Artificial Intelligence is one of the fastest growing areas in the industry. Businesses are rushing to leverage AI to do things differently, faster, and more efficiently. We are seeing a rise in the use of autonomous vehicles. We are able to build houses in a week instead of a year. If you are an ITE graduate who is seeking to upgrade and upskill, this AI course is for you. You will learn to develop chatbots, leverage on natural language processing technologies, undertake object recognition and use machine learning and deep learning algorithms to create solutions.
At least 3 ‘O’ Level passes
and 1 year of relevant working experience OR
GPA ≥ 2.0 OR
GPA ≥ 1.5 and 1 year of relevant working experience OR
GPA ≥ 3.5 OR
GPA ≥ 3.0 and 1 year of relevant working experience OR
|Higher Nitec in Technology/Services||GPA ≥ 2.0 and 1 year of relevant working experience OR|
|Nitec in Technology/Services||GPA ≥ 3.5 and 1 year of relevant working experience OR|
|WSQ Qualification||Relevant WSQ Qualification with 1 year of relevant working experience and WSQ Workplace Literacy Statement of Attainment (SOA)(Level 6) AND Workplace Numeracy Statement of Attainment (SOA)(Level 6)|
|Without relevant academic qualifications||At least 2 years of relevant working experience|
Applicants who do not meet the entry requirements may be considered for admission to the course based on 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.
This subject introduces students to the fundamentals of computational thinking and their application in developing programming solutions to problems. Topics covered include programming concepts, simple data structures and programming techniques.
|CAA1C02||Coding & Development Project
This subject introduces students to coding principles and practices using an object-oriented approach. The subject also introduces the development of an IT application using the latest technologies. Topics covered include object and classes, composition, simple data structures, application architecture, design and development.
|CAA1C03||Database Application Development
This subject will introduce the fundamental concepts of relational database systems, the design methods specific to relational database, database manipulation using a database query language, and the techniques of implementing relational databases. It will also cover implementation of simple application to access relational database.
|CAA1C04||Data Visualisation & Analytics
This subject covers the data analytics lifecycle, including gathering, cleaning, processing and visualizing of data. Exploratory data analysis methods, descriptive and predictive analytics and the presentation of insights will also be covered.
|CAA1C05||Data Science Essentials
This subject equips with knowledge and skills in the emerging field of data science. It covers the data science life-cycle, history and context, as well as its landscape. Topics covered include data exploration and analysis techniques to discover new knowledge from data to aid data-driven decisions in an intelligent and informed way.
The subject covers graphing fundamentals, graphing properties and building dashboard for storytelling and reporting purposes using relevant statistical modelling and analysis techniques. Topics covered include the preparation of reports on data analysis to support managerial decision-making and applying the data storytelling framework and principles of data visualisation to enable business users to effectively communicate and narrate findings and insights relevant to business contexts.
|CAA1C07||Machine Learning for Developers
This subject introduces the fundamentals of machine learning principles and practices. It covers the concepts of supervised and unsupervised learning, and how the trained model can be deployed in an application.
|CAA1C08||Deep Learning & Object Recognition
This subject introduces students to the fundamental principles of deep learning and how it is applied to a collection of computer vision tasks to implement object recognition. It covers the concepts and architecture of convolutional neural networks such as the various layers within, and the hyperparameters involved, using available tools and libraries.
|CAA1C09||AI & Ethics
This subject provides students with insights on the usage and implications of AI in daily life. It touches on the risks of applying AI without a certain set of moral and ethical principles, and discusses issues brought about by machine learning, such as the four types of bias: sample bias, prejudice bias, measurement bias, and algorithm bias.
|CAA1C10||IoT Application Development
This subject covers the concepts of Distributed System Architecture like Service-Oriented Architecture, Representational State Transfer (REST) and Web Services, identification of technology and design principles for connected devices as well as prototyping techniques for developing web services.
This subject equips students with the skillsets for developing and deploying machine learning applications to a cloud platform maintained by cloud computing providers such as Amazon, Google, etc. It covers the storage of data and the use of application programme interfaces (APIs) and tools provided by the cloud platform.
|CAA1C12||Robotic Process Automation
This subject introduces students to the techniques of using an automation tool to automate tasks within a business process. It touches on the various use cases of robotic process automation (RPA) and provides a platform for students to creatively apply the concepts to different scenarios. It also discusses the challenges and limitations of RPA such as integration with unsupported third-party tools, security and governance, etc.
|CAA1C13||Virtual Assistant Development
This subject equips students with the knowledge and skills to apply the concepts of natural language processing to chatbot development using available tools and libraries.
This subject equips students with the knowledge and skills to apply the concepts of natural language processing to text classification using available tools and libraries. It also explores the use of text classification in applications such as sentiment analysis and spam detection.
This subject equips students with the knowledge and skills to apply the concepts of natural language processing to speech recognition using available tools and libraries.
Modes of Assessment
You will be assessed by coursework. There is no examination for this course.
At the entry level, the job positions are AI/Machine Learning Engineers and Software Engineer. Upon completing the course, graduates can look forward to role expansion in their work scope. They can also look forward to upgrading their knowledge and skills through further CET and executive programmes offered by local Institutes of Higher Learning.
|Intake Info||Application Period||Course Duration|
18 October 2021
03 May 2021 - 5 July 2021
8.30am – 6.00pm (Mon – Thu)
8.30am – 5.30pm (Fri)
Closed on Sat, Sun & Public Holidays
Due to Safe Management Measures, our office is currently closed.
Please call or email us your enquiry.
Thank you for your patience and understanding.
- Website: https://www.tp.edu.sg/tsa
Temasek SkillsFuture Academy
East Wing Block 1A,
Level 3, Unit 81
21 Tampines Ave 1
Temasek Polytechnic reserves the right to alter the course, modify the scale of fee, amend any other information or cancel course with low enrolment.