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Overfitting, underfitting, and fitting — these aren't just technical terms, but critical checkpoints in every machine learning workflow. Understanding these concepts is key to evaluating model behavior, improving generalization, and building solutio

People want to jump into AI fast. But you can’t skip the basics. Learn system design before AI agent frameworks. Learn data cleaning before fine-tuning. Learn APIs before MCP. Learn databases before RAG. Learn real NLP before prompts. Learn classic

I spent 4 weeks interviewing 30 people who tried to fine-tune LLMs. Here's what I found:70% of them spent 2-4 weeks just preparing data. 60% copied hyperparameters from blog posts without understanding why. 53% abandoned projects because costs spiral

9 Steps to Build AI Agents from Scratch. I've given a simple step by step explanation. 𝗦𝘁𝗲𝗽 1: 𝗘𝘀𝘁𝗮𝗯𝗹𝗶𝘀𝗵 𝗠𝗶𝘀𝘀𝗶𝗼𝗻 & 𝗥𝗼𝗹𝗲 • Decide what problem the agent will solve. • Figure out who will use it. • Plan how users will interact

3 levels of human involvement in AI systems. I’ve explained each approach in simple steps below. 𝗛𝗜𝗧𝗟 (𝗛𝘂𝗺𝗮𝗻-𝗶𝗻-𝘁𝗵𝗲-𝗟𝗼𝗼𝗽) Humans are actively involved at every step, from collecting data to monitoring results and improving the mod

AIOps vs LLMOps vs MLOps. I’ve explained each approach in simple steps below. 𝗔𝗜𝗢𝗣𝗦 AIOps applies AI to monitor, detect, and fix problems in IT systems. 1. Decide what issue you want AI to solve (like preventing system crashes). 2. Collect l

Why aren't Indian AI startups dominating the global scene yet? As someone who's watched the tech world closely, let's break it down - India boasts insane talent pools from IITs and beyond, but we're still playing catch-up. Here's the real talk on wh

This single reel Gained 1Million+ Views and took us from 200 to 1700 Followers within just 5 days. This experience taught me something really interesting about how people interact with Instagram, and I'd love to share these insights with you. Hav