Machine Learning For Practical Applications

Apply Machine Learning for practical business solutions and accelerate your competitive advantage.

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Course Dates



Course Duration


4 Months, Online
6-8 hours per week

Course Fee


Course Information Special group enrolment pricing

Applications close on 29 June 2022

Apply before 27 June 2022 and avail a tuition assistance of US$100. Use code NUS100TA while applying.

WhatsApp an Advisor on +65 8014 3066
Have questions? Our Advisor will assist you promptly.

Why Enrol for the Programme?

With an estimated global market value of $117.19 billion by 2027, Machine Learning presents immense potential to bring transformative changes across business sectors and industries. In an increasingly data-driven world where every user generates close to 2 Megabits per second (Mbps) of data, businesses could accelerate their competitive edge by leveraging machine learning solutions to process these data to obtain insights to make more accurate predictions and deliver innovative and strong business value.

Curated with a strong emphasis on real-world relevance to meet rapidly evolving industry needs and trends, the Machine Learning for Practical Applications programme offered by National University of Singapore’s School of Computing will provide you with deep conceptual knowledge of Machine Learning to deploy solutions to solve real-life problems and streamline core business processes to increase returns.


Machine Learning Market to Reach USD 117.19 Billion by 2027.


fastest growing

Machine Learning Engineer is the fastest growing job title in Southeast Asia.



More than 348,000 jobs list machine learning as a required skill worldwide (as of February 2022).


Who Is This Programme For?

The programme is designed for professionals, who want to:

  • Build Machine Learning capabilities for real-world applications
  • Acquire Machine Learning techniques for transition into Machine Learning roles
  • Gain the expertise to lead and solve real-world business problems with data model at the core

It is particularly applicable to major industries such as IT Product & Services, Banking and Financial Services, Consulting, Education, Healthcare, and Retail, across sectors and functions.

What Will This Programme Do For You?

  • Analyse advanced machine learning strategies and their applications
  • Create K-means and hierarchical clustering systems
  • Design function estimators, classifiers, and decision tree algorithms
  • Optimise artificial neural network-based functions by using normalisation for best setting of parameters
  • Address the challenges in implementing machine learning

Programme Highlights

130+ Video Lectures

18 Discussion Boards

1 Capstone Project

Programme Faculty



Senior Lecturer in AI and Machine Learning with the School of Computing (SoC) at the National University of Singapore (NUS)

Dr Amirhassan Monajemi is a Senior Lecturer in AI and Machine Learning with the School of Computing (SoC) at the National University of Singapore (NUS). Prior to SoC, he was a Senior Lecturer at NUS School of Continuing and Lifelong Education (SCALE) teaching AI and Data Science to adult learners. Before joining the NUS, he was with the Faculty of Computer Engineering, University of Isfahan, Iran, where he was serving as a professor of AI, Machine Learning, and Data Science... More info

Programme Modules

The programme comprises 12 modules and will be led by an NUS faculty expert with more than 20 years’ experience in the field.
  • Understand the history and definition of Machine Learning and explain its various approaches, methods, and tools for applications and implementation.

  • Understand how machines can learn and explain Machine Learning through the supervised learning, reinforcement learning, unsupervised learning patterns, and the concepts of underfitting and overfitting.

  • Install the RapidMiner Machine Learning platform to explore the libraries, functions, and operators in the RapidMiner environment.

  • Design and practise application and analysis of clustering systems, based on the unsupervised learning algorithms of K-means and Hierarchical Clustering.

  • Execute and explain the implementations of techniques of supervised learning algorithms of Linear and Logistic Regression and Decision Trees.

  • Apply the principles and applications of artificial neural networks in Machine Learning and design neural networks for various practical considerations.

  • Explain the principles of reinforcement learning and design customised recommendation systems for various applications.

  • Explain the types of deep neural networks – their applications and future trends – and design a deep learning system based on advanced models.

  • Identify and discuss the issues and impacts in implementing Machine Learning in areas of safety, diversity/inclusion, human rights, and values.

  • Toward the end of the programme, you will demonstrate your newly gained machine learning skills by applying what you learned to build a simulation of real-life projects.



Upon successful completion of the programme, participants will be awarded a verified digital certificate by NUS School of Computing.

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Early registrations are encouraged. Seats fill up quickly!