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From late nights building courses to reaching over 500,000 learners globally — today, School of AI is ranked as the #2 Popular AI Instructor on Udemy. It’s honestly surreal to see School of AI listed alongside some of the biggest names in online education. What started as a mission to make AI practical, hands-on, and accessible has now grown into a global learning movement. Over the last few years, I focused on one thing relentlessly: → Teaching AI in a way that helps people actually build → Breaking down complex AI concepts into practical systems → Creating real-world projects instead of just theory → Making AI education accessible to beginners, professionals, and builders worldwide This journey was never about chasing rankings. It was about helping people transition into the future of AI with confidence. Today: • 500,000+ learners • 193 AI courses • Thousands of projects built by students worldwide • A growing global AI community through School of AI And we’re only getting started. The next phase is even bigger:Agentic AI, AI Engineering, AI Governance, Autonomous Systems, AI Product Development, and real enterprise AI transformation. To every learner, supporter, and builder who trusted School of AI — thank you.Your support made this possible. The mission remains the same: Build practical AI education that creates real builders for the AI era. #AI #ArtificialIntelligence #Udemy #SchoolOfAI #AIAgents #GenerativeAI #MachineLearning #AIEngineering #OnlineLearning #TechEducation

The era of prompts is over. The era of Agentic AI systems has begun. I’m excited to introduce Agentic AI Mastery: Claude Code, Clawdbot & Beyond — a hands-on course designed for engineers and builders who want to move beyond chatbots and build real multi-agent, production-ready architectures. In this course, you’ll design event-driven systems, implement guardrails, build personal and enterprise agents, and ship a complete capstone project ready for your portfolio. If you want to think like an AI systems architect and build autonomous workflows that actually scale — this course is for you. The future is agentic. Let’s build it.

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Over the next few weeks, we're launching a video series that takes you end-to-end from "I just installed Ollama" to "I can design and ship serious, graph-based AI agents using LangChain and LangGraph, entirely on my own machine." Who This Is For This journey is designed for: - Developers and data practitioners who want hands-on experience building agentic AI systems - Startups prototyping AI features before committing to expensive API costs - Enterprise teams with data privacy requirements who need local-first solutions - Tech leaders and researchers who want to deeply understand agent architectures without being locked into hosted models Prerequisites Before starting, you should have: - Basic Python knowledge: functions, classes, pip, virtual environments - API/JSON familiarity: understanding request/response patterns - Terminal comfort: running commands, navigating directories - Hardware: 16GB+ RAM recommended; GPU optional but helpful for larger models What This Journey Covers Across the series, we'll walk through five major phases. Each phase ends with tangible projects you can run locally and adapt to your own use cases. --------------------------------------------------------------- Phase 0 – Local Stack: Ollama + Python Estimated time: 1-2 hours We begin by setting up a local AI sandbox: - Installing and configuring Ollama - Pulling and running popular open models (e.g., Llama, Mistral) - Understanding hardware requirements and model selection for different tasks - Creating a clean Python environment and wiring up a minimal script to talk to a local LLM By the end, you'll have a lightweight local playground where you can experiment without API keys or usage limits. Troubleshooting covered: Model selection guide, memory optimization, when local models shine vs. their limitations --------------------------------------------------------------- Phase 1 – LangChain Fundamentals with Local Models Estimated time: 3-4 hours Next, we introduce LangChain as the "capabilities layer" on top of your local model: