The benefit of learning together with your friend is that you keep each other accountable and have meaningful discussions about what you're learning.
Courtlyn
Promotion and Events Specialist
Apply Machine Learning for practical business solutions and accelerate your competitive advantage.
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TBD
4 Months, Online
6-8 hours per week
Our participants tell us that taking this programme together with their colleagues helps to share common language and accelerate impact.
We hope you find the same. Special pricing is available for groups.
The benefit of learning together with your friend is that you keep each other accountable and have meaningful discussions about what you're learning.
Courtlyn
Promotion and Events SpecialistApply 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.
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.
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).
The programme is designed for professionals, who want to:
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.
130+ Video Lectures
18 Discussion Boards
1 Capstone Project
DR AMIRHASSAN MONAJEMI
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. He was born in Isfahan, Iran. He studied his BSc and MSc in Computer Engineering at Isfahan University of Technology (IUT), and Shiraz University respectively. He got his PhD in computer engineering, pattern recognition and image processing, from the University of Bristol, Bristol, England, in 2005. His research interests include AI, Machine Learning, Machine Vision, IoT, Data Science, and their applications.
He has taught the artificial intelligence courses, including AI, Advanced AI, Expert Systems, Decision Support Systems, Neural Networks, and Cognitive Science since 2005 at both undergraduate and postgraduate levels. He was awarded the best university teacher of the province in 2012. He also has studied Learning Management Systems, E-Learning, and E-Learning for workplaces since 2007.
Dr. Monajemi has registered a few patents in the fields of AI, Machine Vision, and Signal Processing applications, including an AI and machine vision-based driver drowsiness detection system and a low power consuming spherical robot. He also has published more than a hundred research papers in peer-reviewed, indexed journals and international conferences (IEEE, Elsevier, Springer, and so on), and supervised several Data Science, IoT, and AI industrial projects in various scales, including Isfahan intelligent traffic system delivery and testing, and red light runners detection. He is experienced in different sub-domains of Artificial Intelligence and Machine Learning, from theory to practice, including Deep Learning, Logic, and Optimisation.
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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