Professional Certificate in Text Processing
Dates:
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
Prerequisites
At least a polytechnic diploma.
Course Conveners
(Click their photos to view their short biographies)
Assoc Prof Xavier Bresson
Assoc Prof Xavier Bresson
A/Prof Xavier Bresson is an international leader in the field of deep learning. Particularly, he co-pioneered a new machine learning technology called graph neural networks (GNN), which combines graph theory and neural network techniques to tackle complex data domains. He has organised several international conferences and tutorials on graph deep learning such as the recent UCLA’21 workshop on “Deep Learning and Combinatorial Optimization”, the MLSys’21 workshop on “Graph Neural Networks and Systems”, the UCLA’19 workshop on “New Deep Learning Techniques”, and the NeurIPS’17, CVPR’17 and SIAM’18 tutorials on “Geometric Deep Learning on Graphs and Manifolds”. He has been a speaker at the top machine learning conferences KDD’21, AAAI’21, ICLR’20 and ICML’20. In 2002, he co-developed with Turing award winner Yoshua Bengio a new class of expressive GNN. He has published more than 70 peer-reviewed articles, which have been cited 13,500+.
A/Prof Bresson has been the main lecturer of undergraduate deep learning courses since 2015 at EPFL, NTU and NUS, and has received outstanding evaluations for his teaching material and engaging style. He has also taught graduate courses on advanced deep learning at NUS and was a guest lecturer for Turing award winner Yann LeCun’s course at NYU. He has provided industrial short courses on AI and Deep Learning to Fortune 500 companies s.a. Deloitte, UnitedHealth and university alumni training centres at UCLA, EPFL, and NTU.
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 on teaching Artificial Intelligence and Data Science courses, e.g. machine learning, deep learning, data mining and etc.
She graduated from NUS with a PhD degree on Electrical and Computer Engineering. Her research focused on Game Theoretical Modelling, Optimization Methods, Algorithm Design and Wireless Networks.
She worked in BHP Billiton Marketing Asia for eight years and gained a lot of industry experience through different functions, e.g. risk management, supply chain management, sales and marketing planning and etc.
Dr Lek Hsiang Hui
Dr Lek Hsiang Hui
Dr Lek Hsiang Hui is a Specialist in Data Analytics, System Analysis and Development. He is an IT techie who is passionate about computer systems and technology.
He is involved in a few startups and is constantly looking into innovative IT solutions which can be translated to business ideas, with the recent one in the area of Big Data Analytics. He is also a mobile application developer and has produced more than 10 mobile apps.
Dr Lek has been teaching various undergraduate courses and executive courses in National University of Singapore (NUS) since 2006. Some of these modules include programming methodology, enterprise system analysis and development, and data mining. During this period, he has won a number of teaching awards such as NUS Annual Teaching Excellence Award (2015/16, 2016/17, 2017/18) and NUS Annual Teaching Excellence Honor Roll (2018/19), Faculty Teaching Excellence Award (2014/15, 2015/16, 2016/17), and Faculty Teaching Excellence Award Honor Roll (2017/18).
Dr Lek received his Doctor of Philosophy (Information Systems) from NUS in 2013. His research area is in Natural Language Processing (Sentiment Analysis). He graduated with a Bachelor’s Degree (1st class Honors) in Computer Engineering from NUS.
Industry Credentials
Shopping Malls (UOL, Marina Square, ION), Healthcare (SingHealth), Government (STB), various SMEs in F&B, Apparels, Cleaning Solutions, Photography, Health Products, Baby Products
Course Fees
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 soc-ace@nus.edu.sg
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)