This week, I completed the Stanford Machine Learning Specialization on Coursera. A course I’ve wanted to take for a long time. While I’ve led many tech projects and managed engineering teams, I’ve always believed that real leadership comes with staying curious, adaptable, and ahead of the curve. For me, diving into AI wasn’t just about expanding my skillset — it was about preparing for the future of tech leadership.

I’ve always been driven by systems thinking — connecting dots between users, products, and technology. As AI continues to transform how decisions are made and how products are built, I knew I needed to go deeper. Not just to understand the tech behind the buzzwords, but to be able to build strategies, lead teams, and make decisions that are grounded in real understanding.

This course laid a strong theoretical foundation, and for the first time in a while, I felt both like a student again and a future architect of what’s next.

What I Learned (and Why It Matters)

Some of the core ideas I took away:

• Supervised vs. Unsupervised Learning: Not just as algorithms, but how to choose the right approach when optimizing for clarity vs discovery.
• Linear Regression & Logistic Regression: Simple doesn’t mean outdated. These models still hold powerful explanatory value in many real-world use cases.
• Loss Functions & Gradient Descent: The mathematical engine behind machine learning — and also a perfect metaphor for iterative improvement in teams and product decisions.
• Bias-Variance Tradeoff: A concept I now think about beyond models, even in leadership and hiring decisions.

The way these lessons connected back to how I’ve approached problems as a tech leader gave me new language and new tools.

What Challenged Me the Most

Revisiting foundational math concepts (hello, multivariable calculus!) wasn’t easy. I had to pace myself and give extra time to truly understand how gradient descent works from the inside out. But it felt worth it — and reminded me of something I used to tell junior developers: “The hard stuff is often the real stuff.”

What’s Next

Now that I’ve got the theory in place, I’m rolling up my sleeves. I’ve started a hands-on Udemy course focused on training and deploying ML models. My goal is to fill my GitHub with real, reusable projects. Models that businesses can apply to personalization, churn prediction, fraud detection, and more.

I’ve also enrolled in a 9-week course from Illinois Tech that explores AI in a business context, looking at AI through the lens of strategic decision-making, operations, and digital transformation. This aligns perfectly with where I want to go: leading engineering teams and shaping products that use AI responsibly and smartly.

Looking Ahead

As I pivot toward more senior tech leadership roles — ideally as an Engineering Manager or even Head of Engineering — I want to combine my management experience with AI strategy. The tech industry needs more leaders who understand both people and data, not just one or the other.

This isn’t just about getting a better job. It’s about becoming the kind of leader I wish I’d had more of early in my career: someone who builds bridges between business and engineering, between innovation and responsibility.