Professional Certificate in Text Processing

Natural Language Processing Fundamentals:
21 Nov 2023 | 9am-5.30pm | Online

Sentiment Analysis Fundamentals:
23 Nov, 24 Nov 2023 | 9am-5.30pm | Online

Deep Learning Fundamentals:
4 Dec, 5 Dec 2023 | 9am-5.30pm | Online

Advanced Deep Learning:
6 Dec, 7 Dec 2023 | 9am-5.30pm | Online

Duration: 7 Days

Course Overview

Text processing is essential for many applications that involve natural language data. It enables computers to understand and generate human language, extract valuable insights from text data, and automate various tasks that involve text analysis.

Why text processing is beneficial for organisations?

  • Improved efficiency: Text processing automates many tedious and time-consuming tasks such as data cleaning, tokenisation, and normalisation, which can significantly improve efficiency and save time.
  • Increased accuracy: Text processing algorithms can analyse large amounts of data much faster and with greater accuracy than humans, reducing errors and improving the quality of insights.
  • Enhanced understanding: Text processing techniques such as sentiment analysis, topic modelling, and named entity recognition can help identify patterns and insights that might not be immediately apparent from a surface-level reading of the text.
  • Better decision-making: Text processing can help organisations make better decisions by providing insights and identifying opportunities or risks that may have been missed otherwise.
  • Improved customer experience: Text processing can help organisations better understand their customers’ needs, preferences, and sentiment, enabling them to provide better customer service and personalised experiences.

Text processing can help organisations extract valuable insights and automate many time-consuming tasks, leading to better decision-making, increased efficiency, and improved customer experiences.

Course Objectives

This Professional Certificate will equip learners with the following competencies:

  • Provide theoretical foundations to Natural Language Processing (NLP) and build NLP applications using basic Machine Learning
  • Provide participants with the theoretical foundations to text mining, sentiment analysis and building sentiment classifiers using Machine Learning
  • Equip participants with the skills to mine data from the web and perform sentiment analysis on the data
  • Acquire basic knowledge of deep neural networks and understand the applications of deep learning and its implementation
  • Acquire basic skills on how to design and implement text classification, opinion mining systems and convolutional neural networks in Python using Tensorflow and Keras packages.
  • Articulate advanced deep learning models, their strengths and constraints, and their applications in different businesses
  • Have basic skills in using deep learning algorithms to implement simple Natural Learning Processing (NLP) systems.

Job Role Readiness

It will prepare learners in the following job roles to perform their responsibilities more effectively/ It will prepare learners for the following job roles:

  • Artificial intelligence/Machine Learning Executives and Engineers, Data Analysts

Who Should Attend

IT Professionals who plan to be AI / ML Engineers and Data Analysts


At least a polytechnic diploma.

Course Conveners

(Click their photos to view their short biographies)

ccccc Xavier Bresson

Assoc Prof Xavier BressonAssoc Prof Xavier Bresson

ddddd Ai Xin

Dr Ai XinDr Ai Xin

ddddd Lek Hsiang Hui

Dr Lek Hsiang HuiDr Lek Hsiang Hui

Course Fees

Singapore Citizens
39 years old or younger
40 years old or older
Singapore PRs
Enhanced Training Support for SMEs
International Participants

Total Nett Programme Fee Payable, Including GST, after additional funding from the various funding schemes

Participants must fulfill at least 75% attendance and pass all assessment components to be eligible for SSG funding.

To enquire, email

To register, click Register

Course Codes
Natural Language Processing Fundamentals: TGS- 2022012348 (Classroom Learning) / TGS-2022012353 (Synchronous e-learning)
Sentiment Analysis Fundamentals: TGS-2022012367 (Classroom Learning) / TGS-2022012367 (Synchronous e-learning)
Deep Learning Fundamentals: TGS-2022012786 (Classroom Learning) / TGS-2022012789 (Synchronous e-learning)
Advanced Deep Learning: TGS-2022012788 (Classroom Learning) / TGS- 2022012752 (Synchronous e-learning)