Use machine learning solutions to change the world of business
Growth in Machine Learning jobs
Take your skills beyond code
In the current job market, it’s not enough to be a Python wizard or statistical genius. Top employers are looking for machine learning specialists who understand the business and ethical impact of their work. The Certificate in Machine Learning was created in collaboration with industry leaders, including major banks, ensuring that you graduate with the exact skills and real-world experience that will land you the job you want.
“The field of machine learning and artificial intelligence is going through a rapid growth phase with many emerging applications. It is of utmost importance for businesses and data professionals to understand the implications of these changes and act on them. This program will give students the expertise they need to implement effective machine learning processes in the years to come.”
- Dr. Iman Khodadad, Chief Technology Officer and Co-Founder, Elucid Labs (Advisory Council Member)
You’ll work on multiple machine learning projects of various scales that capture the entire machine learning lifecycle. You will work with both structured and unstructured data and with various supervised, unsupervised and reinforcement learning algorithms. These projects allow you to build upon your technical skills while gaining a holistic understanding of machine learning as it relates to current and relevant problems facing businesses. You’ll learn key skills that will allow you to:
- Build a business case to support the implementation of machine learning solutions in various industries
- Identify and assess the viability of different machine learning models using data, and explain your recommendations clearly to an employer
- Use project management principles to plan and execute a machine learning solution
- Use Python (and other languages) to create, implement and test machine learning algorithms in real-world scenarios
Who should take this program?
The Certificate in Machine Learning is for anyone with at least a second year undergraduate level understanding of linear algebra, calculus, probability and inferential statistics and with experience in Python or other programming languages.
Assess your readiness
Use our self-assessment tool to find out where your knowledge and proficiency fall within the necessary range for success in this program.
“It’s the first time that I’ve found a continuing education certificate that does so much work to help students connect with the industry and get hired.”
- Lynn Liu, Student, Certificate in Machine Learning
Machine learning specialists work in a variety of industries, including:
We know that fitting continuing education into your schedule isn’t easy. So we make it easier by offering a flexible study format that works around your commitments.
The Certificate in Machine Learning is an 8 month part-time program, taken mostly online. You’ll come to our Keele Campus in Toronto, ON for five 3-day weekends over the course of the entire program. This format allows you to easily balance your studies with your work and home life.
Finish in 8 months
This program offers you a suite of additional benefits to enrich your learning and professional development.
These are just some of the extra perks you’ll receive when you enrol:
- NVIDIA Certificate in Fundamentals of Deep Learning for Computer Vision
- Google Colaboratory account
- Chance to enter Kaggle competitions, as part of the curriculum
- Up to $150 in credits for Google Cloud Platform
- Free admission to the Toronto Machine Learning Summit, a key industry networking event
- Opportunity to attend additional professional development events with your peers:
- Toronto Machine Learning Micro-Series
- AI Geeks
- RBC FutureMakers
- First access to job opportunities from top employers
- Continued connections to your peers and program manager after graduation
DR. HARISH BHASKAR, President and Chief Scientific Consultant, ZERO ONE INFINITY CONSULTING
Harish is a data officer and an expert in artificial intelligence (AI)-powered data analytics with 15+ years of experience working in aerospace, telecommunication, automotive, security, geospatial and biomedical industries. He is the President and Chief Scientific Consultant of Zero One Infinity Consulting (ZOIC) Services Ltd., an Ontario-based Research and Development (R&D) company. Previously, he was a chief engineer at Samsung Electronics, India and an assistant professor/lead at the Visual Signal Analysis and Processing (VSAP) research center at Khalifa University, UAE.
Harish has also worked as a researcher, managing the R&D of Ministry of Defense (MoD) UK and European Union (EU)-funded projects at Lancaster and Manchester universities, UK. Harish is a strong engineering professional with a Ph.D. in Computer Science and an M.Sc. in Autonomous Systems from Loughborough and Exeter universities, UK, respectively. He has proven capacity to coordinate and spearhead the establishment of international research units and extensive management experience recognized through the successful completion of several public and private funded R&D projects in more than five different countries across the world.
JEFF BRUNET, President, WYSDOM.AI
Jeff is a serial entrepreneur and investor with over 20 years of proven technology and general management experience. He brings to the table an in-depth understanding of starting, funding, scaling, and exiting software companies. With successful M&A transactions to Bitfone, HP, Google, Apple, and Opera Software, Jeff has the background and track record to overcome the unique challenges in making new technologies successful in the start-up world.
Jeff holds 24 issued patents in the wireless and consumer electronics spaces and is the lead inventor on 30+ pending patents. He is a skilled presenter, communicator, and technology evangelist who speaks English, French and Mandarin Chinese. Jeff is an active angel investor and serves on the boards of a number of technology companies.
Before joining Wysdom as President, Jeff was co-founder and CTO at Storage Appliance Corporation (Clickfree) where he led the software, product and manufacturing teams to create a series of automatic computer backup devices that sold over a million units and generated $100+ million in revenues. Prior to Clickfree Jeff served as Senior Vice President and Chief Technology Officer with Bitfone Corporation where he was responsible for all Engineering and Professional Services around the company’s Mobile Device Management product portfolio. While at Bitfone Jeff also led the ground up creation of a highly successful 100+ person development team in Beijing China. Bitfone was acquired by HP in 2006 for $160 million. Before joining Bitfone, Jeff served as Chief Technology Officer with Mobile Diagnostix Inc. He was responsible for all aspects of the architecture & implementation of the company’s flagship product SmartCare – a customer care & remote diagnostics tool for mobile service providers. Mobile Diagnostix was successfully acquired by Bitfone Corporation in 2004.
CHRISTIAN GRAVEL, co-founder and CEO, INTELLECT DYNAMICS
Christian is a French Canadian born entrepreneur and investor who is the co-founder and CEO for Intellect Dynamics. Christian leads and manages all of Intellect Dynamics commercial operations. Along with CTO and co-founder Stan Yazhemsky, Christian oversees a team that has had dozens of breakthroughs in the field of AI. Christian is focused on delivering Intellect Dynamics’ technology vision by developing profitable technology solutions and services with a focus on real time analytics, artificial intelligence, business process automation and decision support. With a background in neuroscience, Christian and his team are using borrowed-from-nature methods to create new and useful knowledge from otherwise unmanageable data collections.
MELISSA SARIFFODEEN, Co-Founder and CEO, CANADA LEARNING CODE
Melissa is a social entrepreneur, leader and big thinker. As Co-Founder and CEO of Canada Learning Code, Melissa has established a reputation as a fierce advocate for digital skills education. She is dedicated to ensuring that Canadians – especially underrepresented groups – have the critical skills, confidence, and opportunities that they need to become passionate builders — not just consumers — of technology and to thrive in our increasingly digital world.
Founded as Ladies Learning Code in 2011, the organization has evolved to Canada Learning Code and champions digital literacy for Canadians, working nation-wide and across all sectors to equip women and youth with technical skills. To date, the organization has taught over 80,000 Canadians code through their in-person programs, which are offered in over 30 cities across the country. The organization runs programming for adult women, youth and educators through programs Ladies -, Girls -, Kids -, Teens – and Teachers – Learning Code.
Her efforts to promote digital literacy in Canada have garnered attention from national media outlets like CTV, CBC, and the Globe & Mail. She’s spoken on the TEDx stage as well as at notable conference such as Canada2020 and GoNorth. Melissa recently represented Canada as a delegate for the G7’s first-ever Innovation7 focused on the future of work. She has been recognized for her work, being awarded the Governor General Award in Commemoration of the Persons Case honouring Canadians who advance gender equality as well as RBC Entrepreneur of the Year 2017 for Social Change.
Melissa holds an HBA degree from the Richard Ivey Business School and is currently pursuing her Master’s in Education Policy at the Ontario Institute for Studies in Education at the University of Toronto. She taught herself how to build websites from scratch using HTML when she was 11 years old and has been coding ever since.
DR. NIDHI HEGDE, Applied Research team lead, BOREALIS AI
Nidhi is the Applied Research team lead at Borealis AI, Edmonton, Canada. Borealis AI is an RBC institute for research. Before joining in March 2018, I was a researcher at Bell Labs, Nokia, in France since January 2015, and most recently was leading a new team focussed on Maths and Algorithms for Machine Learning in Networks and Systems, in the Maths and Algorithms group of Bell Labs. From July 2010 to December 2014 I was a researcher at the Technicolor Paris Research Labs. Before that I was at France Telecom R&D in Jim Robert’s Performance analysis group from 2005-2010. From mid-2003 to 2005 I was at CWI and Eurandom in the Netherlands, and from late 2001 to 2003 I was a postdoctoral researcher at INRIA at Sophia Antipolis, France.
DR. IMAN KHODADAD, cofounder and CTO, ELUCID LABS
Iman has a diverse technical background in devices and system level architecture planning, design, fabrication and manufacturing for a wide range of remote sensing technologies. He is specialized in spectroscopy, microscopy, optical imaging systems and integrated optoelectronics and nanophotonics. With eight years of industrial experience including entrepreneurial start-up environments and research laboratories, he has brought various sensing technologies to various sectors such as medicine, oil and gas, environmental monitoring, water and agriculture. His interests are pairing remote sensing technologies with artificial intelligence for improved processes. Currently, Iman is a cofounder and CTO at Elucid Labs, commercializing AI-based assistive diagnostic systems in medicine and is part of Canadian CIO Strategy Council to set forth ethical AI standards and policies.
MARIA D’ANGELO, Data Scientist, ZERO GRAVITY LABS
Biography will be posted soon.
YANG HAN, STACKADAPT
Biography will be posted soon.
JESSE HIRSH, METAVIEWS MEDIA MANAGEMENT
Biography will be posted soon.
AJINKYA KULKARNI, SCOTIABANK
Biography will be posted soon.
DAVID SCHARBACH, TORONTO MACHINE LEARNING SUMMIT
Biography will be posted soon.
DR. OZGE YELOGLU, MICROSOFT CANADA
Biography will be posted soon.
Students in this program learn to:
- Describe the major business use cases of machine learning in various industries and how it provides monetary and other value to respective entities.
- Assess the appropriateness of various techniques in identifying machine learning solutions to business problems with highest efficiency and least resources.
- Develop a comprehensive plan for a machine learning project that incorporates principles from different project management methodologies.
- Prospect the feasibility of a machine learning solution being successful by comparing the data requirements with available and usable data.
- Select machine learning models within supervised, unsupervised and reinforcement learning paradigms that map to specific business cases and challenges.
- Design the various evaluation metrics and procedures required for model selection and explain the business rationale for use of the selected model.
- Create, implement and test machine learning algorithms in various real-world scenarios using Python and other languages and frameworks.
- Assess developments in artificial intelligence for ethical and public policy considerations as they relate to data privacy and protection, profiteering with meta-data, and various social and civic issues.
- Present a business case for implementation of a selected machine learning solution that communicates the potential value-add, and using accessible language
ML1000 – Machine Learning in Business Context
You will learn the building blocks and tools that will empower you to take raw data sets and extract valuable insights and data visualizations that bring the data to life. You will be introduced to R programming and learn to manipulate data using various R libraries while working in RStudio and Weka. You will also be introduced to common algorithms and techniques for analyzing data and be introduced to the concepts, practices and tools that are used to implement machine learning. The course is designed to be practical and allow students to quickly apply their new skills to real world problems, with real datasets and using the CRISP-DM methodology.
ML1010 – Applied Machine Learning and Lifecycle
You will cover the lifecycle of machine learning working with Python and R, including processing unstructured data, feature engineering, dimensionality reduction, model selection and optimization, performance evaluation, and model improvement. You will cover advanced algorithms for complex problems that require specialized methods. You will gain hands-on experience with advanced algorithms such as ensemble methods, sequence models, association rule mining, and neural networks. You will construct models using data from a variety of application domains. Time will also be allotted throughout the course to expose students to various ethical and public policy considerations of their work.
* successful completion of ML1000 is required to begin ML1010.
ML1020 – Machine Learning at Scale
Having an understanding of the concepts of machine learning, you will examine and apply the fundamentals of working with extremely large data sets. First, you will parallelize and perform MapReduce operations using Hadoop and Spark. Second, you will take a look at the fundamentals of deep learning and working with TensorFlow/CNTK and Keras. You will focus on image and voice data and build deep neural network models. At the end of this module, you should be ready to take on a large-scale real-world project challenge.
* successful completion of ML1010 is required to begin ML1020.
ML1030 – Machine Learning Capstone
In the machine learning capstone course, students address a business problem facing an organization by applying analytics models, methodologies, and tools learned in the program. The course will allow students to work on an end-to-end machine learning solution from problem formulation all the way to its deployment. As a result, students will create a data product that can be used to show their skill set to potential employers. The course will also provide an opportunity for students to work closely with business stakeholders, provides a networking opportunity, and exposes them to a real business setting.
* successful completion of ML1020 is required to begin ML1030.
FAEZEH HEYDARI KHABBAZ, PhD, MASc
Dr. Faezeh Heydari Khabbaz is a Director of Data Science in Data and Analytics at RBC, where she leads a team of data scientists leveraging machine learning and data science practices to solve various business problems. Dr. Khabbaz joined RBC in February 2016, and since then has worked on multiple initiatives for different lines of businesses. Dr. Khabbaz also holds a PhD in biomedical engineering from University of Toronto, where her research concentrated in medical robotics and teleoperation.
KARTHIK KUBER, PhD, MS
Dr. Karthik Kuber has several years of academic research as well as applied data science experience in the technology and banking sectors. His current interests are in exploring and applying various Machine Learning techniques in the context of large-scale engineering systems, with a special emphasis on building interpretable models. Dr. Kuber received his PhD from Syracuse University in Computer Science, focusing on Machine Learning and Evolutionary Computation, is a certified instructor with NVIDIA Deep Learning Institute, and has co-organised three editions of the International Workshop on Evolutionary Rule-based Machine Learning. Dr. Kuber is currently a data scientist at Microsoft in Seattle, Washington. He also volunteers actively with DataKind on projects applying Data Science for social causes. Outside of work, you will probably find him on a cricket field or in a dojo.
ANNIE LEE, PhD, MMath
Dr. Annie Lee holds a PhD from the University of Waterloo at the Centre of Pattern Analysis and Machine Intelligence with over 12 years of experience in data mining and machine learning. Dr. Lee’s passion in finding patterns in society and in nature in the big data era has lead to dozens of publications in computational advertising, sentiment analysis, and sequence analysis, including a highly-read review paper on big data. Most notably, Dr. Lee developed interpretable unsupervised algorithms, which uncovered patterns utilizing clustering and partitioning of raw data and a priori knowledge.
Currently, Dr. Lee is the Lead Research Scientist for the Data Science team at Vertical Scope, where she drives the efforts for the in-house academic research, algorithmic implementations, and evaluations. During her tenure, Dr. Lee propelled projects on identifying product names, sentiments analysis towards product features, and topic modelling in order to generate key business insights for reporting brand and site health.
Dr. Lee is currently serving as the funding chair for Broadening Participation in Data Mining and was a co-chair of Women in Machine Learning, co-hosted with the 2010 Annual Conference on Neural Information Processing Systems in Vancouver. Dr. Lee was an NSERC and OGS scholar.
HASHMAT ROHIAN, MSC, FNENG, CISSP, PMP, CFE, ACP, TOGAF, LEAN SIX SIGMA
Hashmat Rohian is the Senior Director and Managing Enterprise Architect at The Co-operators Group. For over 10 years he has effectively led initiatives that have involved rapid advancement in data, analytics, digital, and business leadership. As a technology enthusiast and lean start-up evangelist, he is renowned for building a “never rest, kill complexity and care more” culture that manages existing technical debt and prepares the organization for future disruptions by leveraging emerging business models and digital ecosystems.
Hashmat is a published data science, IT, and agile delivery practitioner and academic with over 5 years teaching and curriculum development experience at colleges and universities in Canada. He has also organized conferences and workshops, and been interviewed on print, radio, social media and TV as an analytics expert.
|Winter 2019||Certificate in Machine Learning (Winter 2019)||$6,799.00||Register|
|Fall 2019||Certificate in Machine Learning (Fall 2019)||$6,799.00||Register|
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Students registered in certificate programs will be evaluated using the following categories of achievement:
|Grade||Grade Point||Per Cent Range||Description|
|E||1||(marginally below 50%)||Marginally Failing|
Note: all of the above-noted grades are used to calculate averages and credits.
Definitions of Grading Descriptions
A+ Exceptional. Thorough knowledge of concepts and/or techniques and exceptional skill or great originality in the use of those concepts/techniques in satisfying the requirements of an assignment or course.
A Excellent. Thorough knowledge of concepts and/or techniques together with a high degree of skill and/or some elements of originality in satisfying the requirements of an assignment or course.
B+ Very Good. Thorough knowledge of concepts and/or techniques together with a fairly high degree of skill in the use of those concepts/techniques in satisfying the requirements of an assignment or course.
B Good. Good level of knowledge of concepts and/or techniques together with considerable skill in using them to satisfy the requirements of an assignment or course.
C+ Competent. Acceptable level of knowledge of concepts and/or techniques together with considerable skill in using them to satisfy the requirements of an assignment or course.
C Fairly Competent. Acceptable level of knowledge of concepts and/or techniques together with some skill in using them to satisfy the requirements of an assignment or course.
D+ Passing. Slightly better than minimal knowledge of required concepts and/or techniques together with some ability to use them in satisfying the requirements of an assignment or course.
D Barely Passing. Minimum knowledge of concepts and/or techniques needed to satisfy the requirements of an assignment or course.
E Marginally Failing.
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