WSQ—Applied Machine Learning (SF) (Synchronous and Asynchronous E-learning)

This course will provide learners to design and create machine learning systems that can integrate, make sense of, and act upon varied sensory information sources, using domain knowledge followed in respective industries. Learners will gain skills to build knowledge-based intelligent software applications using machine reasoning techniques and computer programming. This course will get the learners to be familiar with concepts of machine learning and data mining for predictive analytics. Learners learn what are the algorithms available, what they do, and how to choose the best one and apply it to their data. Learners will learn to make predictions with the output of the model and compare them across different models. Learners will be able to apply these concepts in business application and other fields by studying patterns in the data and using them to make predictions.

WSQ—Applied Machine Learning (SF) (Synchronous and Asynchronous E-learning)

Key Facts 

  • Course duration: Please refer to the details below

  • Intake dates: Start in January, April, July, or October

  • Total training fee: S$1,950

  • Funding: Up to 90% of funding is available. Please see the criteria below
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This course intends to bridge the gap between learner’s conceptual knowledge, professional skills, and practical capabilities:

  • In analysing appropriate data sets and data transformation methods for analysis. Learners will gain a practical understanding of the design, analysis, and implementation of machine learning and reasoning systems.
  • In developing scalable data pipelines to extract, load, and integrate unstructured data from various sources.
  • In troubleshooting bugs during deployment and creating bug fixes to address issues.

 

Please choose one of the following options:

1

4.5 days, 9 AM - 6 PM (Monday - Friday). 9 hours/day *4 days + 3 hours of Assessment.

2

2 weekends (Saturday and Sunday) + 3 hours of Assessment. 

3

14.5 days - 2 weeks, 7 PM - 10 PM (Monday - Friday). 

4

Customized upon corporate request. 

5

3 months, 1 session/week, 3 hours/session.

At the end of the course, learners will be able to:

1) Analyse machine learning techniques and business driver applications.

2) Analyse and prepare data for machine reasoning systems with relevant techniques.

3) Analyse, organize, and represent knowledge for machine reasoning systems. 

4) Analyse components, and techniques in knowledge-based systems to make inferences. 

5) Analyse characteristics to deduce logic resulting from applying reasoning system architectural algorithms to infer new inferences.

6) Examine the uncertainty issues, requirements, and explainability of machine learning systems. 

7) Analyse characteristics and results in the evaluation of certainty factor and uncertainty handling techniques.

8) Evaluate methods, models, and techniques of contemporary machine reasoning systems. 

9) Design and create artificial intelligence machine reasoning systems considering ethical issues.

 

The expected skills and knowledge for this course are as follows: 

  • Be able to listen, speak, read, and write English proficiently in a clear and confident manner; or provide documentary evidence of Workplace Literacy (WPL) Level 6 literacy skills in the Employability Skills WSQ framework;
  • Be keen to learn and participate in the learning activities in myriad learning settings such as online, laboratory, and blended learning, self-regulated learning contexts;
  • Be able to source and analyse relevant materials from the library, internet or online databases for the design and development of learning resources.

The entry requirements for this course are as follows:

  • Aged between 21 years old and above;
  • Minimum at least Learners must have ONE GCE “O” Level;
  • Able to read, listen and speak English at a proficiency level equivalent to Employability Skills System (ESS) Literacy Level 6;
  • Able to count with a proficiency equivalent to Employability Skills System (ESS) Numeracy Level 6;
  • Have basic computer literacy (Workplace Skills Series - Information and communication Technologies Skills).

Learners may be required to submit the relevant documents conforming to the pre-requisites for the course as stated above where necessary, if they do not meet the pre-requisites.

If the learner does not meet the entry requirement, the learner would be rejected from the course.

This accreditation enables Singaporeans and Singapore PR holders to get a part of their course funded by the SSG. Subjected to the eligibility and funding caps, the funding support is up to 90% for Singaporeans and up to 80% for Singapore PR holders.

Course Fee Funding for Self-sponsored Individuals (as of 30 Apr 2021)

Certifiable courses approved by SSG Course level Courses starting before 1 Jan 2022 Courses starting on or after 1 Jan 2022
Singapore Citizens (SCs) and Permanent Residents (PRs) (Self-sponsored individuals must be at least 21 years old) PMET Up to 50% of course fees, capped at $15 per hour Up to 50% of course fees
Non-PMET Up to 80% of course fees, capped at $17 per hour
SCs aged ≥ 40 years old (SkillsFuture Mid-career Enhanced Subsidy) PMET Up to 90% of course fees, capped at $50 per hour Up to 70% of course fees
Non-PMET Up to 90% of course fees, capped at $25 per hour

To receive funding from SSG, you must meet the following criteria:

  • Pass the course
  • Achieve a minimum of 75% of the attendance
  • Must not be barred from receiving government grants

  • You may also be eligible for the course fee (CF) funding at the current rates offered by SkillsFuture Singapore (SSG). For more details, please refer to this page.
  • Individuals aged 40 and above may also be eligible for the SkillsFuture Mid-Career Enhanced Subsidy (MCES). For more details, please refer to this page.
  • Singaporeans may also use their SkillsFuture credit to offset course fees payable. For more details, please refer to this page.

Modules

Analyse components, and tree-based techniques in knowledge-based systems to make inferences using legacy rules and new facts. Analyse techniques to draw new conclusions based on existing knowledge rules and new facts.

Analyse nearest-based reasoning model and apply system architectural algorithms to infer new inferences and to deduce logical results. Analyse characteristics and results in evaluation of advanced computational deductive reasoning techniques. Apply logical inference to deduce new conclusions.

Examine the uncertainty issues, requirements, and explainability for neural network machine learning systems.

Request More Information

Contact a programme advisor by calling
+65 6580 7700

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