Program start date | Application deadline |
2024-09-01 | - |
2025-01-01 | - |
2025-05-01 | - |
Program Overview
Artificial Intelligence Graduate Certificate
The world is constantly evolving through technology. Today, artificial intelligence (AI) is used across many diverse industries to improve efficiencies, implement automated processes and more. As such, AI professionals in the workforce are in high demand.
This two-semester graduate certificate program equips you with the much-needed skills and knowledge to manage AI technologies and other related innovations like machine learning techniques. The hands-on learning environment offers students valuable training for the real workforce and provides professional AI credentials for a competitive edge in the job market.
Program Code: ART
Credential: Graduate Certificate
Duration: One Year (2 semesters)
Intakes: Fall (September), Winter (January), Spring (May)
Fees
Semesters | Semester 1 | Semester 2 |
---|---|---|
Tuition | $9,125.00 | $9,125.00 |
Ancillary Fees | $502.49 | $491.89 |
Health Insurance | $700.00 | N/A |
Semester Total | $10,327.49 | $9,616.89 |
Total Program Fees | $19,944.38 |
*Tuition and fees subject to changes.
*Health insurance is mandatory and non-refundable.
Scholarships and Awards
Click here to learn more about entrance scholarships and awards.
Program Highlights
- Learn to design software by embedding AI into applications and frameworks and using machine learning, big data fundamentals and other techniques.
- Apply technical knowledge and skills in hands-on computer lab time, applied research projects and applied learning experiences.
- Improve your skills in project management, collaboration, communication and data analysis.
- Prepare for an in-demand career in AI using the breadth of technical knowledge and experience acquired in the program.
Why Choose Fleming College Toronto
- Learn in a supportive environment that values both the theoretical and practical sides of skill development in college education.
- Build your networking community with an international classroom and unprecedented access to Canada’s bustling tech industry capital, Toronto.
- Expect innovative course content through traditional lectures, applied research, advanced computer lab training, guest speakers and more, supported by faculty from a wide range of disciplines and industries.
- Get access to an extensive suite of support services, such as career services, academic counselling, housing and more.
- Enjoy the benefits of cosmopolitan life, including a thriving job market, cultural diversity and networking opportunities.
Minimum Admission Requirements
Students applying to Artificial Intelligence must meet the following requirements:
- An Ontario College Diploma, Ontario College Advanced Diploma, degree or equivalent with a focus in computer studies, technology, engineering, analytics, mathematics, or statistics, OR an acceptable combination of related work experience and post-secondary education.
- Successful competition of Fleming College Toronto English Language Bridge Level 6 or provide IELTS Academic Overall 6.5 with no band score less than 6.0 or equivalent (students applying through the SDS stream must have an overall 6.0 with no band less than 6.0). Review the Language Requirements page for additional options.
Technology Requirements
Students are required to have their own computer, internet access, webcam and microphone. Some required software applications are not available for MAC OS or Chromebook. Computer must be virtual machine capable. No tablets or Chromebooks for lab work. For this program, the following system specifications are required:
Microsoft Computer
- Operating System: Windows 10
- Processor: Core i5 – 1.6 GHz
- Memory: 8 GB
- Hard disk: 160 GB
Apple Computer
- Operating System: MacOS 10.12
- Processor: Core i5 – 6th Gen
- Memory: 8 GB
- Hard disk: 160 GB
Internet
- Download speed: 2.5 Mbps
- Upload speed: 3.0 Mbps
Career Opportunities
Artificial intelligence is used across a variety of industries, and professionals who can design and manage AI software are in higher demand than ever before.
Some career titles include:
- Data administrator
- Database analyst
- Software engineer
- Software designer
- Software testing engineer
- Information systems analyst
- Computer systems analyst
- Information technology (IT) consultant
Program Courses
Semester 1
- Applied Machine Learning - COMP 647 - Hours: 60
- Fundamental concept and knowledge of machine learning will be covered in this course using Python and MATLAB. This includes supervised and unsupervised learning (e.g. support vector machine, clustering, neural network), mathematical and probative approaches. Students will have an opportunity to experiment with machine teaming solutions on various datasets.
- Cloud Computing for AI - COMP 648 - Hours: 60
- This course offers the student both theory and lab work that examines modern cloud technologies and everything-as-a-service (EAAS) and explores the machine learning and AI workbench in cloud. The students will learn installation, networking, support and administration of cloud technologies that can serve the needs of the businesses of today for AI, data science analytics and engineering. Also, security and disaster recovery strategies will be studied and applied.
- Data and Ethics - LAWS 333 - Hours: 14
- Data governance is the management of the availability, usability, integrity and security of data and information. Legal, ethical and organizational frameworks all must be considered whenever working with or presenting information from data. Reviewing studies and applicable legislation, students will learn to exercise ethical judgement in the use of data for an organization while also protecting the rights of groups and individuals.
- Introduction to Data Analysis - COMP 646 - Hours: 60
- The focus of this course is to explore the analytical and statistical methods and tools used to process and visualize data. In this course, students will learn the essential concept of data analysis using Python programming. Students will work with Python tools and libraries for AI algorithms in a lab environment.
- Linear Algebra and Statistics for Machine Learning – BUSN 266 - Hours: 60
- This course offers a brief introduction to the multivariate calculus required to build many common machine learning techniques. Students will learn the fundamentals of working with data in vector and matrix form and formulate machine learning tasks.
- Technical Project Management - BUSN 254 - Hours: 21
- This is a multi-disciplinary course designed to help students develop their skills in managing technical project management methods. Students will learn how to plan a project and work toward achieving their project goals.
Semester 2
- AI and ML Capstone Project - APST 180 - Hours: 45
- This course allows students to work through a guided project from design to development to implementation. This team-based project will provide students the opportunity to demonstrate their combined knowledge in AI and machine learning. Students will be challenged to assign responsibilities, create, and maintain satisfactory working relationships with the client, accept feedback, meet project deadlines, manage the production of deliverables to industry standards and formally present their findings.
- Deep Learning - COMP 650 - Hours: 60
- This deep learning course is based in the Python programming language and will provide students with experience in pandas, matplot, numpy and TensorFlow. Students will have the opportunity to implement different types of deep learning algorithms, such as convolution neural networks, recurrent networks, generative adversarial networks and autoencoders. Students will train neural networks and create neural network architectures in TensorFlow.
- Machine Vision and Image Processing - COMP 649 - Hours: 60
- This course introduces basic knowledge and concepts of machine vision, including image processing, pattern recognition and object tracking. To gain practical experience in this field, students will use the industry standard OpenCV library for developing machine vision applications. Students will create real-time applications, such as games or simulations, using image- and video-processing techniques.
- Natural Language Processing - COMP 652 - Hours: 60
- By the end of this course, students should have a broad understanding of the field of natural language processing. They should have a sense of the capabilities and limitations of current natural language technologies and some of the algorithms and techniques that underlie these technologies. They should also understand the theoretical underpinnings of natural language processing in linguistics and formal language theory.
- Social Media Analytics - COMP 651 - Hours: 60
- This course introduces the core concepts of social media analytics: an introduction to social media, the foundations of collecting and storing social media data and how to use AI and ML tools to analyze social media data. Also, the course provides students with hands-on practices and the opportunity to build, train and apply models that analyze social media data.
Program Outline
Outline:
- Semester 1
- Applied Machine Learning (COMP 647): This course focuses on fundamental and practical applications of machine learning using Python, including supervised and unsupervised learning techniques.
- Cloud Computing for AI (COMP 648): This course provides theoretical and practical training in using cloud technology and AI tools for data analysis and development.
- Introduction to Data Analysis (COMP 646): This course introduces essential data analysis methods and tools using Python, focusing on statistical analysis and visualization techniques.
- Linear Algebra and Statistics for Machine Learning (BUSN 266): This course provides the mathematical background needed for various machine learning techniques, covering concepts like matrix algebra and statistical modeling.
- Technical Project Management (BUSN 254): This multi-disciplinary course helps students develop project management skills and techniques.
- Semester 2
- AI and ML Capstone Project (APST 180): Students work through a guided project from design to development and implementation, demonstrating their combined AI and Machine Learning abilities.
- Deep Learning (COMP 650): This course delves into deep learning concepts using Python libraries, including TensorFlow. Students gain experience implementing various deep learning architectures like convolutional neural networks and recurrent neural networks.
- Machine Vision and Image Processing (COMP 649): This course introduces image processing, pattern recognition, and object tracking, using industry-standard OpenCV library to develop applications in real-time.
- Natural Language Processing (COMP 652): Students gain a comprehensive understanding of natural language processing techniques, focusing on capabilities, limitations, and underlying algorithms used in natural language technologies.
Careers:
- Potential career paths include software engineers, data administrators, database analysts, software testers, and IT consultants.
- The program prepares students for various industries that utilize artificial intelligence technologies.
Other:
- This program is designed for individuals with computer studies, technology, engineering, analytics, mathematics, or statistics backgrounds.
- Students will have access to advanced computer labs, guest speakers, and industry networking opportunities.
- Fleming College Toronto partners with Trebas Institute to deliver this program, offering international students access to Fleming College credentials while experiencing Toronto.
Fleming College Toronto
Overview:
Fleming College Toronto is a partnership between Fleming College and Trebas Institute Ontario, offering Fleming's programs in the heart of Toronto. It provides access to employment and networking opportunities, as well as the entertainment and multicultural events of Canada's largest city.
Services Offered:
Student Life and Campus Experience:
Key Reasons to Study There:
Location:
Situated in Toronto, students benefit from access to Canada's business and financial capital, offering numerous career opportunities.Program Variety:
Fleming College Toronto offers a range of diploma and graduate certificate programs in fields like business, healthcare, and technology.Industry Relevance:
Programs are designed to equip students with the skills and knowledge needed to succeed in today's job market.Supportive Environment:
Students benefit from a supportive learning environment with approachable faculty and career services.Academic Programs:
Diploma:
- Business Diploma
Graduate Certificates:
- Artificial Intelligence
- Global Business Management
- Health Care Management – Canadian Context
- International Business Management
- Project Management
- Supply Chain Management – Global Logistics
Certificate:
- Personal Support Worker
English Language Bridge (ELB):
- A pathway to diploma or graduate certificate programs at Fleming College Toronto.
Other:
New Programs:
Fleming College Toronto launched two new graduate certificate programs in 2024: Artificial Intelligence and Health Care Management – Canadian Context.Student Testimonials:
The website features testimonials from students highlighting their positive experiences and the benefits of studying at Fleming College Toronto.Entry Requirements:
Domestic Applicants (Canadian Citizens and Permanent Residents):
- Ontario Secondary School Diploma (OSSD) or equivalent with a minimum of six 4U/M courses at the College, University, or Mixed level.
- Specific course requirements vary depending on the program you are applying to.
- Minimum overall average of 70% on your most recent completed academic studies.
- Completion of specific academic prerequisites as outlined in the program requirements.
- Proof of English language proficiency as outlined below.
International Applicants (Non-Canadian Citizens and Non-Permanent Residents):
- A completed high school diploma or equivalent from your country of origin.
- A minimum GPA of 70% on your most recent completed academic studies.
- Completion of specific academic prerequisites as outlined in the program requirements.
- Proof of English language proficiency as outlined below.
- A study permit if required by Canadian immigration regulations.
Language Proficiency Requirements:
- All applicants must demonstrate English language proficiency at the following levels:
- Academic IELTS: Overall score of 6.5 with no band score below 6.0; or
- TOEFL iBT: Overall score of 88 with no section score below 21; or
- PTE Academic: Overall score of 65 with no section score below 59; or
- CanTEST: Overall score of 3.5 with no section score below 3.0; or
- Completion of an acceptable English language program recognized by Fleming College.
Additional Requirements:
- Some programs may have additional requirements, such as a portfolio, interview, or audition.
- Applicants with international credentials may be required to have their credentials assessed by a recognized credential evaluation agency.