Artificial Intelligence & Machine Learning
CIP Code: 11.0102
Overview
The emerging career field of artificial intelligence involves developing and applying advanced technologies such as machine learning and natural language processing to create intelligent systems that can automate and optimize a wide range of tasks.
The Lambton College Artificial Intelligence and Machine Learning, Ontario College Graduate Certificate provides comprehensive knowledge, skills, and training in the utilization of Artificial Intelligence (AI) and Machine Learning (ML) to solve significant business problems such as advanced trend analysis, generating business intelligence, digital automation, and intelligent manufacturing. As artificial intelligence (AI) continues to rapidly transform the way organizations and people work, businesses continue to have great demand for a variety of skill sets and disciplines to create new workflows within manufacturing and industry. The shortage of skilled professionals remains the single most significant challenge facing AI and ML adoption by businesses.
Program curriculum includes development of AI and ML models to identify patterns, provide insights, recommend actions or perform tasks. Students design and apply AI and ML systems that will take into account an organization's manufacturing and automation needs. As team communication in AI and ML is essential for organizational success, students learn applied project management skills, in addition to reporting communications and recommendations for various audiences, stakeholders and purposes.
Admission Requirements
- University degree in the information technology field
The admissions process is competitive and meeting the minimum academic requirements does not guarantee admission.
Lambton College reserves the right to alter information including admission requirements and to cancel a program or course at any time; to change the program curriculum as necessary to meet current competencies or changes in the job market; to change the pathways to third-party certification bodies; or to withdraw an offer of admission both prior to and after its acceptance by an applicant or student because of insufficient applications or registrations or over-acceptance of offers of admission. In the event Lambton College exercises such a right, Lambton College's sole liability will be the return of monies paid by the applicant or student to Lambton College.English Language Requirements
- IELTS of 6.5
-or-
IELTS of 6.0 + Completion of EAP-3106 (English for Academic Purposes) during the first term of study
- or -
- TOEFL iBT 79
-or-
ITOEFL 70 + Completion of EAP-3106 (English for Academic Purposes) during the first term of study
- or -
- Passed Lambton Institute of English placement test
Please Note: IELTS is the only proficiency score accepted by the Study Direct Stream (SDS) program. Additional country-specific requirements may also be applicable.
Meeting the minimum English requirements does not guarantee admission. Students with higher English proficiency scores will receive priority in the admission assessment process. Not all students will qualify for EAP-3106 in place of the required IELTS or TOEFL test scores.
Costs
- Term 1 $9,325.65
- Term 2 $8,539.73
- Term 3 $9,239.73
- Co-op Term $0.00
Total Cost of Program
Tuition fees are estimates and are subject to change each academic year. Fees do not include books (unless specifically noted), supplies or living costs.
Lambton College reserves the right to alter information including admission requirements and to cancel at any time a program or course; to change the location and/or term in which a program or course is offered; to change the program curriculum as necessary to meet current competencies or changes in the job market; to change the pathways third-party certification bodies; or to withdraw an offer of admission both prior to and after its acceptance by an applicant or student because of insufficient applications or registrations or over-acceptance of offers of admission. In the event Lambton College exercises such a right, Lambton College’s sole liability will be the return of monies paid by the applicant or student to Lambton College.
Additional Fees
Textbooks
The anticipated cost for textbooks in this program is approximately $500 - $700 per term. This amount accounts for both mandatory textbook costs (included in tuition fees) as well as textbook fees not included in your tuition fee amount.Textbooks
The anticipated cost for textbooks in this program is approximately $500 - $700 per term. This amount accounts for both mandatory textbook costs (included in tuition fees) as well as textbook fees not included in your tuition fee amount.
Important Dates, Deadline & Late Fees
For additional information on registration dates, deadlines and late fees please refer to Registration Dates and Deadlines.
Student Fees
A student services fee is included in your tuition.
Health Insurance Coverage
Emergency medical insurance is mandatory for all international students at Lambton College. This includes students who are full-time and part-time and who are on a co-op. This insurance is provided by GuardMe - a third party insurance provider.
See Insurance Costs & DetailsScholarships Available
There are 9 scholarships available for this program, with a total value of $1,000!
Technology Requirements
In order to keep pace with the requirements of each and every course in your program, Lambton College requires that each student have access to a laptop while studying at our college.
Courses
Data Science & Machine Learning
This course introduces the fundamentals of data science and machine learning. It covers key principles and concepts of data science, various data types, and common practices in data management. The course also explores the correlation between data science and business, providing insights into how Canadian businesses leverage data science for business intelligence and decision-making. Students will analyze various data science tools, focusing on Python programming language. Additionally, the course delves into data science methodology, guiding students through the common approaches to data science tasks and projects. A significant component of the course is understanding data preprocessing and feature engineering techniques, which are essential for preparing data for machine learning models. The laboratory portion is designed to provide practical applications of the topics covered to Canadian business and industry challenges.
Python Programming
This course introduces the core concepts of Python programming. The theory part includes an introduction to python and its properties, primitive data types, modules, functions, loops, and conditions. The laboratory portion is designed to provide students with the opportunity to work with a set of practical problems that Canadian businesses and industries have to resolve on a day-to-day basis.
Big Data Fundamental Data Storage Networking
In this course, students will discuss and evaluate the advancement and growth of data storage networking. This course is delivered as a combination of lecture and demonstration, as well as hands-on practice; exercises for the participants will be employed. The course will cover the state of the various data storage technologies available today and how they can be used to develop data storage strategies for business.
Mathematics of Data Science
This course provides a comprehensive introduction to the mathematical and statistical concepts essential for understanding and applying artificial intelligence (AI) and machine learning (ML). The course covers key mathematical principles, including algebra, linear algebra, calculus, statistics, probability, and information theory, tailored specifically to data science applications. Students will learn to perform statistical analyses, understand distributions, and apply linear algebra concepts to data science problems. The course also explores the fundamentals of regression, classification, and other statistical techniques used in machine learning. This foundational knowledge is crucial for grasping more advanced topics in AI and ML. The course emphasizes practical applications, ensuring students can confidently apply these mathematical concepts in real-world data science projects.
Introduction to Artificial Intelligence
This course introduces the core concepts of Artificial Intelligence, its related fields, and its applications. It covers the principles and fundamentals of AI, including its history and current trends. Students will explore the role of AI in various industries, particularly within Canadian business contexts, to understand how AI-powered solutions are applied to real-world problems. The course examines the relationship between AI and big data, highlighting how large data sets drive AI advancements and insights. Additionally, the course analyzes the role of intelligent agents in AI, explaining their functions and behaviors. A significant component of the course is dedicated to exploring various AI learning methods, including supervised and unsupervised learning, classification, regression, clustering, dimensionality reduction, and deep learning. Students will also examine artificial neural networks, focusing on their components, strengths, limitations, and applications. Lastly, the course delves into the Internet of Things (IoT) and its connection to AI, providing a comprehensive understanding of how these technologies integrate. The laboratory portion offers hands-on practice, allowing students to apply their knowledge to real-life AI challenges.
Introduction to Project Management
This course provides a fundamental knowledge to manage people, workflows, and costs in project management. Students (1) analyze the project management landscape; (2) apply the project management knowledge areas, process groups, and traditional methods to IT projects; (3) apply agile methods to IT projects; and (4) use project management software to conduct project analysis, develop reports, and manage changes to IT projects. The group assignment and term project provides students an opportunity to incorporate and apply project management skills to solve real-world problems.
Job Search & Success
This course provides student with skills and knowledge to help support their career search and succeed in the workplace. Students align their personal skill set and goals to guide them on their career paths. They will learn how to effectively conduct a job search, build a professional and well-tailored resume and cover letter, and develop and practice interview techniques. Students will also develop their personal brand to help support effective career networking and aid in their job search. Teamwork and collaboration in the workplace are also discussed. Self-reflection is used to inspire insight and support their professional career journey.
Visualization for AI and ML
This course introduces the core concepts of data visualization for Artificial Intelligence and Machine Learning. The theory part includes an introduction to the foundations of data visualization and data presentation. The laboratory portion provides students with hands-on practices and the opportunity to work with the most advanced visualization tools and libraries to make sense of data, present clear evidence of findings, and tell engaging stories through data graphics.
Advance Python - AI & ML Tools
This course introduces advanced concepts of Python programming language. The theory includes designing, implementing, and using APIs and advanced modules for AI and ML. The laboratory portion is designed to allow students to work with a set of practical problems and apply their knowledge to real-life software application challenges. Students use Python modules to interact with a database, analyze image datasets, create visualizations, and use PySpark to handle big datasets using the Apache Spark data processing framework.
Data Mining & Analysis
Data mining is a powerful tool used to discover patterns and relationships in data. Students learn how to apply data mining principles to the dissection of large complex data sets, including those in very large databases or through web mining. Students also explore, analyze and leverage data and turn it into valuable, actionable information for an organization
Natural Language Processing
This course introduces Natural Language Processing (NLP) and its key concepts. The theory part includes the use of classic machine learning methods to solve machine translation, language modeling, and sequence tagging. The laboratory portion is designed to provide students with the opportunity to work with a set of NLP problems and the opportunity to apply their knowledge to resolve them.
AI & ML Lab
The laboratory course is designed to provide students with the opportunity to select a semester-long project, inspired by a real-life issue or problem, and combine and apply their knowledge to tackle the issues and produce weekly deliverables in a systematic way and within the framework of project management.
SQL & NoSQL Database Design
This course provides a comprehensive introduction to both SQL and NoSQL databases and their applications in artificial intelligence (AI) and machine learning (ML). Students will explore the core concepts of relational (SQL) and non-relational (NoSQL) databases, understanding the differences and use cases for each. The course covers SQL database design, data modeling, and the use of Data Definition Language (DDL) and Data Manipulation Language (DML) to create and manage databases. Students will also delve into the four types of NoSQL databases: Document-oriented, Key-Value Pair based, Column-oriented, and Graph-based databases, with a particular focus on MongoDB. Hands-on experience will be provided through practical projects and assignments that reflect real-world challenges faced by Canadian businesses and industries.
Neural Networks & Deep Learning
Social Media Analytics
Software Tools & Emerging Technologies for AI & ML
AI & ML Capstone Project
Cloud Computing & Big Data & AI
Covering foundational technologies of cloud computing, this course includes virtualization, load balancing, scalability & elasticity, deployment, and replication. The course also explores the basics of cloud computing and its place in the modern enterprise through exploration of public and private clouds, comparison of "as a service" models for PaaS, SaaS, IaaS, and XaaS platforms, and cloud security. Additionally, the course integrates concepts of big data and AI, focusing on data storage solutions and the use of AI as a service provided by cloud platforms. Students will gain practical experience with cloud infrastructure and services, addressing real-world challenges in Canadian business and industry contexts.
Ethical Practices in AL & Data Sciences
This course is designed to equip students with a comprehensive understanding of the ethical considerations and legal obligations associated with the development and deployment of Artificial Intelligence (AI) technologies. It covers Canadian law as well as international perspectives, emphasizing the importance of ethical frameworks and legal requirements in AI development. Students will explore topics such as data privacy, intellectual property, and the social impact of AI. The course also addresses the ethical implications of emerging technologies like cloud computing and big data analytics in the context of AI. Through case studies and real-world scenarios, students will learn to make informed decisions in AI development and deployment.
Co-op Work Term (Full-Time)
Co-operative education provides students with the opportunity to apply classroom learning to the workplace, undertake career sampling and gain valuable work experience that may assist students in leveraging employment after graduation.
WIL Project
Work Integrated Learning (WIL) Project is aimed at enriching students by connecting different program areas of study, cutting across subject-matter lines, and emphasizing unifying concepts. The focus of the WIL Project is to make connections between study and industry by engaging students in relevant and meaningful activities that are connected to and practiced within the professional workplace. WIL Project allows students to enhance and strengthen their employability prospects post-graduation by fine tuning skills and knowledge and meeting the expectations of today's employers. Students are required to attend the scheduled shifts in the WIL office, reporting to the WIL Supervisor. Weekly real-world challenges are presented in the WIL office, designed by industry professionals. In addition to the weekly assigned deliverables, students are also offered professional development sessions, and exposed to industry guest speakers, enhancing their opportunity to develop their professional network.
Co-op Eligibility & WIL Project Fee
In order to be eligible to secure an approved full-time co-op work term (CPL-1049), students must have a GPA of 2.8 or greater and complete all the co-op eligibility requirements. Failing to do so will require students to enroll in CPL-5559 WIL Project at an additional cost.
Contact
Centre for Global Engagement
LAMBTON COLLEGE SARNIA
1457 London Road
Sarnia ON, N7S 6K4
After Graduation
Employment Opportunities
Graduates will have the in-demand skills to operate at entry and intermediate roles on an AI and ML project in a variety of industries and occupational areas, including but not limited to technology implementation, business transformation, management and consulting.
Career positions may include, but are not limited to:
- Business Intelligence Designer
- Business Intelligence Designer
- Robotics Process Analyst
- AI Interaction Designer
- Artificial Intelligence Technologist
- Machine Learning Analyst
- Machine Learning Technologist
- AI System Developer
- Business Transformation Consultant
Looking for Support After Graduation?
The International Graduate Services & Support Centre (GSSC) is a place dedicated to assisting International alumni as they seek employment and settle into Canadian life following graduation.
Post-Graduate Employment
International students who successfully complete their programs of study at Lambton College may be eligible to apply for a Post-Graduation Work Permit (PGWP) Program. This program allows students to gain valuable Canadian work experience.
A work permit under the PGWP may be issued for the length of the study program, up to a maximum of three years. A post-graduation work permit cannot be valid for longer than the student's study program, and the study program must be a minimum of eight months in length. The length and approval of the PGWP is determined solely by Immigration, Refugees and Citizenship Canada (IRCC).
Students must meet the eligibility requirements to apply for a post-graduation work permit.
Immigration Regulations & Changes
Immigration regulations are legislated by the Federal Government of Canada and are subject to change at any time without notice. Students are responsible for ensuring that they are in compliance with all Immigration, Refugees and Citizenship Canada regulations at all times during their studies and while in Canada. Lambton College staff are not authorized to provide advice or guidance on immigration-related matters. Prospective applicants and current students should consult the Immigration, Refugees and Citizenship Canada website or call the IRCC Call Centre at 1-888-242-2100 to answer or clarify any immigration-related questions or information.
Co-op
About Co-op
Students in this program have the opportunity to gain valuable work experience by applying classroom learning during co-op experiences.
Learn more about co-op terms and the roles and responsibilities of students and co-op advisors.
More Information
Student Responsibilities
- Course and program delivery schedules are proposed and subject to change for each intake.
- Students are required to bring their own laptop with wireless capability.
- Students are advised to bring an official copy of their most recent police clearance, driver's license, and vaccination record from their home country.
Technology Requirements
It is recommended that students purchase a laptop with a Windows operating system.
Internet Speed Requirements
For best performance for students learning remotely, an internet connection with a minimum of 40 Mbps download and 10 Mbps upload speed is recommended in order to effectively use video conferencing and remote lecture delivery software as well as, other online resources remotely. Due to the large area over which students may be dispersed, we are unable to recommend a specific provider, so you will need to inquire around your area to find one that best suits your needs.
Minimum Laptop Requirements
In order to access the internet and virtually-delivered software and courseware, student laptops should include the following at a minimum. By meeting the following specifications, students will be equipped to access software and courseware on their laptop through the internet:
- Intel i5 8th Gen Processor or equivalent
- 16 GB of RAM (with a minimum of 8 GB)
- 100 GB HDD or more
- HD Graphics
- Webcam with a microphone
- Wireless 802.11n/ac 5ghz capable
- Windows Operating System (Windows 11)
Software
To ensure students are getting the most our of their classroom experience, some software will be required.
Lambton College has made this software easily accessible online. Students can leverage our Microsoft Office 365 software packages and services. In addition, much of the software you require for your courses will be available on demand for use on any device - on or off campus.