MACHINE LEARNING IN PYTHON
PROGRAMME OBJECTIVES
In this course, we will provide an introduction on machine learning and how to utilize machine learning models to solve business problems. We will teach you the steps necessary to create a successful machine learning application with Python. We will focus on the practical aspects of using machine learning models to solve business problems, evaluating the models and improving the model performance by tuning hyperparameters.
PROGRAMME HIGHLIGHTS
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| Welcome message from Dean of NUS SCALE |
Tutorials conducted over Zoom
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PRE-REQUISITES
We will provide some learning resources for students to pick up on python programming in week 1 but it will be much better if students have some basic Python programming background.
WHO SHOULD ATTEND
Undergraduate with good command of English
MODE OF DELIVERY
18 hours of synchronous lessons, tutorials and a final project presentation.
MODE OF ASSESSMENT
- Continuous Assessment: five individual coding exercises
- Final Assessment: one group project
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Dr AI Xin
Lecturer
School of Computing, NUS
Dr. Ai Xin is currently a Lecturer with the School of Computing at the National University of Singapore (NUS). She has many years’ experiences 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.
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CERTIFICATE OF COMPLETION
Successful participants who complete all requirements of the programme including passing the assessment will receive a certificate of completion issued by NUS SCALE.
A sample of the certificate of completion can be found here:
Participants from the project team with the highest score for the group project will also receive a commendation letter. A sample is shown below:
For programme enquiries, please contact us at sgprogrammes@nus.edu.sg