Advanced Diploma in Machine Learning

Offered by School of InfoComm Technology

Post-Diploma / 1.5 years

Note: 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.
Course Information
Learning Outcomes
Course Schedule
Lesson Plan
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.

 

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

Day: Mon and Thur
Time: 6pm to 10pm
Delivery Mode: F2F & HBL (Sync)
Venue: TBC

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)
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)
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
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.

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

Note:

  • The fees below are per semester.
  • The fees below are inclusive of GST.
  • The fees below are determined based on prevailing funding policies and subject to review and revision.

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 f​or 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 at www.skillsfuture.sg/credit.

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.

Refer here for full table of course fees throughout the semesters.

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.

Applicants / Eligibility Fees
Singapore Citizens (SC) below the age of 40 years$546.50
Singapore Citizens (SC) aged 40 and above$364.34
Singapore Permanent Residents (PR)$1457.34
Enhanced Training Support for SME Scheme (for SC & SPR)$376.26
Others (and Repeat Students)$3643.36