Let’s Write and Grow Together
If you’ve ever used ChatGPT, Gemini, or Claude, you’ve already interacted with what’s called a Large Language Model (LLM).
But have you ever wondered — how do these models actually work? How can they write essays, answer questions, or even generate code like humans?
Let’s break it down step by step — in simple language.
If you’ve been following the rise of Artificial Intelligence, you might have heard new buzzwords like AI Agents and Agentic AI
These terms sound fancy — but what do they actually mean? Let’s break them down in simple words.
If you’ve been following the world of Artificial Intelligence lately, you’ve probably heard the term “Generative AI” everywhere — from ChatGPT to AI art to music generation.
But what exactly is Generative AI?
How is it different from traditional AI or Machine Learning?
Let’s break it down step by step in simple language.
When learning Machine Learning, two of the first terms you’ll encounter are Supervised Learning and Unsupervised Learning.
These are the two main ways machines learn from data — and understanding their difference is key to mastering AI.
If you’ve just started exploring AI and ML, you’ve probably come across terms like Statistical Learning and Deep Learning. They sound similar, but they actually represent two very different approaches to solving problems with data.
When people hear Artificial Intelligence (AI), they often think of Machine Learning (ML) — and sometimes use the two terms interchangeably. But while they’re closely related, AI and ML are not the same.
Artificial Intelligence (AI) is evolving fast, and one of the most exciting areas today is Agentic AI — systems that don’t just generate responses, but also plan, take actions, and use tools like a real assistant.
If you’re new to this space, the learning path can feel overwhelming. That’s why I’ve created a step-by-step roadmap that will help you go from zero to building Agentic AI applications.
The day I joined my first project as a fresher in an MNC, I thought I’d start slow — maybe shadow someone, understand the system, or assist with small tasks.
But no.
I was handed a 4,000-line shell script — the main script of the entire module — and told:
“Go through this today. Share your understanding tomorrow.”
Are you interested in transitioning into data engineering, even though your background is not in technology? You’re not alone. Many people from fields like business, healthcare, or the arts dream of harnessing the power of data but worry that their lack of technical experience will hold them back. The good news: breaking into data engineering is absolutely possible—with a roadmap and determination.
If you’d asked me in college what I’d become after graduation, I’d have probably said:
“Maybe a Python content writer… definitely not a developer.”
I wasn’t into competitive programming.
I was somewhere between a “basic coder” and a “kuch toh aata hai” coder.
But life — and career — had different plans for me.
Connect 1:1 With Me: Schedule Call
If you have any doubts or would like to discuss anything related to this blog, feel free to reach out to me. I'm here to help! You can schedule a call by clicking on the above given link.
I'm looking forward to hearing from you and assisting you with any inquiries you may have. Your understanding and engagement are important to me!
This will close in 20 seconds