Machine Learning For Humans: How to think like a Data Scientist | SGInnovate



Machine Learning For Humans: How to think like a Data Scientist

Presented by SGInnovate & Ngee Ann Polytechnic

“Machine Learning for Humans” seeks to develop the cognitive capacities of participants, to help them think like a Data Scientist. These include learning to frame problem statements using the lens of Data Science, understanding the data requirements, and designing and selecting the most appropriate Machine Learning models that can be used to address various problem statements. Upon completion of the course, participants will receive a Certificate of Completion issued by Ngee Ann Polytechnic.

The programme is designed for a wide range of professionals who are keen to understand how Machine Learning is changing and disrupting businesses. This course will be of interest for professionals who deal with a lot of data on a day-to-day basis and wish to understand how data can be leveraged to make smarter business decisions.

This fully online workshop comprises of eight chapters with 24 modules, covering a broad spectrum of topics from data processing to Machine Learning techniques. The concept within each module is delivered through an engaging visual presentation using animation and motion graphics, delivered by well-known Data Scientists and Kaggle Experts. At the end of each module, participants will apply their knowledge and understanding to answer questions based on real-world scenarios. This will allow participants to pinpoint gaps in their understanding and seek clarification.

Workshop Overview:
In this course, participants will learn:

  • The rise of a data-driven world
  • How to conduct a Machine Learning project
  • How machines predict values
  • How machines predict categories
  • How machines predict relationships
  • Advanced modelling techniques
  • About key considerations when machines learn


  • A passion for Machine Learning

Course Fee: $695.50 (after GST)

SSG Funding administered by Ngee Ann Polytechnic

Topics: Artificial Intelligence / Deep Learning / Machine Learning / Robotics