Hello everyone! 👋
I'm Nathishwar C, an aspiring AI/ML engineer and current Python Developer Intern. If you had told me two years ago that I'd be building machine learning models and working on AI-based projects, I would've laughed nervously and said, "That stuff is way too complicated for me."
But here I am, writing this blog — not as an expert, but as someone who went from being completely lost in the world of machine learning to confidently solving real-world problems using it.
I hope my journey helps those who are just starting or feeling overwhelmed. Remember, we all start somewhere! 👇
🌱 The Beginning: Intimidation and Overwhelm
My journey began like most — with curiosity.
🔹 The reality:
I kept hearing about "machine learning," "AI models," and how they're changing the world. I was excited but equally overwhelmed. The first time I opened a YouTube tutorial on Linear Regression, I understood nothing. The math flew over my head, and the code looked like alien text.
🧠 My struggle:
Even worse, I kept comparing myself to others — especially those who seemed to understand everything instantly. That self-doubt? It's real. I thought maybe this wasn't for me.
📚 The Learning Curve: Trial, Error, and a Lot of Googling
But something inside me kept pushing.
🔹 My approach:
I started small. Really small. I began with basic Python — learning syntax, playing with lists and loops. Then I took a free online course on ML — which I watched twice before even attempting the assignments. Every time I failed a quiz or my code threw an error, I reminded myself: "Struggling is part of learning."
🔍 What it looked like:
I can't count how many hours I spent debugging code or re-watching a concept until it finally clicked. Terms like overfitting, gradient descent, and cross-validation were intimidating at first. But slowly, with practice and consistent effort, they started making sense.
🛠️ Building Projects: From Theory to Real Impact
The real turning point came when I stopped focusing only on theory and started building.
🔹 Project progression:
My first project was a simple spam classifier. It didn't win any awards, but it gave me confidence. Then came more complex ones — a hand gesture recognition system using CNN and OpenCV during my internship, and later, an AI-based price predictor for used cars.
🎯 The lesson:
Each project taught me something new — not just technical skills, but also how to think critically, debug with patience, and communicate results.
💬 What No One Tells You: The Mental Game
Here's what most people don't talk about: learning ML is as much a mental challenge as it is a technical one.
🔹 The hidden truth:
There were days I felt like giving up. Days when nothing worked. But I learned to celebrate the small wins — understanding a tricky concept, fixing a bug, or simply finishing a tutorial.
👤 Personal confession:
Imposter syndrome? I still face it. But I remind myself how far I've come — from being confused by a simple algorithm to now confidently discussing model accuracy, optimization, and deployment.
🌟 Final Thoughts: Keep Going, Keep Growing
If you're starting out and feeling lost — you are not alone. Everyone struggles. Everyone Googles basic stuff. What matters is that you keep going.
📌 Here's what I've learned:
- • Consistency beats talent when talent doesn't work hard.
- • Understanding builds gradually, not overnight.
- • Real learning happens when facing challenges, not when things are easy.
Today, I proudly introduce myself as an aspiring AI/ML engineer. I still have a long way to go, but I now enjoy the learning process instead of fearing it.
So to anyone reading this — confused, overwhelmed, or doubting yourself — trust me: confidence comes with consistency. You've got this.
Let your journey begin ✨
"The difference between a novice and an expert is that the expert has failed more times than the novice has even tried."
— Anonymous
