I Finished Machine Learning. And Then Changed The Plan.

A few months ago, I had no idea what feature engineering was. Today, I finished my Machine Learning roadmap. Not "mastered ML." Not "became an AI expert." Finished the part where every tutorial starts making sense. I built projects, broke models, overfit them, leaked data into them, fixed them, and slowly started understanding why things worked instead of blindly following notebooks. My latest project: Predicting which customers are likely to leave a company before they actually do. Built with: Project: Customer Churn Prediction Demo The funny thing? The more I learned, the less interested I became in rushing toward Deep Learning. Originally the plan was: ML → Deep Learning → NLP But somewhere along the way I realized something. I don't just want to understand models. I want to build products. Things people actually use. So the roadmap changed. Now I'm diving into: Deep Learning isn't gone. It's just waiting its turn. And DSA? That was supposed to stay consistent. Instead, I keep finding myself opening AI documentation at 2 AM and disappearing into another rabbit hole. Not because I have to. Because I genuinely can't stop. Somewhere between building projects and studying, curiosity quietly took over. So that's where we are now. Machine Learning: complete. Next stop: GenAI. Let's see how deep this rabbit hole goes. If you're earlier in your journey: Build projects before you feel ready. Most of what I learned came from fixing mistakes I didn't know I was making. Project: Customer Churn Prediction Demo