Artificial Intelligence and Machine Learning (Co-op)
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Program Overview
The one-year Artificial Intelligence (AI) and Machine Learning post graduate co-op education program will focus on teaching students how to build, manage and administer systems that analyze big data and convert it into an autonomous task. If you're ready to build a highly in-demand skillset in artificial intelligence and machine learning, then this certificate will provide you with everything you need to hit the ground running in this fast-growing field of information technology.
international only
No
Program Details
Program Code
AIM2
Credential:
Ontario College Graduate Certificate
Duration
30 Weeks
Canadian Students
Start Dates
Locations
Availability
Full Time Offerings
2025 January
London
Open
2025 May
London
Open
2025 September
London
Open
2026 January
London
Open
2026 May
London
Open
2024 September
London
Closed
Your Learning Experience
AIM2 is only available to domestic students. A version for international students can be found at AIM1
The AI and Machine Learning one-year graduate certificate program is designed for students with software development backgrounds who want to specialize in this highly demanded field of information technology.
With a focus on deep learning neural networks, students will discover how to design and deploy cutting-edge technologies such as convolutional neural networks, recurrent neural networks and generative adversarial networks in areas such as healthcare, bioscience, manufacturing, financial services and supply chain sectors. Students will also work with mainstream technologies such as Google's TensorFlow to train various machine learning models using big data and state-of-the-art hardware.
Not only will students gain extensive technical knowledge of AI and Machine Learning, but they will also learn and apply skills in project management, communication and teamwork through hands-on, industry-based research projects and a comprehensive in-class capstone project.
In their final semester, students will complete a paid, co-operative work term with an industry partner.
Laptop and technical requirements
Please note, this program requires a laptop. It is recommended that students use a PC laptop vs. a Mac laptop, as Windows is required to be able to load program-specific software. For more information on specific requirements, visit the Laptop Requirements page on the Fanshawe CONNECTED website.
Career Information
Fanshawe’s AI and Machine Learning program will prepare graduates to take advantage of the growing opportunities in the AI and Machine Learning field of information technology. Graduates can expect to be hired as the following:
- AI Developer/Programmer - developing artificial intelligence software and applications, and programming systems based on the data collected and analyzed
- Machine Learning Developer - developing artificial intelligence systems that use big data to research, develop, and generate algorithms to learn and make predictions
- Data Analyst - collecting, processing and tracking down statistical information from datasets
- Computer Systems Analyst - maintaining and upgrading existing systems and designing new computer systems and frameworks
Admission Requirements
An Ontario College Diploma, an Ontario College Advanced
Diploma, a Degree, or equivalent in Computer Science or a related
field with software development education.
International Admission Equivalencies
Admission equivalencies for Fanshawe depends on your country of study. Please enter your location to see the requirements for your country below.
English Language Requirements
English Language Requirements
Applicants whose first language is not English will be
required to demonstrate proficiency in English by one of the
following methods:
- A Grade 12 College Stream or University Stream English
credit from an Ontario Secondary School, or equivalent, depending
on the program�s Admission Requirements
- Test of English as a Foreign Language (TOEFL) test with a
minimum score of 88 for the Internet-based test (iBT), with test
results within the last two years
- International English Language Testing System (IELTS)
Academic test with an overall score of 6.5 with no score less than
6.0 in any of the four bands, with test results within the last two
years. SDS Program Requirements
- Canadian Academic English Language (CAEL) test with an
overall score of 70 with no score less than 60 in any of the four
bands, with test results within the last two years
- Pearson Test of English Academic (PTE) with a minimum score
of 59, with test results within the last two years
- Cambridge English Test (FCE/CAE/CPE): with an overall score
on the Cambridge English Scale of 176 with no language skill less
than 169, with test results within the last two years
- Fanshawe College ESL4/GAP5 students: Minimum grade of 80% in
ESL4/GAP5 Level 9 or 75% in ESL4/GAP5 Level 1
Applicant Selection Criteria
Applicant Selection Criteria
Where the number of program eligible applicants exceeds
available spaces, the Applicant Selection Criteria for this program
will be:
1. Preference for Permanent Residents of Ontario
2. Receipt of Application by February 1st (After this date,
Fanshawe College will consider applicants on a first-come,
first-served basis until the program is full)
3. Achievement in the Admission Requirements
Post-Admission Requirements
Post-Admission Requirements
- Mandatory Placement Assessment for WRIT course
- Possession of a Standard First Aid course certificate
(either St. John Ambulance or Canadian Red Cross or equivalent) and
a Basic Rescuer course certificate - Basic Life Support (BLS) for
Health Care Providers (HCP) in accordance with the Heart and Stroke
Foundation of Canada Canadian Guidelines for CPR
- Possession of a Standard First Aid course certificate
(either St. John Ambulance or Canadian Red Cross or equivalent) and
a Basic Rescuer course certificate (Level "C" CPR)
- Evidence of Good Health
- Police Record Check and Vulnerable Sector Screening,
including a check of the Pardoned Sexual Offenders Database
- Placement Agreement
Courses
| Level 1 | ||||
| Take all of the following Mandatory Courses: | ||||
| INFO-6146 | Tensorflow & Keras With Python | 4 | ||
| This course provides students with an introduction to the Google TensorFlow platform through the Python Keras framework, including a review of Python and related development tools. Coursework includes deep learning models utilizing classification and regression, unsupervised clustering, and HMMs (Hidden Markov Models). | ||||
| INFO-6147 | Deep Learning With Pytorch | 3 | ||
| This course covers the theoretical and practical applications of state-of-the art deep learning for various datasets (e.g., tabular, image, text, time series). An open-source software stack (i.e., Python, PyTorch, PyTorch Lightning) will be utilized for this course. | ||||
| INFO-6148 | Natural Language Processing 1 | 4 | ||
| This course introduces Natural Language Processing (NLP) and its key concepts. Students will utilize the spaCy Python library to solve real world text processing problems. This will include the application of text-processing pipelines, the extraction of linguistic features, word vectors, intent recognition and other language processing strategies. | ||||
| INFO-6149 | Machine Learning Security | 3 | ||
| In this course, students will discover how to mitigate the major kinds of machine learning security risks, including compromises of unsupervised learning systems utilizing strategies such as evasion attacks, data poisoning and model stealing. | ||||
| INFO-6150 | Data Mining & Analysis | 3 | ||
| 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. | ||||
| INFO-6151 | Data Visualization for Machine Learning | 3 | ||
| This course delves into the principles and methodologies of data visualization driven by machine learning using Python. Participants will grasp the art of crafting informative and compelling visualizations throughout the entire machine learning journey, spanning from data exploration and preparation to the interpretation of model evaluations. | ||||
| COOP-1020 | Co-operative Education Employment Prep | 1 | ||
| This workshop will provide an overview of the Co-operative Education consultants and students' roles and responsibilities as well as the Co-operative Education Policy. It will provide students with employment preparatory skills specifically related to co-operative education work assignments and will prepare students for their work term. | ||||
Tuition Summary
London
*Total program costs are approximate, subject to change and do not include the health and dental plan fee, bus pass fee or program general expenses.