A Complete Overview of LangChain, LangGraph, and LangSmith from the Basics

You will learn about RAG, VectorDB, LangChain, OpenAI, sLLM, and fine-tuning, which encompass the most important AI-related technologies at this time.

From PC control to high-level research and app development A guide to building your own 'AI Work Assistant' ready for immediate real-world deployment

AI is only as good as you are. Learn vibe coding properly in three steps: how to use AI → essential foundational knowledge → project development. For those who want to learn vibe coding the right way, we deliver the core concepts that remain unchanged regardless of which AI you use, broken down into bite-sized pieces.

We explore how to create an AI judge by combining knowledge graphs, reasoning systems, and various agent frameworks such as CrewAI, LangChain, and AutoGen, which enable the implementation of LLM-based multi-agent technology.

Master Claude Code from the basics to a professional level. Learn core features (CLAUDE.md · subagents · MCP · Hooks, etc.), develop and deploy a real-world service, and wrap up with foundational appendices and advanced-use lessons for non-majors.

Generative AI is rewriting how the world works — and the people who learn it now will lead the next decade. Every industry is being reshaped in real time. Marketers are creating campaigns in minutes. Developers are shipping code faster than ever. Entire teams are automating work that used to take days. And at the center of it all sits one skill that separates those who get left behind from those who get ahead: knowing how to actually use Generative AI. The problem? Most people are still only scratching the surface. They've typed a question into ChatGPT, gotten an average answer, and assumed that's all there is. But beneath that simple chat box is one of the most powerful technologies ever built — and once you understand how it works and how to direct it, you'll never work the same way again. That's exactly what this course is built to do. Introduction to Generative AI takes you from complete beginner to confident, capable AI user — no coding, no maths, and no prior experience required. In clear, jargon-free language, you'll discover what Generative AI really is, how it works under the hood, and how to put it to work in your job, your business, and your everyday life. This isn't a dry, theoretical lecture series. It's a practical, demo-driven journey packed with real examples you can follow and copy immediately. You'll start with the foundations — what Artificial Intelligence, Machine Learning, and Deep Learning actually mean, and how Generative AI fits into the bigger picture. You'll explore ChatGPT's features and capabilities hands-on, understand why this technology has exploded so quickly, and trace its remarkable evolution. From there, you'll demystify the engine that powers it all: Large Language Models. You'll see exactly how next-word prediction, embeddings, and fine-tuning work, and you'll understand game-changing concepts like Retrieval Augmented Generation (RAG) and the rise of Agentic AI — the difference between an assistant that answers and one that acts. Then comes the real superpower: prompt engineering. This is where most learners go from "AI is interesting" to "AI is indispensable." You won't just learn what prompting is — you'll master more than a dozen proven techniques used by professionals every day, including chain-of-thought prompting, zero/one/few-shot prompting, tabular prompting, perspective prompting, comparative prompting, reverse prompting, the RGC framework, and many more. You'll get ready-to-use prompt starters, learn how to revise and sharpen your prompts for dramatically better results, and discover how to generate fresh ideas, marketing copy, client emails, and content at scale. By the end, you'll know how to make AI do precisely what you want — consistently. You'll see it all in action. This course is loaded with practical demonstrations across real business scenarios. Watch AI summarise meetings and capture insights with an AI notetaker, draft polished email responses in seconds, and spin up multiple marketing headline variations from a single product image. You'll explore how Generative AI is transforming software development, retail, and marketing, and you'll see real tools at work — generating working code with GitHub Copilot and creating images and video with cutting-edge tools like Amazon Nova. These aren't hypotheticals. They're workflows you can start using the moment you finish the lesson. But great power demands responsibility — and this course doesn't skip the hard parts. You'll dive into Responsible AI and learn how to spot and manage hallucinations, factual inaccuracies, bias, and fairness issues. You'll understand the genuine technical limitations of today's models and the practical workarounds that experienced users rely on. Through real examples — including safety refusals and bias-correction rewrites — you'll learn to use these tools wisely, ethically, and with confidence, the way professionals and organisations are expected to. And you'll finish looking forward, not just at today. You'll discover how Generative AI is disrupting search and reshaping the way the world finds information, explore the road toward Artificial General Intelligence (AGI), and get a clear-eyed view of the jobs, certifications, and fast-growing career opportunities this field is creating. Whether you want to future-proof your career, boost your productivity, or build something new, you'll leave knowing exactly where this technology is heading and how to ride the wave. Why learn from this course? You'll be joining a community of over 207,000+ students taught across leading platforms, backed by a 4.6 instructor rating and 13,000+ reviews. Every concept is explained simply, every technique is shown in practice, and every lesson is designed to take you one step closer to genuine confidence with AI. No fluff. No overwhelm. Just clear, practical, up-to-date learning that respects your time. This course is for you if you're a beginner curious about AI, a professional who wants to work faster and smarter, a marketer, creator, developer, entrepreneur, or simply someone who refuses to be left behind by the biggest technology shift of our generation. If you can use a web browser, you can do this course — and you can start today. The gap between people who use AI and people who master it is widening every single day. The tools are here. The opportunity is now. The only question is which side of that gap you want to be on. Enrol today and start your journey from AI curious to AI confident — one practical, powerful lesson at a time.

This course covers the GitHub notebooks and bonus content for <Building an LLM from Scratch> (Gilbut, 2025). GitHub: https://github.com/rickiepark/llm-from-scratch/ <Building an LLM from Scratch> is the Korean translation of the bestseller <Build a Large Langauge Model (from Scratch)> (Manning, 2024) written by Sebastian Raschka. Starting from scratch and building a complete version of OpenAI’s GPT-2 model, this book provides a way to learn how large language models work and how to use them.

No Coding Skills Required: Build Your Very First Service with Google Stitch An All-in-One AI Development Workflow That Seamlessly Connects Every Step from Planning to Deployment

🔥Don’t have time to complete the 32-hour all-in-one course? We’ve condensed the four subjects of the Big Data Analysis Engineer written exam into around 4 hours. Connect concepts directly to actual exam questions through modified past exam questions, and receive 150 pages of lecture materials plus 13 pages of final review notes for the exam room.

From core theories of deep learning and CNN to various CNN model implementation methods, and practical deep learning development know-how through real-world problems, If you want to become a deep learning CNN technology expert based on Pytorch, join us in this lecture :)

Part I Practiced in code the process of loading a PDF as page-by-page Documents, chunking it, converting the chunks into vectors through embeddings, and indexing them in a vector store. Learned the structure of converting questions into vectors, performing similarity searches (including scores, vectors, and MMR), wrapping the process as a Retriever (Runnable), and connecting it to RAG. Established practical standards for application by organizing search quality checks, security and governance (including prompt injection), and common mistakes. Part II Implemented in R the random forest algorithm, which overcomes the limitations of decision trees, and learned how to accurately interpret model results. Built the ability to read and explain key metrics such as OOB error, variable importance, and confusion matrices using iris data and real-world biomarker data. Completed an end-to-end workflow applicable directly in practice, covering hyperparameter tuning, cross-validation, and ROC curve analysis.

Master the complete lifecycle of building modern Generative AI applications using Large Language Models, Retrieval-Augmented Generation, and Agentic AI systems. Learn to design enterprise-grade AI solutions from prompt engineering to deployment, combining LLMs with vector databases and external knowledge sources for production-ready applications.

Instead of using more agents, experience firsthand how to entrust them with greater confidence. With Codex CLI, build your own harness so you can delegate work without worry!

The online course “AI Teammates Do It All” is a hands-on program that teaches you how to use AI not merely as an automation tool, but as an “AI teammate” that works alongside you on practical tasks. The course is structured around common real-world workflows, including market research, proposal writing, strategy report creation, brand content visualization, project promotion and operations, and follow-up management. Participants assign various AI tools—including ChatGPT, Perplexity, Copilot, Canva, Gamma, MiriCanvas, and Vrew—as planning partners, strategic research partners, design partners, and operations support partners, and complete actual work deliverables with them. Across 30 sessions, you will learn step by step, from the basic mindset for working with AI to practical prompt writing, market research, proposal and report writing, brand content creation, and project operations. By following the course, you can develop the AI collaboration skills to work on your own like an entire team.

How long are you going to manually look through 2,500 stocks every day? Searching through Naver Finance to find surging stocks after the market closes, Reading news to judge whether it's good or bad news, Checking foreign/institutional supply and demand one by one, Opening charts to analyze patterns... Aren't you repeating this every single day? I used to do that too. I spent 2 to 3 hours after work analyzing stocks, and yet I still missed more stocks than I found. With over 2,500 stocks across KOSPI and KOSDAQ combined, it's impossible for a human to see them all every day. --- So, I built a system. I created a system that automatically analyzes 2,500 stocks every day after the market closes. - Automatically collects market prices, supply/demand, and news - AI (Gemini) reads the news and determines if it's positive or negative - Scores stocks out of 15 points based on 6 factors - Selects only the stocks that pass the criteria and calculates entry, stop-loss, and take-profit prices - Sends notifications via Telegram Now, I just check my phone after work. Additionally, every Saturday, the system analyzes its own performance from the past week and automatically adjusts stop-loss lines, take-profit lines, and holding periods. It is a structure where the system learns and improves on its own. --- But I am not a developer. I didn't write the code for this system myself. I gave all the instructions to the AI verbally. "Filter out stocks that rose more than 5% today with a trading volume exceeding 50 billion won." "Send these 3 news articles to Gemini and have it judge if they are positive." "Create a scheduler so this runs automatically every day at 4 PM." When I speak like this, the AI (Claude) generates the code. This is "Vibe Coding." --- In this course, we will build this exact system. Over 58 lessons, we will build the system I actually use every day from start to finish. Starting from data collection, AI news analysis, scoring engine, signal generation, Flask API server, Next.js web dashboard, Telegram automatic notifications, and even the self-learning system. The final product is not a Jupyter Notebook. The final product is a web dashboard and Telegram notifications that actually run every day. You don't need to know how to code. In every lesson, I will show you how to talk to Claude, and if you follow along, you will get the same results. --- Recommended for: - Office worker investors who lack the time to analyze stocks every day - Those who want an automated system but don't know how to code - Those curious about quant/system trading but don't know where to start - Those curious about how to utilize AI in practice --- ⚠️ This course does not guarantee investment returns. This is a programming course on building your own stock analysis tools. Actual investment decisions are the responsibility of the student.

For those of you who have completed intermediate SwiftUI, it is now time to integrate AI into your apps. Complete an on-device AI app that runs locally without an internet connection using Apple Intelligence and the FoundationModels framework.

The Gemini agent that works across Google Drive! Put the finishing touches on your Google AI workflow with overwhelming productivity.

Solve the point where 80% get stuck at makeathons. Complete Raspberry Pi · Computer Vision · LLM · 3D Design in 4 weeks! Achieve top rankings at makeathons with a demonstrable smart mirror PoC!

The purpose is to improve, compared with last year, awareness campaigns that are not defined in the regulations and often end merely with the distribution of promotional gifts, by referring to best practices from various organizations, given the current situation in which the performance of events and campaigns such as Information Security Day or Personal Information Protection Day, held monthly or annually, is gradually declining. These events must now move beyond gift-distribution activities organized by the Information Security Team and become events in which all employees participate and uphold security together. It is necessary to build mutual understanding through the establishment of a communication system for embedding security into the organization. Ultimately, employees must have a good understanding of information security and the Personal Information Protection Act so that the organization can realistically protect itself from hacking and insider threats. Since the departments other than the Security Team—including those responsible for commissioning various projects, managing entrusted service providers, controlling applications and security settings, planning various informatization projects, and conducting IT audits—are designated to take overall responsibility, the Security Team is distributing security-related tasks among the respective departments. However, the current situation is that each department is merely expressing dissatisfaction. This opportunity should therefore be used to move away from that situation, as internal consultative bodies alone are insufficient. Security-related scoring criteria must be reflected in the powerful personnel evaluation system. Through unannounced audits, those who fail to comply should be disciplined through pay reductions or suspension, while those who properly observe security requirements and demonstrate achievements in support of security should receive substantial financial rewards through an incentive system. Security must be embedded in the organization so that employees take the initiative themselves. Accordingly, department and individual evaluation KPIs should be adjusted through a report to the CEO under the direction of the CISO. In addition, through a system for designating department-level information security officers and personnel in charge, the organization should strengthen its human security framework against hacking and information leakage—including the leakage of documents managed by each department and the management of service providers—by providing position allowances for these responsibilities.

Find your way through the sea of data and explore the future of artificial intelligence. This course explores the two massive technological trends of Big Data and Artificial Intelligence (AI), examining how they are bringing innovation to our lives and work.