From Zero to AI-Ready: How I Taught Myself Machine Learning (And What I would Tell You Now)

Even with my fancy master’s degree, none of this felt easy at the beginning. That’s how I know degrees aren’t what make this work click, curiosity, practice, and patience do. I didn’t start with confidence. I started with Google.

\ When I first heard terms like "neural networks" or "support vector machines," I genuinely thought machine learning was off-limits unless you were a genius. It looked complex, mathematical, intimidating. I thought: "This isn’t for people like me."

\ But now, years later, I am a data analyst and machine learning practitioner. Not because I magically “got it,” but because I took it slow, piece by piece, mistake by mistake. This is the story I wish someone had told me when I began: that machine learning isn’t about being smart. It’s about being persistent, curious, and patient.

\ So, if you’re reading this, wondering, “Can I really learn AI?”  the answer is yes. Here’s how I did it and how you can, too.

\ Before I wrote a single line of code, I had to understand what I was even asking the machine to do. So, I started with the world around the model, not the model itself.

\ I picked up basic math again. Not PhD math. Just simple things: how averages work, what “probability” really means, how to spot patterns in numbers. Then I got curious about data, and that’s when it clicked.

\ I realized that learning machine learning is like learning to cook. You don’t start with a Michelin-star recipe. You start with eggs and toast. Small, simple ingredients. You burn some. You try again. And slowly, it becomes second nature.

\ Python became my first kitchen tool. I didn’t try to learn everything; I just learned enough to ask questions, test things, and see what came back. That loop question, try, see, fix is still how I learn today.

\ When I finally tried building my first ML model, I didn’t even call it that. I just tried to make a prediction: “Will this person buy this product?” I used a dataset I didn’t understand fully, a model I copied from StackOverflow, and an accuracy I couldn't explain. But it ran. And it taught me something no tutorial ever could:

\ I started treating every project like a puzzle. If it broke, I didn’t panic. I would change one thing and try again. I was not chasing perfection; I was chasing understanding.

\ Eventually, I got to the big stuff: neural nets, computer vision, and transformers. But it wasn’t a leap, it was a series of tiny steps. And every one of them taught me how to think better, ask sharper questions, and debug my own assumptions.

\ Here’s the truth: the scariest part of learning AI is starting. But once you start, once you hit “run” on your first script and see your code do something, it becomes addictive.

\ And you realize: “Wait. I can actually do this.”