The course consists of 10 lessons that cover the fundamentals of building AI agents. Each lesson stands alone, so start wherever you like! ✨ [Link to the course in comments!] Here's what it covers: 🏁 Intro to AI Agents 🔍 Agentic Frameworks 🧠 Agentic Design Patterns 🔨 Tool Use Design Pattern 🔗 Agentic RAG 🛡️ Building Trustworthy AI Agents 🗺️ Planning Design Pattern 🤝 Multi-Agent Design Pattern 💭 Metacognition Design Pattern 🚀 AI Agents in Production

Hello everyone! I graduated three years ago and have spent the past year and a half self-studying embedded software programming. Initially, I planned to pursue a career as an embedded software engineer. However, with the rise of AI, I’ve been questioning whether continuing down the embedded path is the right choice. In my current role in the after-sales service department, I’ve been involved in AI project development, which I find incredibly interesting, but I feel I lack the foundational knowledge and skills in AI. While I have worked on a smart lock project using STM32, MQTT server, and FreeRTOS, I’ve never applied Python to a real project, and my embedded learning hasn’t yet resulted in tangible, validated outcomes. I find myself stuck in a cycle of learning without seeing concrete results, and I’m concerned that switching to AI would only add to my uncertainty because I haven’t yet fully applied the skills I’ve learned in embedded systems. I aspire to: 1. Extract data from databases like SQL or Snowflake and move it to a local data lake. 2. Process large datasets (millions of rows). 3. Leverage AI models, specifically large language models, to analyze long service ticket texts, summarizing key information like the issue (in no more than 5 words), the actions taken, and the solution. 4. Combine this AI output with the original data to create a web app that provides insights on common issues, most time-consuming problems, etc. I’m wondering if anyone else has faced this dilemma: should I stick with my embedded software engineer path, or is it time to fully embrace AI? And if I do transition, what specific skills should I focus on, what learning path should I follow, and what projects should I pursue to gain hands-on experience in AI? I’d love to hear your thoughts and any advice you might have. Thanks in advance!

Hey everyone, I just completed a new course for you: "Data Science Accelerator". This course will be unlocked, together with "Building Applications with LLMs" at level 3. How to level up? Just interact with the group, get likes and comments, and watch your level go up!

The goal of this group is to help you navigate the complex and rapidly evolving world of data science and artificial intelligence. This is your hub to stay up-to-date on the latest trends, learn specialized skills to turn raw data into valuable insights, connect with a community of like-minded individuals, and ultimately, become a Data Alchemist. Together, let's decode the language of data and shape a future where knowledge and community illuminate our way. Rules - Don't sell anything here or use Data Alchemy as any kind of funnel - We delete low effort community posts, and posts with poor English. Proofread your post first. - Help us make the posts high quality. If you see a low quality post, then click on the 3 dots on the post and "Report To Admins". Start by checking out these links - Classroom - Introduction - Roadmap - Contribution Be Aware of Scammers - Please be aware that this is a public group. Unfortunately, some people abuse the Skool platform to send DMs or post comments to trick people. This is the internet, so always do your own due diligence. Never automatically trust someone here on the Skool platform other than @Dave Ebbelaar's official account. To kick things off, please comment below, introducing yourself. Let us know: 1. Your name and where you're from 2. What project(s) you're currently focused on See you in the comments!