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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.
I was never a topper.
I was never a failure either.
I lived somewhere in the “decent marks zone” — the middle child of academia. Not loud enough to be noticed, not quiet enough to be forgotten.
But if you asked me what I really lacked during my school days, it wasn’t grades — it was confidence.
Python, with its simplicity and versatility, has become one of the most popular programming languages today. Whether you’re a beginner or an experienced developer, the abundance of free learning resources available online can help you master Python and unlock its full potential. In this blog post, we present a carefully curated selection of what we believe to be the best free resources available online. From YouTube channels to websites and offline tools, these resources are handpicked to empower you with the knowledge and skills needed to excel in Python programming. Let’s dive in!
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