How Was My First Steps in Machine Learning: Lessons and Surprises
As someone with a background in industrial engineering, I had always been attracted by solving complex problems and searching for the way to enhance the efficiency of systems. When I, about a year ago for the first time, started exploring machine learning, I knew it was a powerful tool with enormous potential, though at that time, I did not realize fully how it would expand my perspective. It has been an exciting journey, challenging and full of unexpected lessons. In this post, I'll share a bit about what I've learned and some surprises that came along the way.
Finding a New Kind of Problem-Solving
In industrial engineering, we're often taught to look at systems and find ways to make them more
effective. Machine learning introduced me to a different type of problem-solving: training models on
data to find patterns or predict outcomes without needing to program the ”rules” ourselves. This concept
felt completely different from traditional approaches, and it opened up a lot of possibilities that I
hadn't considered before.
The transition wasn't always easy, though. I found that machine learning requires a balance of
technical
skill and experimentation. Understanding how to work with algorithms and data was one thing, but tuning
models and interpreting results, required patience and persistence. I learned quickly that machine
learning is not a “one-size-fits-all” approach.
The Power of Data—and the Challenges It Brings
Another eye-opening aspect of machine learning was how much the quality of data mattered. In traditional
engineering projects, we work with precise measurements and detailed plans; Machine learning relies so
extensively on data that, until relatively recently, I came to realize how messy or incomplete data
might affect results. There's an entire process to handling data: cleaning it, preprocessing it and
sometimes collecting more. This was both a challenge and a lesson, because it proved that data is the
backbone of success in machine learning models.
Working with data also taught me about the importance of metrics and evaluation. I came to realize that
accuracy alone doesn't always tell the full story, especially on complex models. Evaluation metrics
like
F1-score became critical tools to ensure that my models wouldn't just stop at accurate predictions
but
meaningful ones.
Building a “Growth Mindset” in Machine Learning
Perhaps one of the biggest surprises for me was the mindset shift that machine learning encouraged. This
field moves fast: new tools, frameworks, and techniques are constantly emerging. At first, it was
overwhelming, but I came to accept this fast pace as part of the learning process. Adopting a “growth
mindset” has been key for me. I've learned to see each challenge as an opportunity to build new
skills
and deepen my understanding.
Learning machine learning has taught me that it's okay to be a beginner again. Every project has
its own
learning curve, and even the mistakes I made taught me something valuable. Building this mindset has not
only helped me with machine learning but also in approaching challenges in other areas of my work.
Looking Forward
My journey in machine learning is just beginning, but already it's broadened my view of what's possible and equipped me with skills which I never anticipated. The more I explore machine learning, the more I look forward to see how to apply these techniques in solving real-world problems.