Professional Certificate in Machine Learning (Python)- Practical Concepts in Unsupervised Machine Learning


In the world of unsupervised learning, the data scientist does not guide, or supervise, the pattern discovery in a system by some prediction task, but instead uncovers hidden structure from unlabelled data. Unsupervised learning encompasses a variety of techniques in machine learning, from clustering to dimension reduction to natural language processing. These algorithms are closely aligned with what some call true artificial intelligence.


Run 1: 23, 27, 28 September 2019 [FULLY SUBSCRIBED - 30 PAX] [REGISTRATION CLOSED!]

Run 2: 7, 11, 12 October 2019 [NOW OPEN FOR REGISTRATION!]


In this course, participants learn the fundamentals of unsupervised learning and implement the essential algorithms using scikit-learn and scipy. They will also learn how to cluster, transform, visualize, and extract insights from unlabelled datasets. Participants end the programme with a capstone project by building either:

1)    a recommendation system to recommend popular youtube videos / music artists, OR
2)    exploring key factors in predicting success in crowdfunding projects, OR
3)    data driven housing valuation and trend analysis

Next Course Starts On23 Sep 2019 (Mon) See Full Schedule
Fee SGD1712.00* (as low as SGD113.60* after maximum funding) Learn more

2 weekday evenings and a full Saturday (15 hours)

Learning Objectives

By the end of this course, students will be able to:

•    Get introduced to unsupervised learning processes and algorithms.
•    Implement pattern recognition and segmentation models including multi-dimensional scaling and cluster analysis with SKLearn.
•    Explore concepts of natural language processing and text mining.

Work towards a bigger goal

This module is part of:

Professional Certificate in Machine Learning (Python)

Learn more

Who Should Attend

•    Aspiring data science professionals seeking to apply Python to real world data problems e.g. business intelligence analysts, data engineers
•    Anyone with an interest in learning about the fundamentals of data science programming!
•    Anyone whose work interfaces with data analysis who wants to learn key concepts, formulations, algorithms, and practical examples of what is possible in machine learning and artificial intelligence
•    Managers who need the vision and understanding of the many opportunities, costs, and likely performance hurdles in predictive modeling, especially as they pertain to large amounts of textual (or similar) data
•    Professionals looking for a deeper understanding and hands-on experience with SMU School of Information System's adjunct faculty and industry expert
•    No prior experience or background required in the field



•In-class group work

•Individual assessment

Fees and Funding

Total Nett programme fee (self-sponsored)

  • S$1,712 (after GST) for non-Singaporean Citizens and non-PRs
  • S$513.60 (after GST) for Singaporeans below 40 and Permanent Residents
  • S$193.60 (after GST) for Singaporeans age 40 and above
  • S$113.60 (after GST) for Singaporeans on Workfare Training Support (WTS)
  • SME sponsored Singaporeans/PRs (non-WTS) only need to pay only S$193.60 (after GST) per pax.

All self-sponsored Singaporeans aged 25 and above can use their $500 SkillsFuture Credit to offset the Total Nett Programme Fee.


SkillsFuture Series 
Course fee grant of 70% of course fees (excluding GST) for participants who are successfully enrolled by SMU into approved courses under the Programme. Participants must be Singapore Citizens or Singapore Permanent Residents.

SkillsFuture Mid-Career Enhanced Subsidy ("MCES")
Up to 90% of course fees for Singapore Citizens aged 40 years and above

Enhanced Training Support for SMEs ("ETSS")
Up to 90% of course fees; and 80% of basic hourly salary capped at $7.50/hr for local employees of SMEs

SkillsFuture Credit
Singapore Citizens aged 25 and above, and self-funding may use their SkillsFuture Credit (up to S$500) to defray part of the course fee. Please click User Guide on how to submit your claim. SkillsFuture Credit claims may be submitted by logging in via


SkillsFuture Course Code*: CRS-N-0048213


SkillsFuture Course Name*: Professional Certificate in Machine Learning (Python): Module 6 - Practical Concepts in Unsupervised Machine Learning


* Important Note: Participants claiming SkillsFuture credits should locate the course in Training Exchange using the Course Code / Name 


Start Date(s)
Intake Information


23 September 2019,

27 September 2019,

28 September 2019


7 October 2019,

11 October 2019,

12 October 2019

Speaker/Trainer Bio

Mr. Johnson Poh

Adjunct Faculty, Big Data and Analytics, SMU

Johnson is currently Adjunct Faculty at Singapore Management University's School of Information Systems and his focus areas include applied statistical computing, machine learning as well as big data tools and techniques. His industry experience spans across finance, consulting and government sectors, serving as Head of Data Science and Principal Data Scientist in DBS, Booz Allen Hamilton and Ministry of Defence respectively. An avid programmer and data enthusiast, Johnson enjoys developing apps and data products. Most recently, he was awarded first prize in Singapore’s largest coding competition, Hackathon@SG 2015 as well as the CapitaLand Data Challenge 2016. Johnson completed his bachelor’s degree at University of California, Berkeley, majoring in the subjects of Pure Mathematics, Statistics and Economics. He received his postgraduate degree in Statistics at Yale University.


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