Introduction
This course provides a comprehensive introduction to Natural Language Processing (NLP), guiding learners from foundational principles to cutting-edge technologies. It emphasizes hands-on experience, as learners apply advanced NLP methods—including attention-based deep learning models—to practical projects such as machine translation and text summarization using real world datasets. With the rapid emergence of Artificial Intelligence-powered generative tools such as ChatGPT, NLP expertise is increasingly critical in research, development, and enterprise sectors. Equipped with essential skills in machine learning and deep learning, learners will be effectively prepared for careers in data science and artificial intelligence.As this micro-credential course is only offered in the FlexiMasters in Signal Processing and Machine Learning programme, learners are required to enrol into the programme and complete all required courses within this programme.
This course is credit-bearing (3 AU) and stackable to:
- Graduate Certificate in Signal Processing and Machine Learning (9 AU)
- FlexiMasters in Signal Processing and Machine Learning (15 AU)
- MSc in Signal Processing and Machine Learning (30 AU)
Learners will receive their Statement of Accomplishment (for a grade of D and above) or Certificate of Participation for this course—dependent upon their assessment performance.
This course is part of the FlexiMasters in Signal Processing and Machine Learning.
Note: Shortlisting will be conducted.
Course Availability
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Date(s): 10 Aug 2026 to 02 Jan 2027
Venue: Nanyang Technological University
Registration Closing Date: 13 Jul 2026
At the end of the course, learners will be able to:
- Identify the appropriateness of Natural Language Processing (NLP) preprocessing techniques in different contexts.
- Explain the mathematical derivations of traditional NLP methods such as term weighting schemes, feature extraction techniques, and topic modelling.
- Execute simple NLP tasks such as classification and clustering on small-scale problems and evaluate the algorithm performance.
- Implement NLP algorithms in Python programming language.
- Distinguish between the theoretical concepts of traditional and deep neural network-based NLP techniques.
- Formulate the construction of word embeddings and model training using deep neural network-based architecture such as Recurrent Neural Network (RNN), Seq2Seq, Attention mechanism, and transformers.
- Describe the working principles of pre-trained language models.
- Design NLP-based project as a team to solve a real-life application by employing any techniques learnt along with fine-tuning the model
Suitable for practicing engineers, hardware and software designers, data scientists, R & D managers, and industry planners who seek an understanding of current approaches and evolving directions for DSP and AI technologies. It is also intended for engineers and data scientists who anticipate future involvement in these areas.
As the micro-credential courses are only offered in this FlexiMasters in Signal Processing and Machine Learning programme, learners are required to enrol into this programme and complete all required courses within this programme.
Note: Shortlisting will be conducted.
Standard Course Fee: S$5615.68
| SSG Funding Support | BEFORE funding & GST | AFTER SSG funding (if eligible under various schemes) & 9% GST |
| Course Fee | Course Fee Payable | |
| Singapore Citizen (SC) and Permanent Resident (PR) (Up to 70% funding) | $5,152 | $1,684.70 |
| Enhanced Training Support for SMEs (ETSS) | $5,152 | $654.30 |
| Singapore Citizen aged ≥ 40 years old SkillsFuture Mid-career Enhanced Subsidy (MCES) (Up to 90% funding) | $5,152 | $654.30 |
- NTU/NIE alumni may utilise their $1,600 Alumni Course Credits for this course. Click here for more information.
- Learners can utilise their SkillsFuture Credits for this course.
- Singaporeans aged 40 years and above are able to use their SkillsFuture Credit (Mid-Career) top-up of $4,000 to offset the course fee after SSG funding.
- Real-time DSP Design & Applications (3 AU)
- Analytic & Ensemble Machine Learning (3 AU)
- Genetic Algorithms & Machine Learning (3 AU)
- Video Analysis and Processing (3 AU)
