“This program didn’t just expand my technical knowledge. It helped me trust myself in spaces that once felt intimidating.” - Uzma Ahmad, SCS learner
Two years ago, Uzma Ahmad couldn’t have imagined contributing to predictive modeling projects or speaking confidently in technical discussions. Today, she’s doing exactly that in a new data-focused role, thanks in part to the Machine Learning program offered through the University of Toronto School of Continuing Studies in Partnership with WatSPEED at the University of Waterloo.
“I originally transitioned into data without a traditional technical background. I didn’t have everything mapped out. I was simply curious and willing to learn,” Ahmad shares.
“I remember being in a meeting where one simple data insight shifted the entire direction of the discussion. Opinions paused. The numbers grounded the conversation. That moment stayed with me. I realized data isn’t just reporting — it influences real decisions.”
As she became more comfortable with analysis, Ahmad became curious about how to move from examining what happened to anticipating what might happen next and how those predictions are built.
“I was also noticing predictive systems everywhere,” she says. “Recommendations, search suggestions, personalized content. Instead of just consuming them, I wanted to understand the mechanics behind them.”
That curiosity is what led her to enroll in Machine Learning as part of the U of T SCS and WatsSPEED Data Science certificate.
Once classes began, Ahmad found the structure made it easy to apply lessons immediately. Each course resulted in tangible projects she could add to her portfolio, reinforcing her progress from foundational concepts to more advanced modeling.
Classmates from healthcare, marketing, finance, tech, and other industries made for rich classroom discussions, and Ahmad says they demonstrated that Machine Learning isn’t a one-size-fits-all solution.
“Context shapes everything,” she explains.
“Those conversations helped me better understand where I see myself specializing and how adaptable these tools can be.”
Completing the Machine Learning course has already influenced Ahmad’s approach to her work, reshaping how she approaches uncertainty and decision-making.
“I no longer look at outputs without questioning inputs and assumptions. I think about edge cases, trade-offs, and real-world impact before drawing conclusions,” she says.
“It has also changed how I communicate insights. I focus more on explaining the reasoning behind results, not just presenting numbers.”
One of the ways the course prepared her for this was through a group project inspired by a marketing call Ahmad received. It prompted her to consider how predictive models could help call centre agents focus on the right prospects, improving efficiency and reducing pressure.
She shared that thought with her Machine Learning class group and they decided to build their project around it, developing a bank call centre prediction model to explore whether they could forecast outcomes in a way that supported smarter decision-making.
“What stood out to me wasn’t just the model itself, but the process. As a team, we had to agree on which approach made the most sense, compare models, evaluate performance metrics, and defend our choices. There were real discussions, not just about accuracy, but about assumptions and trade-offs,” says Ahmad.
“I remember looking at the final results and realizing I understood every part of what we had built: how it worked, where it might fail, and why we made certain decisions. Two years ago, I wouldn’t have known how to contribute meaningfully to a project like that. Being able to bring an idea forward, collaborate on it, and stand behind the technical decisions was a defining moment for me.”
Ahmad used that confidence, backed by her new skills and knowledge, to pursue data-focused roles more seriously. She says that directly contributed to landing her first role in the field. “That transition validated the work I had been putting in behind the scenes,” she adds.
“It also clarified that this is a long-term path for me. I’m completing my Data Science certification and plan to continue into the AI certification pathway next.”
Ahmad encourages anyone else considering taking a Machine Learning course at SCS to participate in discussions, connect with your classmates to create a valuable network, and emphasizes the importance of starting before you feel fully ready.
“You grow into it. The program gives you structure, but your progress comes from consistent effort and engagement. The assignments gradually build your portfolio, and before you realize it, you have tangible proof of your growth,” she says.
Reflecting on her own learning journey, Ahmad reflects on how far she’s come.
“This program didn’t just expand my technical knowledge,” she says.
“It helped me trust myself in spaces that once felt intimidating — and that’s something I’ll carry forward.”
For more information about the School of Continuing Studies Machine Learning certificates and courses, please visit our Machine Learning course page.
