A Personal Story of Grit, Google, and a Last-Minute Scholarship
Learning on your own can feel overwhelming, especially when you don’t know where to start. That’s exactly how I felt when I first tried diving into AI. I knew it was a trending topic, but the sheer volume of concepts, tools, and tutorials made it hard to take the first step. I had no clear direction.
Until one day, while casually scrolling Instagram, I came across a post about a free AI scholarship by Dicoding x Laskar AI. I paused.
“Free?” I thought. “Why not?”
I googled the program immediately and , to my surprise the registration closed that very day. I decided to give it a shot, thinking it was just a basic form to fill out. Turns out… there was an entrance test. Yep.
Still, I followed my instinct and tried anyway.
The test was mostly about basic algorithms and a bit of AI theory. Since I had a tech background, it wasn’t too hard and thankfully, I finished it in time and… I passed.
🧭 Walking Blindly Into the Program
At that point, I had no idea what I signed up for. I just followed the instructions step-by-step, unsure what to expect from Laskar AI. I assumed it would just be self-paced learning with videos.
Well… I was wrong.
The hard part wasn’t the AI material itself, it was everything else around it. The regular mentoring sessions, consultations, group check-ins, as an introvert who prefers learning quietly with clear structure, that was the real challenge.
😅 The Struggle of Staying Involved
I don’t make friends easily. For most of the program, I kept to myself and simply followed the modules. It wasn’t until the very end during the Capstone Project that I actually connected with a team.
I was the only girl in a group of three brilliant guys. At first, I felt intimidated. But I reminded myself that I had something to contribute too. Thankfully, my teammates were incredibly kind and supportive. One of them, in particular, took the lead and contributed the most, I admire his drive and I also learned a lot from observing how he worked.
🚧 Almost Gave Up
Toward the end, we had to complete an individual final project using MLflow, which meant building an entire Machine Learning pipeline, training, tracking, deploying.
It. Was. Exhausting.
I failed multiple submissions. I revised and tried again, sometimes with no clue what went wrong. But I didn’t want to quit. After all that effort, giving up would mean letting down not just myself, but the version of me who applied with zero expectations and got this far.
Eventually, after lots of rejections and long days, I got it done.
🎓 The Result That Didn’t Matter (At First)
By the end, I didn’t even care whether I passed or not. What mattered to me was that I had learned the core principles of AI , not just the theory, but real hands-on experience.
But guess what?
I passed.
I was one of the selected few out of over 12,000 applicants only 600 were accepted, and many dropped out during the process. I don’t know how many of us finished, but I’m proud to say I was one of them.
💡 Final Thoughts & Honest Advice
- You don’t need expensive courses to learn AI.
- Use what’s out there: Dicoding, YouTube, GitHub repos, Kaggle.
- But more importantly: You need persistence more than intelligence.
- You don’t have to be extroverted or a genius , just curious, consistent, and willing to fail forward.
I’m still learning, still growing , but if someone like me can do it, you can too.
✨ AI is not just about machines that learn, it’s about people who refuse to stop learning.