Machine learning is no longer just a foundational skill - it powers today’s most advanced AI systems.
AI is rapidly evolving, shifting not just toward new models, but new ways of working with data. From predictive analytics to large language models, machine learning sits at the core of how insights are generated, decisions are made, and workflows are automated.
Join this free workshop on April 22, 2026 from 12 - 1 p.m. ET to gain a practical introduction to machine learning in today’s AI landscape, including how core ML concepts such as generative AI underpin modern systems, and how these approaches are accelerating data science across industries.
We’ll explore how machine learning fits alongside newer AI tools, what’s changing in real-world workflows, and why foundational ML knowledge remains critical as automation increases.
You’ll also see a live demonstration of machine learning in action and learn how WatSPEED’s Data Science Certificate and AI Certificate programs can help you build applied, in-demand skills.
What you will learn:
- Machine learning fundamentals: How models learn from data, and why these concepts remain essential in today’s AI landscape
- Shift to modern AI systems: How generative AI and foundation models extend core machine learning
- Supervised vs. unsupervised ML: When to use each approach across real-world data science problems
- Evolving data science workflows: How automation, AI-assisted tools, and new model types are changing how work gets done
- Real-world applications: Where machine learning and AI drive impact across analytics, prediction, automation, and decision-making
Meet your workshop hosts
Larry Simon
Data Science Certificate Lead
Larry Simon is an entrepreneur, management consultant, and angel investor, specializing in IT strategy and data analytics. He is the founder and managing director of Inflection Group and has over 30 years of experience advising startups, global corporations, and government institutions.
Larry was previously a partner with Ernst & Young Consulting, their CTO and national director of their strategy and delivery centres. Larry is the lead for the Data Science certificate program, which is offered in partnership with the University of Toronto School of Continuing Studies.
Delina Ivanova
Director, Growth Data Science | Wealthsimple
With over 12 years of experience in data and analytics, Delina Ivanova has expertise as both an individual contributor and leader across multiple industries, including tech, CPG, banking, and consulting. Her domains of expertise span marketing, product management, supply chain, and operations. Currently, Delina is the director of Growth data science at Wealthsimple, having previously served as director of analytics at Mistplay and associate director of data and analytics at HelloFresh.
Delina is also passionate about sharing her knowledge, and currently teaches data science and machine learning at WatSPEED at the University of Waterloo, Schulich School of Business, and the University of Toronto School of Continuing Studies.
You may also be interested in:
Machine Learning Practitioner Certificate
Harness the power of data like never before. From understanding complex algorithms to adopting large language models into your work, our programs provide the knowledge and skills necessary to thrive amidst an ongoing data and AI revolution.
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FAQ
Who is this workshop for?
This session is designed for professionals exploring data science, machine learning,
or AI. It is suitable for beginners, as well as those looking to understand how newer
AI technologies relate to core machine learning concepts.
Is this webinar free?
Yes. This is a complimentary online workshop offered by WatSPEED at the University
of Waterloo.
Is this webinar recorded?
Yes. A recording will be shared with registered participants after the session.
How long is the webinar?
The session will run for approximately 60 minutes, including time for audience Q&A.
How do I attend?
After registering, you will receive a confirmation email with the Zoom access link
prior to the event.
Do I need prior experience in machine learning or programming to attend this workshop?
No prior machine learning or programming experience is required for this workshop.
Basic familiarity with data or programming is helpful but not necessary - concepts
will be explained clearly with practical examples.
What is the difference between machine learning and generative AI?
Machine learning is the broader field focused on building models that learn from data.
Generative AI, including large language models, is a subset of AI that builds on machine
learning to generate content such as text, images, or code.
Will this workshop cover tools like ChatGPT or large language models?
Yes. The session will provide context on how tools like ChatGPT and other AI systems are powered by machine learning, and how they fit into the broader
data science landscape.
Is this workshop technical?
The session is designed to be accessible while still providing meaningful technical
insight. It focuses on concepts, applications, and a practical demonstration rather
than deep mathematical theory.
How does this relate to data science careers?
Machine learning is a core skill in data science roles. Understanding how models work,
and how modern AI tools are evolving, is critical for roles in analytics, data science,
AI, and technical product development.
What will I be able to do after attending?
You will have a clear understanding of:
- How machine learning works
- How it connects to modern AI tools
- What skills are needed to move forward in data science or AI
Does this webinar relate to a WatSPEED program or course?
Yes. This webinar introduces concepts covered in WatSPEED’s AI Certificate Technical
Track, including the course Machine Learning, for those interested in deeper, applied
learning.
How do I continue learning after this session?
You can continue your learning through WatSPEED’s Data Science Certificate and AI
Certificate programs, which provide structured, applied training aligned with industry
needs.
How can I get started in data science or AI if I have no programming background?
If you’re new to coding, a strong starting point is Python I, which introduces core programming concepts in a structured, beginner-friendly way.
From there, Python 2: Data Science and AI Applications will help you apply Python to real-world data analysis, visualization, and introductory
machine learning.
This progression provides a clear, practical pathway into data science and AI, especially for those transitioning from non-technical backgrounds: Python I → Python 2 → Data Science Certificate → AI Certificate
What is machine learning in simple terms?
Machine learning is a method of teaching computers to learn from data and improve
over time without being explicitly programmed. It is widely used in applications such
as recommendations, forecasting, fraud detection, and automation.
How is machine learning used in real-world applications?
Machine learning is used across industries to solve practical problems, including
customer segmentation, demand forecasting, anomaly detection, recommendation systems,
and predictive analytics. It also powers many modern AI tools and platforms.
How does machine learning relate to artificial intelligence (AI)?
Machine learning is a core subset of artificial intelligence. It provides the techniques
that allow AI systems to learn from data, recognize patterns, and make decisions.
Most modern AI applications, including generative AI, are built on machine learning
foundations.
What are large language models (LLMs), and how do they work?
Large language models (LLMs) are AI systems trained on vast amounts of text data to
understand and generate human-like language. They are powered by advanced machine
learning techniques and are used in tools for writing, coding, analysis, and decision
support.
Why is machine learning still important with the rise of generative AI?
While generative AI has introduced new capabilities, it is still built on machine
learning principles. Understanding machine learning helps you better evaluate, use,
and apply modern AI tools effectively in real-world scenarios.
What skills are in demand for careers in AI and data science?
In-demand skills include machine learning, data analysis, Python programming, statistics,
data visualization, and an understanding of modern AI tools such as large language
models and automation frameworks.
Is machine learning a good career path in 2026 and beyond?
Yes. Demand for machine learning and AI skills continues to grow across industries.
Organizations are increasingly looking for professionals who can work with data, build
models, and apply AI to real business problems.