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Short Course

Chat Models and Natural Language Processing

Course ScheduleDuration (days)Mode of DeliveryTGS Reference Number
17 and 18 November 2025 | 9 am - 5.30 pm2Classroom LearningTGS-2025056108

Course Overview

This course provides a basic introduction to chat models and natural language processing (NLP), focusing on state-of-the-art techniques, architectures, and Artificial Intelligence (AI) applications. Students will learn about various NLP tasks, such as sentiment analysis, named entity recognition, and machine translation. They will also explore chatbot development using deep learning-based methods, with a focus on popular architectures like Transformers and GPT. The course will include hands-on exercises using popular NLP frameworks and libraries.

Course Objectives

By the end of the course, students will be able to:

  • Understand the core concepts and techniques in NLP.
  • Develop a solid understanding of state-of-the-art NLP architectures and methods.
  • Gain hands-on experience in building chatbots and NLP applications using popular frameworks and libraries

Prerequisites

Basic programming skills in Python.

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This programme is part of

  • Professional Certificate in ChatGPT, Advanced Chat Models, and Generative AI

    • Funding Available

  • SkillsFuture Funding
  • Enhanced Training Support
  • SkillsFuture Credit
  • SFEC
  • UTAP
  • PSEA

    • Testimonials


    Prof Low was extremely good in explaining complex information to simpler content for everyone to understand.

    Alicia Aw

    This course is what I am looking for to equip me with the technical knowledge and theoretical know how to perform my own model training. Looking forward for similar courses. The size of the class is just nice, which makes less intimidating to ask questions.

    Foo Chuan Wen, Jenson

    This course provided a great exposure to NLP models and an in-depth understanding of training.

    Karthik Venkataraman

    Quite useful.

    Sean Foo Suan An

    Instructors


    Dr Ai Xin

    Dr Ai Xin

    Dr Ai Xin is currently a Lecturer with the School of Computing at the National University of Singapore (NUS). She has many years’ experience teaching Artificial Intelligence and Data Science courses such as machine learning, deep learning and data mining. She graduated from NUS with a PhD degree in Electrical and Computer Engineering. Her research focused on Game Theoretical Modelling, Optimization Methods, Algorithm Design and Wireless Networks. She worked at BHP Billiton Marketing Asia for eight years, gaining extensive industry experience in various functions such as risk management, supply chain management, and sales and marketing planning.

    Dr Edmund Low

    Dr Edmund Low

    Dr Edmund Low is currently Senior Lecturer with the NUS College at the National University of Singapore. He has nearly 20 years of academic and professional experience in the use of data-driven tools to answer questions in public health and the environment. His past projects include applying AI techniques and machine learning models for environmental modelling and impact assessment. He currently heads the quantitative reasoning domain at USP, and teaches courses on statistical methods, data science and machine learning. As an educator, Edmund is a multiple recipient of both the USP Teaching Excellence Award, as well as the NUS Annual Teaching Excellence Award. Edmund holds a PhD in Environmental Engineering from Yale University.

    Course Fees


    Singapore Citizens Singapore PRs Enhanced Training Support for SMEs International Participants
    39 years old
    or younger    		 40 years old
    or older
    Full Course Fee S$2,100.00 S$2,100.00 S$2,100.00 S$2,100.00 S$2,100.00
    9% GST on Full Course Fee S$189.00 S$189.00 S$189.00 S$189.00 S$189.00
    SSG Funding S$1,050.00 S$1,050.00 S$1,050.00 S$1,050.00 -
    Additional Funding if Eligible under Various Schemes - S$420.00 - S$420.00 -
    Total Nett Course Fee Payable, including GST S$1,239.00 S$819.00 S$1,239.00 S$819.00 S$2,289.00
    1. Total nett course fee payable, including GST, after additional funding from various funding schemes.
    2. Participants must fulfill at least 75% attendance and pass all assessment components to be eligible for SSG funding.
    3. The applicable funding is for Singaporeans and Singapore PRs. This is subject to change and in accordance with SSG funding guidelines as of course commencement.