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Practice Module for Practical Language Processing

Overview

Part of Graduate Certificate in Practical Language Processing
Duration 10 days
Course Time
Enquiry Please contact ask-iss@nus.edu.sg for more details.

The goal of the Graduate Certificate in “Practical Language Processing” (PLP) is to teach participants the skills and industry best practices for processing language data in business scenarios.

It is targeted at professionals such as data scientists, machine learning engineers, AI engineers, and chatbot developers, who wish to gain specialised knowledge required for building automated solutions or systems for finding insights in text data, interacting with user with natural language, etc.  This certificate consists of four component courses and a practice module.

The main aim of the practice module is for the students to assimilate the knowledge gained through the four component courses and to be able to apply them in a holistic manner to solve real-world problems using language data, especially textual data. The practice module consists of two parts i.e.; a Practice Project and an Examination

Objective

The objective of the practice module is twofold:

  • Firstly, to expose participants to real world problems so that they may practice the use of the skills they have learned during the component courses in a holistic manner.
  • Secondly, to enable participants to demonstrate their proficiency across all of the skills that they have learned in the course modules and hence obtain a grade at the Graduate Certificate Level.



  • Intended Audience

    This practice module is targeted at participants who wish to complete the certification process for the Graduate Certificate in “Practical Language Processing”.




    Prerequisites

    Participants must have successfully obtained a competent score  (or have been exempted from) the four component courses for the Graduate Certificate in Practical Language Processing as listed in the introduction to the Graduate Certificate page.




    Components

    There are two parts to the Practice Module.

    1. Practice Project: Participants will need to undertake one or more projects to gain practical experience and demonstrate their understanding and mastery of the skills taught in the four component courses. The practice project will require each participant to expend an estimated 10 man-days of effort. These days are not expected to be contiguous and may stretch over many weeks. These projects may be conducted by individual participants or in teams depending on the nature of the project requirements.
    2. Examination: Each participant is required to sit for an examination on a stipulated date and time.
    3. The overall grade for the participant will be based on the Practice project and Examination.

          Typical example of projects to be undertaken

    1. Problem description:

    A restaurant would like to have an additional channel other than emails, phone calls, and web forms to interact with their customers, and be available 7 days a week, 24 hours a day. They want this channel to be able to converse with the customers, identify and handle what the customers want to do, such as answering common customer queries, guiding the customers through reservation service, and receiving and forwarding customer feedbacks to respective staffs. This requires a conversational UI that can interpret the customer’s input correctly and produce appropriate response based on the situation, as well as back ends that perform sentiment analysis, question answering, topic discovery, etc. using appropriate language processing techniques.

    Deliverables and success criteria:

    • The overall system is to solve a business problem using language data
    • The system must have a conversational user interface, either speech or text.
    • The system should perform at least two text mining tasks, e.g. text classification, text clustering, topic modelling, information extraction, collocation detection, sequence labelling etc.
    • The system must have a component performing sentiment mining.
    • At least one of the above task must be implemented using deep neural network.

     




    Application (For Stackable Students)

    Semester 1 (Jul to Nov) Semester 2 (Jan to May)
    Application* 15 Apr to 15 Jun 15 Oct to 15 Dec
    Payment Deadline 30 Jun 31 Dec
    Briefing First two weeks of Jul First two weeks of Jan
    * Eligible participants will be contacted 1 week after application closure.

    Note:
    • Participants are only allowed to take the practice module after completing all courses in the Grad Cert.
    • Participants who wish to take the practice module concurrently in the same semester with the courses in the same Grad Cert must write to ask-iss@nus.edu.sg citing reasons by the application deadline. Email requests received after the deadline will not be considered. Requests will be reviewed after the deadline and approved on a case-by-case basis.
    • Participants who miss the application window will have to apply for the practice module in the next semester.
    • Participants who do not attend the briefing will be withdrawn from the practice module.
    Apply Here



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