Machine learning

Every field of computing is being impacted by Machine Learning: software engineering, data analysis, and artificial intelligence.

Discover the machine learning models that interpret large amounts of data. Learn to utilize Python libraries to solve predictive problems (supervised learning) and data clustering problems (unsupervised learning). Study machine learning techniques such as multiple linear regressions (Ridge and Lasso), generalized linear models and classification, clustering and dimensionality reduction methods (SVM). Gain hands-on experience solving complex and simple real-world problems across a broad array of industries. This course concludes with an opportunity to test, try, and implement a small freelance coding assignment.


Learning Objectives

Upon successful completion of this course, students will be able to:

  • Describe how machine learning can be used to create models that interpret large amounts of data
  • Apply machine learning methods and algorithms in the context of real-world problems
  • Explore Machine Learning oriented practical applications of scientific libraries such as SciKit-Learn and TensorFlow
  • Identify how to formulate learning tasks as computational problems and the methods that are designed to solve these problems
  • Design and implement methods for problems in pattern recognition, system identification or predictive analysis
  • Complete a freelance coding assignment to demonstrate proficiency using Python programming language for Machine Learning



  • Introduction to Machine Learning
  • Playing with built-in datasets
  • Linear regression
  • Polynomial regression
  • Logistic regression
  • Support vector machines
  • Decision trees and random forests
  • K-nearest neighbours
  • Naive Bayes
  • Clustering models
  • Artificial neural networks and deep learning


Financial Assistance
Financial Assistance

This course is eligible for Ontario Student Assistance Program (OSAP) micro-credential funding. Find out if you are eligible.




Machine learning is complex, advanced level python course.  A foundational knowledge in Python, mathematics and statistics through prior course work or work experience is required.

Applies Towards the Following Certificates

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Feb 07, 2022 to Mar 18, 2022
Total Hours
Potential Discount

What to Expect

This is a self-directed course offered through the RoboGarden online learning platform. There are opportunities to engage online with your class and instructor during regular bi-weekly webinars (dates TBA).

You can expect to spend approximately five hours of work per week on course requirements.

Completion Requirements

This is a graded course where a complete or incomplete will be issued. Evaluation will be based on participation, and completion of course activities. Attendance in the live Zoom sessions is not mandatory, but is recommended.

Course Materials

No textbook required. All course materials are provided. Students will be required to create accounts and use tools typically used in the technology industry like Slack and GitHub.