Continuing Education

CCTB464 - Machine Learning I

Course Code

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Campus

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Course Overview

Following up on the fundamentals started in Introduction to Data Science, this course explores all the basic parts of executing a machine learning experiment. Upon successful completion of this course, you will be able to design your own machine learning experiments. Machine Learning I will present basic algorithms, such as regression, C4.5 decision trees, and Nearest Neighbours, with an emphasis on the fundamentals of properly designing a machine learning experiment which includes cross validation and evaluation metrics. At the end of this course you will be able to create your own ML pipeline, describe the differences between classic ML algorithms and of their best fit use cases, and build well performing ML models. Lessons are taught with the scikit-learn library in python.

It is recommended that you complete CCTB463 - Data Mining and Data Visualization prior to enrolling in this course.

Upcoming Offerings

This course is part of these programs or course groups:

Data Science


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Delivery Methods

  • Face to Face: Where: In-person meetings. When: Course is scheduled at a specific time for students to attend. Face-to-face instruction at all class meetings. Location may be on campus or at a worksite.
  • Blended: Where: Mixture of in-person & online components. When: Course is scheduled at a specific time for students to attend. Combination of face-to-face and online components at specific times. Some online components may be accessed online anytime.
  • Hyflex: Where: Choice to attend in-person or online meetings. When: Course is scheduled at a specific time for students to attend. For each class, students choose to attend in-person with the instructor or online at a specific time.
  • Remote Live Delivery: Where: Online with instructor. When: Course is scheduled at a specific time for students to attend. Instruction is delivered at set times online. Students do not come to campus.
  • Remote On-Demand Delivery: Where: Online anytime. When: No set class meetings. Coursework is accessed on-demand and online. While there are no set class meetings, there may be set due dates and deadlines for some activities. Students may interact with peers through virtual tools.
  • Remote Independent: Where: Online anytime. When: No set class meetings. Coursework is accessed on-demand and online, with no instructor support. While students choose when to do coursework, there may be set due dates and deadlines. 
  • Work Placement: Where: In-person meetings. When: Work is scheduled at a specific time for students to attend. Onsite work integrated learning. Location at a worksite.
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