I don't know if this is the right place to share this... But I wanted to share a small realization I had. For the past few weeks, I've been documenting my Machine Learning journey here on LinkedIn. Nothing fancy. Just sharing what I'm learning and trying to explain complex concepts in simple words. Recently, two of my posts were liked by 𝗥𝗮𝗽𝗵𝗮𝗲𝗹 𝗕𝘂𝗰𝗸, the Founder & CEO of Get Inspired. The interesting part? He lives around 𝟲,𝟲𝟬𝟬 𝗸𝗺 𝗮𝘄𝗮𝘆 in Zurich, Switzerland. I don't follow him. Before this, I didn't even know who he was. Yet somehow, my post reached him. And that made me realize something. 𝗧𝗵𝗮𝘁'𝘀 𝘁𝗵𝗲 𝗽𝗼𝘄𝗲𝗿 𝗼𝗳 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗮𝗻 𝗼𝗻𝗹𝗶𝗻𝗲 𝗽𝗿𝗲𝘀𝗲𝗻𝗰𝗲. When you consistently share your work, your ideas can reach people you've never met, in places you've never been. Not because you're famous. But because the internet gives everyone a chance to be discovered. Will every post go viral? No. Will every post create an opportunity? Probably not. But every post increases the chances that the **right person** might come across your work one day. So if you're learning something... Don't wait until you become an expert. Share your journey. You never know who's reading silently on the other side of the world. (And who knows... maybe one day even Sundar Pichai comes across one of our posts. 😂)

Welcome to the Codebasics AI Community 👋 Glad you are here. This is a space for anyone who wants to actually use AI, not just read about it. This is where we go deeper: you learn, you try things, and you share what you build with people working toward the same goal as you. How it works here: 1. Learn from our videos and from what other members are building 2. Try things out and start doing your own work 3. Share what you made, even if it is small or unfinished 4. Help each other and grow together No question is too basic here. If you are stuck, ask. Do this now to get started: 👉 comment to this post with: - Your name and what you do - Where are you from - One task you wish AI could do for you - One thing which is outside of work about you That is it. Say hi, and let us build together.

My mom always says, "If you learn something early in the morning, you tend to remember it for longer." So today, I decided to learn some AI jargon. Task : pick 3 terms from the video. In your own words, write a one-line meaning for each. If you cannot, ask your AI chat to explain it, then rewrite it your way. What I did is i first explain my answer to the ai tool and asked him to improve it based on the context and then asked him to make it one liner My Prompt: Prompt : It is the command we give to llm. RAG: It is a technique which retrieves answer by looking at knowledge base without memorizing or learning for it hence it is fast. AI Agents/Agentic AI : AI agents not only tells how to do it but can also do the task on your behalf. Response from AI : 1. Prompt A prompt is the instruction, question, or context we give an LLM to tell it what we want. 2. RAG (Retrieval-Augmented Generation) RAG is a technique where an AI retrieves relevant information from an external knowledge base before generating a response. 3. AI Agents (Agentic AI) Definition (One-liner): An AI agent is an AI system that can plan, make decisions, use tools, and complete tasks on your behalf. Woohooo AI is way smarter than me 😁 I also made some handwritten notes because, as mom says, "If you write things down, you understand and remember them better." Once again, my mom's advice came in handy.

Excited to learn about the ai jargons which makes understanding them easily

I guess I'm the first one to post in the Wins & Progress channel! That counts as a win in itself, right? So, here is my update for Day 1 of the 10-Day AI Kickstart. The task was to ask an AI tool about our line of work and have it explain it like I'm five (which I'm definitely not!). My prompt was: "Explain what a Machine Learning Engineer does like I am five in simple, short terms, and with a small example." I honestly thought it would be a bit difficult to explain the role of ML to a five-year-old, but AI is pretty smart! 😁 Here is the answer the AI gave me: Imagine you have a smart robot 🤖. A Machine Learning Engineer teaches the robot to learn from examples instead of giving it every rule. Simple Example: You show the robot 100 pictures of cats 🐱 and dogs 🐶. "This is a cat." "This is a dog." "This is a cat." After seeing many examples, the robot learns the pattern. Now when you show it a new picture, it can say: "I think this is a cat!" 🐱 The Machine Learning Engineer: Collects the pictures (data). Teaches the computer using that data (trains a model). Checks if it learned correctly. Puts it into an app so people can use it. Pretty cool, right? AI is amazing, but I went a step further and asked it to convert the answer into an infographic since we usually understand things better visually. That's it for me wrapping up the first day!