Program Overview
Program Details
Canadian Students
Full Time Offerings
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
Explore our vibrant campuses and discover what makes Fanshawe unique. Get a firsthand look at classrooms, labs, student spaces, and residence halls, and connect with faculty, staff, and current students. Whether you're interested in learning more about academic programs, student services, or extracurricular activities, this is your chance to experience campus life up close. Don't miss the opportunity to see how we can help you achieve your academic and career goals!
Admission Requirements
International Admission Equivalencies
English Language Requirements
English Language Requirements
Applicant Selection Criteria
Applicant Selection Criteria
Post-Admission Requirements
Post-Admission Requirements
Courses
Level 1 | ||||
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.