Graduate Certificate in Applied A.I. Solutions Development (Co-op)

George Brown College - Casa Loma Campus

Canada,Ontario

 8 Shortlist

12 Months

Duration

CAD 18,443/year

Tuition Fee

CAD 110 FREE

Application Fee

Jan 2026

Apply Date

Canada, Ontario

Type: College

Location Type: Urban

Founded: 1967

Total Students: 32,117 +

Int. Students: 4,900 +

Campus Detail

Main Campus Address

160 Kendal Ave, Toronto, ON M5R 1M3, Canada

Graduate Certificate in Applied A.I. Solutions Development (Co-op)

Program Overview

In our innovative Applied A.I. Solutions Development one-year graduate certificate program, you'll immerse yourself in a cross-disciplinary blend of computer science, mathematics, and business, equipping you with the expertise to craft solutions across industries using artificial intelligence (A.I.), machine learning, deep learning, and data science/analytics.

This unique program merges computer science, math, and business with a design-thinking approach to produce machine-learning/deep-learning models and intuitive dashboards to communicate results and findings. As a student, you'll also receive comprehensive training in tailoring your presentations to target audiences, including technical experts, business stakeholders, and potential investors.

If you apply to the May 2024/25 intake for this program, you will start in May 2025. To begin your studies in May 2024, apply to the 2023/24 academic year.

As we advance further into this increasingly digital world, artificial intelligence and data science will revolutionize most industries by optimizing business processes and automating decision-making in many white-collar jobs. As a student in the Applied A.I. Solutions Development one-year graduate certificate program, you'll gain the skills to provide existing businesses and emerging startups with the tools they need to thrive in the digital revolution.

This three-semester program uses a hands-on, applied approach to the field of A.I., giving you the broad range of skills needed to excel in this booming industry.

We'll teach you to develop and apply machine-learning/deep-learning models, giving you a fundamental understanding of the underlying mathematical algorithms that power them. This combination of knowledge and skills will allow you to identify and select algorithms for different uses, build and fine-tune models, and communicate the resulting data––bridging the traditional roles of data scientist, machine-learning engineer, and business translator.
Technology Requirements

This program requires you to have access to a personal computer with the following specifications:

  • 8 GB minimum (16 GB RAM recommended0
  • 256 GB SSD Hard Drive (500+ GB is optimal)
  • Quad-core i5/i7 2.4GHz or better
  • Webcam

Your Field Education Options

In semester 3, students complete a Work Integrated Project, or qualified students are eligible for co-op. Learn more about how to qualify, apply, and important dates for co-op on the Centre for Arts, Design & Information Technology Experiential Learning page.

Program Learning Outcome

The graduate demonstrates the ability to:

  • Identify, evaluate and manage relevant data sources to support data analytics and to meet organizational needs.
  • Recommend different systems, architectures and data storage technologies to support data-driven solutions.
  • Develop and deploy complete machine learning/deep learning production systems for a variety of industry use cases that meet the needs of a specific operational/business process.
  • Assess and apply appropriate mathematical models, algorithms, tools and frameworks to develop A.I.-enabled, industry-specific solutions.
  • Design and present A.I. solutions effectively to stakeholders through the use of data visualizations.
  • Apply legal, ethical, privacy and security-related standards and considerations in data science projects in a manner that protects privacy and confidentiality, addresses data bias and transparency and ensures data integrity.
  • Implement artificial intelligence systems on time and budget using best practices and strategies in design thinking, project management and lifecycle management.
  • Design artificial intelligence (A.I.) systems through the application of systematic approaches and methodologies to meet growing organizational needs.