Advanced Generative Text Artificial Intelligence
Specialised micro-credential for strong technical and theoretical foundation in Natural Language Processing and Large Language Models (LLMs), focusing on AI-driven text automation, business intelligence, and conversational AI. Earn a Specialist Certificate and stack it towards a degree in Applied Computing.
About Course
This specialised micro-credential equips learners with in-demand expertise in Natural Language Processing (NLP) and Large Language Models (LLMs), focusing on AI-driven text automation, business intelligence, and conversational AI.
Hands-on experience will be gained in developing and optimising NLP and LLM systems, covering key topics such as transformer architectures, attention mechanisms, embeddings, and transfer learning – essential for building scalable solutions like sentiment analysis engines, machine translation systems, chatbots, and automated content generation tools.
The micro-credential combines both theoretical frameworks and practical applications. Learners will develop skills in text preprocessing, tokenisation, embedding techniques, and model evaluation, with a focus on training classification and regression models, applying optimisation strategies, and using industry-standard evaluation metrics. Additionally, the course delves into LLMs, with an emphasis on transformer models and fine-tuning them for real-world applications such as chatbots and generative AI solutions.
Integrating online and in-person delivery, the micro-credential includes activities such as building an NLP application, developing preprocessing pipelines, training and evaluating models, and fine-tuning a GPT model. You will also create runnable notebook code to demonstrate model outputs. By the end, you will be equipped to design and implement practical NLP and LLM solutions for real-world use cases.
This micro-credential is part of the CSM Pathway in Applied Computing.
Skills you’ll gain
AI and Machine Learning
Data Science and Analytics
Programming and Software Development
Competency-based Education
Who Should Attend
Data scientists
Machine Learning engineers
Natural Language Processing specialists
Artificial Intelligence researchers
Individuals aiming to stack towards SIT's Bachelor of Science (Honours) in Applied Computing
Assumed Prior Knowledge
It is recommended that learners have at least one year of experience in Python programming, along with a foundational understanding of Machine Learning and Natural Language Processing.
Learning Outcomes
This micro-credential is predominantly delivered through a competency-based education (CBE) approach where learners acquire and demonstrate mastery of knowledge and skills that are directly relevant to job functions. This prepares them to be industry-ready where they can apply their newly acquired competencies to their work.
List of Competency Units
| Code | Competency Unit Title | Credits |
|---|---|---|
| ICT3506C | Natural Language Processing | 9 |
| ICT3507C | Large-Language Models (LLM) Architecture | 9 |
The above are competency units that constitute this micro-credential. Upon completion of the micro-credential, you will be able to:
- Develop and implement NLP and LLM-driven AI solutions by applying core methodologies in text processing, sentiment analysis, machine translation, chatbots, and automated text generation, ensuring scalability and real-world applicability
- Design, train and optimise machine learning and deep learning models for natural language understanding and generative AI, leveraging transformer architectures, attention mechanisms, embeddings, and transfer learning to enhance text-based applications
- Evaluate and fine-tune NLP and LLM models using industry-standard performance metrics, optimisation strategies, and emerging advancements, ensuring effective deployment and alignment with business and AI industry needs
Coaching for Success
During the course, you will have access to a team of qualified success coaches who can work with you on learning strategies or to develop a personalised learning plan. Through the success coaches, you can gain access to a wide range of resources and support services, and be empowered with the necessary tools to navigate your learning journey successfully.
Teaching Team
Christopher Teoh
Assistant Professor, DigiPen (Singapore)
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Anne Yu
Lecturer, DigiPen (Singapore)
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Course Details
Schedule
| Week | Learning Activity | Delivery and Time |
|---|---|---|
| 1 | Micro-credential and competency unit briefing | In-person Time TBC |
| 1 – 13 | Online content and video lessons Self-paced online laboratory sessions Self-directed learning | Asynchronous Online |
| 2 – 6 & 9 – 13 | Consultations | In-person Time TBC |
| 3 – 5 & 10 – 12 | Assignments | Online |
| 6 & 13 | Quizzes | Synchronous Online |
Certificate and Assessment
A Specialist Certificate in Advanced Generative Text Artificial Intelligence will be issued to learners who:
- Attend at least 75% of the course and
- Undertake and pass all credit bearing assessments
Assessment Plan
The learner will undertake a combination of quizzes, and a project during the course.
Fee Structure
The full fee for this course is S$9,868.86.
Note:
- A one-time, non-refundable matriculation fee of $54.50 will be collected before course commencement.
- The fee above includes GST. GST applies to individuals and Singapore-registered companies.
For Organisations
Interested in this course for your business or team?
Tailored to your organisation's specific training needs, we can help you design and develop custom courses to grow key talent and build specialist expertise to meet the future demands of work.
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