Machine Learning for Humans (Asynchronous e-Learning)

Offered by School of InfoComm Technology

Online Course / 24 hours

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Course Information
Learning Outcomes
Course Schedule
Lesson Plan
Trainer Profile
Entry Requirements
Course Fees

Course Information

Machine Learning for Humans develops the cognitive capacities in participants on how to think like a data scientist. Through the lens of data science, participants will gain a broad overview on the history of data, how to conduct a machine learning project from start to finish, and the practical application of various machine learning models.

The course breaks down complex data processes and machine learning techniques / models through the visual power of animation, motion graphics, and storytelling.

The course is presented and explained by experienced data scientists. They will share the concepts used when designing machine learning models for real-world problems.

Participants will complete scenario-based assessments to ensure that they understand and can apply the techniques taught in the modules.

As this is an asynchronous learning, it allows participants to learn on their own schedule, within a certain timeframe. Lectures, readings, and other learning materials can be accessed and completed at any time during a given period.

Participants are not required to install any software on their computer for this course.

Learning Outcomes

This online course comprises 8 chapters with 20 modules, covering topics like how to conduct a machine learning project from start to finish to basic machine learning techniques. The concept within each module is delivered through an engaging visual presentation using animation and motion graphics, delivered by experienced data scientists.

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

  • Grasp the complexities of big data generated by people and systems
  • Learn the data requirements of a problem space
  • Discover the steps to conduct a machine learning project from start to finish
  • Recognise 10 basic machine learning techniques
  • Apply appropriate machine learning techniques for different types of business problems
  • Understand how to work effectively with a data expert to implement a machine learning solution
  • Comprehend the ethical considerations when implementing a machine learning solution


Duration: 24 hours
Delivery Mode: Asynchronous e-Learning / Self-pacing Online Course
Venue: Online

Ngee Ann Polytechnic reserves the right to reschedule / cancel any programme, modify the fees and amend information without prior notice.​


Lesson Plan

The Rise of a Data-Driven World

Learn about the history of data and how machine learning will revolutionise the way decisions are made on an organisational and global level.

Conduct a Machine Learning Project - [Part 1]

Discover the fundamental steps data scientists take to frame a problem statement, collect and prepare data and select or engineer features for a machine learning implementation.

Conduct a Machine Learning Project - [Part 2]

Understand how data scientists select, train and evaluate appropriate machine learning models, to achieve business objectives.

How Machines Predict Values

Follow the science behind linear regression to predict the future.

How Machines Predict Categories

Identify how machine learning is able to categorise data accurately, enabling companies to effectively profile a large customer database and provide the most relevant products and services.

How Machines Predict Relationships

Develop an understanding of data clusters and association rules and discover how machine learning creates differentials and associations with data

Advanced Modelling Techniques

Grasp concepts such as artificial neural networks and reinforcement learning and see how they have already begun to propel machines into the new age of artificial intelligence.

Key Considerations when Machines Learn

Define and navigate the moral and ethical grey areas when dealing with machine learning.

Trainer's Profile

Janet Uy

Janet Uy, Sr CSA Manager, Customer Engineering APAC (Data and AI), Microsoft

Ritchie Ng

Ritchie Ng, Chief Executive Officer, and Executive Director, Hessian Matrix

Elaine Liew

Elaine Liew, Data Scientist, GovTech Singapore

Kenneth Soo

Kenneth Soo, Co-Author, Numsense! Data Science for the Layman


This course provides a broad overview of the knowledge, skills and mental models that data scientists use to frame problem statements, select machine learning model​s and evaluate those models.

The course is estimated to take 20-30 hours to complete on average. Upon the successful completion of the course, participants will be awarded a certificate of completion from Ngee Ann Polytechnic.

Entry Requirements

Applicants with any of the following qualifications are invited to apply for the course:
  • Basic IT Literacy.
  • Participants need not have any prior knowledge in programming as this in an introductory course.

Course Fees

This is a complimentary course from NP.


Applicants / Eligibility Fees
Full Course Fee$702.00