This course provides essential knowledge and skills required to perform deep learning based text processing in common tasks encountered in industries. A combination of lectures, case studies, and workshops will be used to cover the application of DL techniques such as word-embedding, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) etc.
Course Description & Learning Outcomes
This course is part of the Artificial Intelligence, Data Science and Graduate Certificate in Practical Language Processing Series offered by NUS-ISS. At the end of the course, the participants will be able to: - Identify common tasks that industry has with textual data - Gain a practical understanding about advanced machine learning techniques for NLP - Acquire proficiency in implementing and creating NLP models for the above tasks - Learn how the fundamentals and cutting-edge machine learning approaches work together for performing text-related tasks in industry.
This is a course at advanced level, and focuses on the application of deep learning techniques in text mining tasks. - Participants must successfully completed Text Analytics course offered by NUS-ISS. - Participants must have strong programming skills using Python, familiar with packages like Numpy, Pandas, Scikit-Learn, and well versed with Anaconda, Jupyter Notebook, and GitHub. - Participants must have sufficient background knowledge of machine learning and text mining, with experience building models from text data using common ML techniques(e.g. SVM, MLFF NN, etc.). - Participants must understand basic calculus to appreciate basic machine learning mathematics. - Participants must code /program/debug in the hands-on practical sessions.
- No printed copies of course materials are issued. - Participants must bring their internet-enabled computing device (laptops, tablet etc) with power charger to access and download course materials. - Participant must have administrator’s access right to install applications and libraries on the laptop. Registration close date: 23/01/2024
Date: 13 Feb 2024, Tuesday
Time: 12:00 AM - 12:00 AM (GMT +8:00) Kuala Lumpur, Singapore
Location: NUS-ISS, 25 Heng Mui Keng Terrace, 119615