appropriate visualisation components using systems to gain insights from the data. These visualisation components will be synthesized into dashboards that can be readily adopted by users.

Post-Diploma Certificate in Machine Learning and Deep Learning (TGS-2023020208)

Module

Machine Learning

This module introduces the fundamentals of Machine Learning and its applications. You will be provided the essential context and background knowledge of Machine Learning. You will gain exposure to both supervised and unsupervised learning models such as Linear & Logistic Regression, Decision Tree, K-means Clustering and more. Using leading software and associated libraries, you will be able to implement and train Machine Learning models to address business challenges.

Module

Deep Learning

This module introduces the fundamentals of Deep Learning (DL) and its applications. You will be provided the essential context and background knowledge of Deep Learning, a subset of Machine Learning. You will explore Deep Learning models such as Neural Networks, Convolutional Neural Networks and Recurrent Neural Networks. There will be ample opportunities for you to experience the practical applications of these models in areas such as computer vision and natural-language processing. Adopting an experiential approach, you will implement and train the Deep Learning models using leading software and associated libraries.

Trainer's Profile

Mike Wee Toon Kim

Mike Wee is an experienced IT professional with over 20 years of expertise in deploying, supporting, and teaching enterprise systems. Throughout his career, he has worked with leading global companies such as BMW, Mercedes-Benz, and HP, leveraging his knowledge in enterprise computing to drive significant technological advancements and operational efficiencies.

Mike's key domain areas encompass a wide range of critical IT functions, including Enterprise Computing, Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), Analytics, Data Visualisation, Machine Learning, SAP, Salesforce, PowerBI, Tableau, and Python. His multifaceted skill set enables him to provide comprehensive solutions that meet the complex needs of modern businesses.

Educationally, Mike Wee holds a Master’s Degree in Business Administration (MBA) from Nanyang Business School, Nanyang Technological University (NTU), and a Master of Education (MEd) from the University of Adelaide. He also possesses a Bachelor’s Degree (Honors) in Mechanical Engineering from NTU. Complementing his academic achievements, Mike is certified in Advanced Certificate in Training and Assessment (ACTA) and is a certified Scrum Master, underscoring his commitment to continuous professional development and excellence in project management.

Mike's extensive experience and diverse skill set make him an asset in the IT field. His proficiency in cutting-edge technologies and enterprise systems has enabled numerous organisations to achieve their strategic goals through effective IT solutions and innovative approaches. Whether it's through enhancing operational capabilities or teaching the next generation of IT professionals, Mike consistently demonstrates a passion for technology and a dedication to excellence.

Certification

1 Advanced Diploma in Machine Learning

3 Certificates
*PDC 1 and 2 will be covered under Specialist Diploma in Data Analytics

6 Modules
*4 modules will be covered under Specialist Diploma in Data Analytics

Entry Requirements

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

Entry to the advanced diploma programme in Machine Learning is via our Specialist Diploma in Data Analytics programme. Please refer to our Specialist Diploma in Data Analytics programme page for more information.

Course Fees

Funding period for this course is from 1 Apr 2021 to 31 Dec 2024.

SkillsFuture Credit
This course is eligible for SkillsFuture Credit.

All Singaporeans aged 25 years and above can use their $500 SkillsFuture Credit from the government to pay for a wide range of courses. The credits can be used on top of existing course fee subsidies/funding. This is only applicable for self-sponsored applicants and must be applied via the SkillsFuture Portal. More details on the SkillsFuture Credit Claims will be advised upon admission into the course. Find out more about SkillsFuture Credit here.

Union Training Assistance Programme (UTAP)
NTUC members enjoy 50% unfunded course fee support for up to $250 each year (or up to $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.

Skills-Based Modular Courses (SBMCs) are bite-sized part-time courses for individuals to acquire new skills or deepen relevant skills, without the need to pursue a full diploma. Refer here for course fees for SBMCs.

GST Increment in 2024
Please note that the GST rate will be revised to 9% with effect from 1 Jan 2024, as such any payable course fees will be subjected to the new GST rate adjustments in 2024.

Note:

The fees below are determined based on prevailing funding policies and subject to review and revision.
Please refer to our FAQs for more information.
The fees below are per semester. For full course fees throughout the semesters, refer here.

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

Applicants / Eligibility	Fees
Singapore Citizens aged below 40	$556.14
Singapore Citizens aged 40 & above	$370.76
Singapore Citizens sponsored by SME	$382.88
Singapore PRs	$1522.80
Singapore PRs sponsored by SME	$394.80
Full course fee for SC (for repeat, deferred modules)	$3707.56
Full course fee (for repeat, deferred modules)	$3807.00