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IT Programming for Artificial Intelligence (AI)

Create intelligent applications that use Machine Learning (ML) technologies.

An office setting with several people sitting in front of computers and programming.
Start Date:
Typical Length:
1 Year
Graduate Certificate

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Learn the most important tools used in one of the most exciting fields of programming today. The growing field of AI helps increase organizational efficiency and productivity, and is being adopted by every major sector, including health, manufacturing, transportation and more.

In this graduate certificate program, you learn to:
  • Become proficient with the in-demand Python programming language and create applications in Python
  • Use the most popular Python libraries and frameworks for Machine Learning
  • Use Machine Learning techniques to develop application features.
  • Develop core database skills and learn how to work with datasets – analyzing, extracting and transforming them, and preparing them to be used to train models
  • Develop a basic understanding of the underlying foundations upon which AI technologies are built
  • Explore current and future applications of AI as well as potential societal implications, considering ethics, governance and sustainability as it relates to business decisions and industry need
  • Create and use Deep Neural Networks including the associated, practical mathematics

Work experience

  • Field experience is a mandatory credit course, providing the opportunity to apply your knowledge and skills outside the classroom.
    • Opportunities are typically unpaid and last approximately 5 weeks.
  • Transportation, accommodations and other costs related to work experience courses are your responsibility.
  • For more information, visit work experience opportunities.

Choose NSCC

  • Our instructors come from industry with world-class experience and skills.
  • This program was developed with industry – for industry, ensuring your skills are current and relevant.

September 2024

Campus Full time/part time Delivery Availability
Institute of Technology Campus/Online
Full time Blended
Delivered through a combination of online and in-person classes. At least 50% of learning is in-person.

Admission requirements


Tuition amounts are for the 2024-25 academic year. Program costs and fees (textbooks, supplies, etc.) are additional.

Tuition (Domestic):
Tuition (International):

Tuition, fees and program costs

In addition to annual tuition, there are program costs (books, tools, etc.) and student fees for College services, health and dental plans, your student association and parking.

View detailed program fees page(s). Please note that amounts on these pages are meant for planning purposes only. They don't represent final amounts owing.

Career options

Graduates may find employment in the following areas:

  • AI Developer
  • Python Developer
  • Software Developer (Machine Learning)
  • Machine Learning Engineer

Courses may include

These are some of the courses offered in this program. It is not a complete list and courses are subject to change in advance of the academic year.

Recognizing prior learning / transfer credits
If you have previous learning (course, employment, etc...) that's relevant to your program, you may be able to apply to earn credit. Not all programs are eligible. Learn about our recognizing prior learning (RPL) process.

DBAS 5115 - Introduction to Data
This course provides an overview of various types of databases starting with simple files and formats – like CSV and JSON. The focus is on working with existing databases rather than designing and creating new databases.

DBAS 5125 - Data for Artificial Intelligence
This course leverages the data tools and technology knowledge introduced in 'Intro to Data' and applies it directly to the field of Artificial Intelligence programming.

INFT 5105 - Agile Methodologies
This course covers the specific knowledge required to manage IT projects using an agile methodology model.

INFT 5500 - Work Experience
This course is either a campus-based or external applied learning experience which may consist of work experience, directed studies, industry projects or applied research.

PROG 5110 - Topics in Artificial Intelligence
This course provides an overview and history of the field of Artificial Intelligence. Students will examine subcategories of AI: AGI vs ANI, Machine Learning, Statistical Learning and Neural Networks as well as past and current applications of AI. Students will speculate on future AI applications, profile AI companies and debate associated ethical issues.

PROG 5115 - Neural Networks
Students will be introduced to very basic neurophysiology to motivate the introduction of the first artificial neural models. Starting with the perceptron, students will build basic artificial neural networks (ANNs) to develop a basic understanding of the concepts and acquire the foundational knowledge to understand and use current, popular deep learning frameworks.

PROG 5120 - Programming for Artificial Intelligence I
This course is a basic introduction to logic and programming.

PROG 5125 - Mathematics for Artificial Intelligence
This course provides a survey of the foundational mathematics used in Artificial Intelligence programming. It covers basic algebra, basic statistics, linear algebra, and a short introduction to the principles of calculus.

PROG 5135 - Programming for Artificial Intelligence II
This course builds upon Programming for Artificial Intelligence I, and introduces different programming techniques and domains. Students will learn at least one new programming paradigm (OOP) and will be introduced to web application programming.

PROG 5145 - Mathematical Programming and Algorithms
This course builds on the mathematics learned in Mathematics for AI and examines how to implement the techniques using industry standard mathematical libraries.

PROG 5146 - Career Development for Artificial Intelligence Professionals
This course focuses on career paths and the documentation and processes required when applying for jobs in the field of Artificial Intelligence.

PROG 5155 - Machine Learning
This course uses industry standard frameworks, libraries, and services to create and train machine learning models using realistic data.

SAFE 1000 - Introduction to WHMIS (Workplace Hazardous Materials Information Systems)
This course offers learners basic overview of WHMIS principles and establishes a solid foundation to support workplace-specific training on the safe storage and handling of controlled/hazardous products. Upon successful completion of the course, students receive basic WHMIS certification.

SAFE 1001 - Introduction to NS OH&S Act
This course offers students an introduction to the Occupational Health & Safety (OH&S) Act of Nova Scotia, which is required by any person employed in a Nova Scotia workplace. This is a generic, introductory course that provides basic knowledge of the Act for students and is considered to be the basis from which more specific training can be given.

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