Continuing Education

Machine Learning and Artificial Intelligence Bootcamp

Unleash the power of AI and Machine Learning in our Immersive Bootcamp

This Bootcamp will teach students the skills to demonstrate competency in Machine Learning and Artificial Intelligence (AI) leveraging Python programming.

Students will learn Python coding while building their Machine Learning and AI skills. As a result of using Machine Learning and AI Python programming, students will transfer their prior studies and life experiences to authentic and relevant coding and programming projects while learning in a game-based environment.

In this Bootcamp students will discover how Machine Learning helps to create models that understand large amounts of data. Students will learn to use Python libraries for predictive problems also called ‘supervised learning' and for data clustering problems also called ‘unsupervised learning'. Students will study major Machine Learning techniques such as multiple linear regressions (Ridge and Lasso), generalized linear models and classification, clustering, and dimensionality reduction methods. This Bootcamp will offer students hands-on experience in solving simple and complex real-world problems across different industries that may include retail, finance, tech, and healthcare. The Bootcamp includes an experiential learning project. Within the Capstone project, students will learn about freelance coding projects and successfully complete a small freelance coding assignment.

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Quick Facts

Campus: Main

Any in-person components of your courses will be delivered at NAIT's Main Campus in Edmonton. View map

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      Courses, certificates and transfer credits

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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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