**Build Log: Zero to AI – Day 3**
Today was a mixed bag, but I feel like I’m finally starting to hit my stride! The goal for today was to make some real headway on the data preprocessing stage, and I’m happy to report that I managed to clean, organize, and even partially label the dataset I chose. Honestly, I didn’t expect it to take as long as it did; I underestimated the amount of cleaning up the raw data would require. Formatting issues, missing values, duplicates—you name it, I probably encountered it. It was slightly frustrating, but I think it’s all part of the learning curve.
On the bright side, I got to try out new tools I had been eyeing. I dove into some Python libraries like Pandas and NumPy. Once I got the hang of it, I couldn’t believe how easy it made my life! The visualization functions helped me quickly identify patterns and outliers, which I otherwise would have spent ages trying to find manually. This bit was surprisingly satisfying; nothing beats seeing your hard-won data transformed into clean, organized tables that actually make sense.
However, not everything went as planned. I ran into a hiccup with the labeling process. Ideally, I wanted to automate some of it using existing models, but they didn’t produce the accuracy I was looking for. I spent way too much time debugging, trying to figure out why I was getting such weird results. Turns out, the specific features I was trying to extract weren’t compatible. Mental note for tomorrow: be more mindful of feature selection!
Looking forward, tomorrow’s challenge will be to finalize the labeling and then dive into the actual model training. I’m envisioning starting with a basic framework and then iterating from there as I gather results. I’ll also want to do some research on different model architectures to see what might work best for the type of data I’m handling.
All in all, today was a productive day, even with the challenges. I’m starting to feel more confident in my ability to navigate this AI landscape. Let’s see what tomorrow holds!