NUS
 
ISS
 

Text Processing using Machine Learning

Overview

Reference No TGS-2020001446
Part of Graduate Certificate in Practical Language Processing
Duration 4 days
Course Time 9.00am - 5.00pm
Enquiry Please contact ask-iss@nus.edu.sg for more details.

The landscape of Natural Language Processing (NLP) has undergone a significant transformation since the emergence of Deep Learning and Large Language Models.

Deep Learning, a subfield of machine learning, has revolutionised NLP by enabling models to learn and understand language patterns and structures more effectively, leading to advancements in various NLP tasks such as semantic parsing, sentiment analysis, machine translation, and question-answering systems. The development of Large Language Models, such as OpenAI's GPT-3, has further propelled NLP capabilities by training models on massive amounts of data, enabling them to generate coherent and contextually relevant text. These models have shown remarkable proficiency in language understanding, text generation, and even performing creative writing tasks.

The combination of Deep Learning and Large Language Models has not only propelled the field towards more sophisticated and powerful language processing capabilities but has also opened up new possibilities for applications in fields like healthcare, customer service, and language translation.

This course is designed to meet the pressing demand for expertise in cutting-edge language technologies. From classic DNN models to the revolutionary Transformer architecture, from transfer learning to Large Language Models (LLMs), this course demystifies the complexities of language processing and the intricacies of deep learning, ensuring you're equipped to meet the demands of the data-driven future to harness the transformative power of Generative AI (GenAI).

Upcoming Classes

Class 1 23 Aug 2025 to 13 Sep 2025 (Full Time)

Duration: 4 days

When:
Aug:
23(Sat), 30(Sat)
Sep:
06(Sat), 13(Sat)
Time:
09:00am to 05:00pm



Course Content

  • Classic DNN Models: Dive into the fundamentals of Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) networks. Understand their applications in language tasks and learn how to build recognition models.
  • Attention & Transformer: Uncover the secrets of attention mechanisms and the revolutionary Transformer architecture.
  • Sequence-to-Sequence & Encoder-Decoder Framework: Explore the magic behind sequence-to-sequence models and how they enable tasks like machine translation and summarisation.
  • Transfer Learning with Pretrained LLMs: Get hands-on experience with BERT, GPT, and T5. Learn how to leverage their pretraining for downstream tasks.
  • Fine-Tuning LLMs (Bloom/LLaMa/Gemma): Master the art of fine-tuning large language models. Understand model tuning with prompts and achieve parameter-efficient fine-tuning.

You will gain practical experience through scenario-based case studies and hands-on sessions using popular libraries such as NLTK, skLearn, Gensim, spaCy, and LLM-based toolkits.

This course is part of the Artificial IntelligenceData Science and Graduate Certificate in Practical Language Processing Series offered by NUS-ISS.

Key Takeaways

  • Gain proficiency in classic DNN models, including CNNs, RNNs, LSTMs, and GRUs, setting the stage for advanced language processing.
  • Navigate cutting-edge frameworks like sequence-to-sequence models and transformer architectures.
  • Explore the true potential of Large Language Models through fine-tuning LLMs like Bloom and LLaMa, and probing advanced topics like emergent phenomena, hallucination, and Retrieval Augmented Generation.



    Who Should Attend

    • Data Scientists & Engineers: Enhance your skills in language processing with the latest deep learning techniques.
    • Analysts & Researchers: Learn to use advanced tools for in-depth analysis of text data.
    • Technology Enthusiasts: Stay at the forefront of technology by exploring the limitless possibilities of language processing with DL and LLMs. 



    Prerequisites

  • Possess foundational knowledge in text processing and predictive modelling with text data (at the level of Text Analytics course offered by NUS-ISS).
  • Strong programming skills using Python and familiar with packages like Numpy, Pandas and Scikit-Learn.
  • Well-versed with Anaconda, Jupyter Notebooks, Google Colab and Github.



  • Course Logistics

    • No Printed Materials: Course materials are accessed digitally. Do kindly note that no printed copies of course materials will be issued.
    • Device Requirements: Bring an internet-enabled device (laptop, tablet, etc) with power chargers to access and download course materials.
    If you are bringing a laptop, kindly refer to the table below for the recommended tech specs:

     

     

    Minimum

    Recommended

    Operating Systems

    • Windows 7 above
    • Mac OS

    Laptop running the latest
    version of either Windows or
    Mac OS

    System Type

    32-bit

    64-bit

    Memory

    8 GB RAM

    16+ GB RAM

    Hard Drive

    256 GB disk size

     

    Others

    • An internet connection – broadband wired or wireless
    • Installation permissions (non-company laptops)
    • Keyboard
    • Mouse/Trackpad
    • Display
    • Power adapter (laptop battery might run out)

    DirectX 10 graphics card for graphics hardware acceleration

     
     



    Fees & Subsidies

    Fees for 2024
      Full Fee Singaporeans & PRs
    (self-sponsored)
    Full course fee S$3600 S$3600
    ISS Subsidy  - (S$360)
    Nett course fee S$3600 S$3240
    9% GST on nett course fee S$324 S$291.60
    Total nett course fee payable, including GST S$3924 S$3531.60
    Note:
    1. All fees and subsidies are valid from January 2024, unless otherwise advised.
    2. All self-sponsored Singaporeans aged 25 and above can use their SkillsFuture Credit to pay for course fees. For more information about SkillsFuture Credit, click here.
    3. From 1st January 2024, the GST will be increased to 9%.



    loading

    Certificate

    Certificate of Completion
    Participants have to meet a minimum attendance rate of 75% and are required to pass the assessment to be issued a Certificate of Completion.



    Join Us

    Elevate your text processing capabilities. Register now to harness the potential of DL and LLMs.




    Preparing for Your Course

    NUS-ISS Course Registration Terms and Conditions

    Find out more.

    NUS-ISS and Learner’s Commitment and Responsibilities

    Find out more.

    WIFI Access

    WIFI access will be made available to participants.

    Venue

    NUS-ISS
    25 Heng Mui Keng Terrace
    Singapore 119615

    Click HERE for directions to NUS-ISS

    In the event of a change of venue, participants are advised to refer to the acceptance email sent one week prior to the commencement date.

    Course Confirmation

    All classes are subject to confirmation and NUS-ISS will send an acceptance email to participants one week prior to the commencement date. Confirmed registrants are to attend and complete all lectures, class exercises, workshops and assessments (where applicable). Additionally, all responses to feedbacks and surveys conducted by NUS-ISS and its partners must be submitted. All training and assessments will be delivered as described in the course webpage.

    General Enquiry

    Please feel free to write to ask-iss@nus.edu.sg if you have any enquiry or feedback.




    Course Resources

    Develop your Career in the Following
    Training Roadmap(s)

    Please click on the discipline(s) to view the training roadmap of related courses to assess your training needs and goals.

    Data Science

    Driving business decisions using insights from Data

    Read More Data Science
    Artificial Intelligence

    Advance your business by harnessing artificial intelligence (AI) and deep machine learning

    Read More Artificial Intelligence

    You Might be Interested in...

    A+
    A-
    Scrolltop
    More than one Google Analytics scripts are registered. Please verify your pages and templates.