LangChain on Azure - Building Scalable LLM Applications- (2026)

Notion is one of the most powerful tools for organizing work, projects, and knowledge—but many users struggle because they rely on templates without understanding how Notion actually works. In this course, I help students move beyond confusion and scattered setups by teaching Notion as a system. You’ll learn how to configure Notion correctly from day one, understand how blocks, pages, and databases truly function, and build workflows that remain clean, flexible, and scalable over time. Through clear explanations and practical examples, I guide you step by step—from the fundamentals of Notion to advanced features like databases, dashboards, automations, formulas, and Notion AI—so you can confidently design systems that support your productivity instead of slowing you down.

In this course, you will learn how to design, build, and evaluate advanced RAG systems using the LangChain framework. You will master LCEL, Runnables, advanced retrieval techniques, chunking strategies, cross-encoder reranking, agent-based RAG, tool calling, SQL integration, and safety techniques using NeMo Guardrails. You will also learn how to trace, debug, and deploy a full-stack AI chatbot with LangFuse, React, FastAPI, and Docker.

Mastering Model Context Protocol (MCP) is a practical, engineering-focused course designed to help developers build real, secure, and production-ready AI backends. After helping thousands of students overcome confusion around LLM integration, tool calling, and backend architecture, I created this course to solve the most common problems: “How do I build a reliable backend that LLMs can call safely?” “How do I choose between SSE, stdio, or streamable-http?” “How do I scale MCP into real applications with FastAPI, Auth0, and LangGraph?” “How do I structure my MCP tools, resources, prompts, and context?” In this course, I guide you step-by-step—from spinning up a minimal MCP server to deploying a fully secure, Dockerized system. Every lesson is hands-on, designed to remove complexity and give you a clear, repeatable workflow for building modern AI systems. If you're frustrated by vague tutorials and want a clear, concrete, engineering-level understanding of MCP, this course is built for you.

Course Summary: In this Masterclass, you’ll master AI-driven development using Claude Code, an agentic coding tool that fits naturally into real-world workflows. You’ll automate repetitive tasks with slash commands, create custom hooks, and adopt a spec-driven workflow to stay in control as projects grow. Learn how to extend Claude Code with MCP servers, plugins, and subagents, integrate it directly with GitHub and the web, and deploy applications on Vercel. This course solves developers’ productivity challenges and helps them leverage AI to build applications faster and more efficiently.

This course is designed to help absolute beginners learn Python through short, focused, and practical lessons. With daily bite-sized sessions of about 15 minutes, I guide students step by step through the core concepts of Python, making coding approachable and less overwhelming. Based on my experience helping beginners overcome common learning obstacles—such as confusion around syntax, logic, and problem-solving—I emphasize clarity, repetition, and hands-on practice. Each lesson builds on the previous one, reinforced by practical exercises and regular rest days for review and consolidation. By the end of the 30 days, students will not only understand Python fundamentals but will also confidently build their own command-line Python application, connecting all the concepts learned throughout the course.

What to Expect from This Course Welcome to LangGraph in Action, your ultimate guide to mastering the design and deployment of advanced AI agents using LangGraph. In this course, you’ll explore the fundamentals of building modular, scalable, and production-ready agents, all with a hands-on approach. From understanding the basics of LangGraph’s state-based design to creating a full-stack application, you’ll gain the skills needed to bring AI agents to life. Course Highlights State-Based Design: Dive into LangGraph’s core philosophy of nodes and edges to create structured, maintainable agents. Memory Management: Explore short-term memory with checkpointers and long-term memory with the Store object to enable agents that adapt and learn. Advanced Workflows: Build human-in-the-loop systems, implement parallel execution, and master multi-agent patterns. Production-Ready Development: Learn asynchronous operations, subgraphs, and create full-stack applications using FastAPI and Docker. By the end of the course, you’ll not only have a strong theoretical understanding but also the practical skills to deploy AI agents anywhere, entirely with open-source tools. Whether you're a developer aiming to stay ahead of the curve or a seasoned engineer looking to expand your AI toolkit, this course equips you for the rapidly growing field of AI agents. With the increasing adoption of AI agents in real-world applications, this course ensures you're prepared to design, build, and deploy advanced systems that solve practical challenges. Let’s start building and shaping the future of AI together!

It's structured so you can learn many parts of Kubernetes (☸) through illustrations. 🧭 In particular, it was written to provide even deeper learning of Kubernetes itself by expanding on the already proven bestseller 'Kubernetes/Docker for Building Container Infrastructure Environments'. 💯 Additionally, practical content is periodically added to the 'Supplementary Lessons'. 👉 Be sure to compare it with other courses :)

"Will you make money just by creating an app? No." Vibe coding is just a tool; the key is 'profitable planning and strategy'. This course goes beyond simple vibe coding technical education and reveals a practical manual for building an automated revenue system, gained through operating 200 apps as a non-developer.

This course aims to cover the latest Gemini API SDK (@google/genai) in a Node.js and TypeScript environment. You will learn how to use multimodal technology to process text, images, and video simultaneously. We will practice the entire process, from issuing an API key to project setup and model calling.

How did Assistant Manager Kim, who worked overtime every day, become a "leave-on-time pro" in just 1 hour? The secret to reclaiming your "clock-out time" Staying up all night because an Excel formula broke, or having an entire report ruined by a single typo—every office worker has experienced this at least once, right? This course was designed to put an end to that "recurring pain." You don't need to memorize complex coding syntax. We utilize Cursor AI, an AI-powered tool that writes code based on text commands, and Streamlit, which allows anyone to easily create websites. I have captured the entire process of solving the "pain points" I personally faced in the field—such as merging files by branch, automatically classifying items for repair, and generating infinite PDF certificates—all by "conversing" with AI without typing a single line of Python code yourself. By the end of this course, you will be reborn not as a mere office worker, but as a "Digital Transformation Expert" who revolutionizes your team's work efficiency.

Packed with LLM Fine-Tuning Know-how, learned from Silicon Valley LLM Project Practitioners.

This course is built from years of real-world experience solving one recurring problem: students want to build real trading bots, but get stuck between theory, complex APIs, and fragmented resources. In Become a Quant, I guide you step by step through the exact workflow used by professional quant developers—from Python fundamentals to machine learning, AI-assisted coding, and live-trading preparation on QuantConnect. Instead of overwhelming you with unnecessary theory, I focus on practical problem-solving: how to structure a trading algorithm, how to avoid common QuantConnect mistakes, how to debug, backtest, and improve strategies efficiently, and how to use AI tools as a productivity multiplier rather than a shortcut. Every concept is explained through clear examples, real trading logic, and progressive projects, so you always understand why you are doing something—not just what to type. By the end of the course, you won’t just “know Python” or “know QuantConnect” — you’ll be able to design, test, and prepare professional-grade algorithmic trading systems across multiple asset classes.

This course conducts hands-on practice related to Diffusion models among generative artificial intelligence models. By reading and implementing the prompt-to-prompt paper, which is a representative Diffusion model application paper, we expect to cultivate the ability to understand the latest artificial intelligence papers.

"Are you still studying with coding books?" A course, rated 5.0/5.0 by MBA and business undergrads at a London management university. You can take AI data analysis lectures from home. Even for humanities-background non-majors: practical data analysis skills immediately usable in the field, plus a data analysis portfolio for job search completion. I am a British Professor (British Professor) teaching data analysis and project classes with ChatGPT, no coding knowledge required.

From server setup to blog and YouTube automation, build a complete AI workflow with n8n. We will practice creating a stable operating environment, covering Contabo VPS-based server installation, Ubuntu/Docker/Portainer environment setup, domain connection, and HTTPS security configuration. The course covers Search API and file server development using FastAPI and Docker, Airtable and n8n integration, building blog AI agents, and image/video automation. You will be able to complete a profit-generating automation strategy that can be immediately applied to actual service operations.

This course is designed to help developers who feel overwhelmed by Docker, Kubernetes, and cloud deployments finally understand and apply DevOps concepts step by step. Based on real-world experience deploying containerized applications to production, I guide students through hands-on labs, clear explanations, and common mistakes beginners face. You will start from the basics of Docker and Kubernetes, build real containers, deploy applications locally, and then move confidently to Azure Kubernetes Service (AKS) with CI/CD pipelines using Azure DevOps. Every concept is explained practically, with a strong focus on “why” and “how”, so you don’t just follow commands—you actually understand what you’re doing and how to use it in real projects.

Develop a deep understanding and intuition for calculus. Solve problems and implement algorithms by hand and in Python.

A course chosen by 3,000 students! Updated to GPT-5 version! How far can you go with ChatGPT? Create automated stock trading applications and web investment reports!

This is a 'Practical Linux Troubleshooting Course' where you analyze and solve problems together with generative AI, based on a Docker practice environment that reproduces 5 representative failures that occur in actual Linux operating environments. Even those who were overwhelmed about where to start examining problems can learn the problem-solving approach and thought process of field engineers through AI guidance.

You will develop the ability to define problems based on data and clearly explain the rationale and decision-making process behind them. Additionally, rather than focusing solely on the performance of a single model, you will acquire a pipeline-oriented mindset that evaluates the completeness and reliability of the entire machine learning workflow. Furthermore, you will strengthen your problem-solving skills by tracing back the causes of errors when they occur and deriving improvement directions, and through end-to-end project experience, you will acquire practical ML pipeline capabilities that can be immediately applied in real-world settings.

Theory, numerical approximations, intuition, and visualization in integral calculus. Enhance your math-coding skills.

Build a local LLM with RAG-based Q&A system in just 2 hours of hands-on practice using Docker + ollama + R2R framework. (Suitable for in-house/internal Q&A systems, personal portfolios, AI-powered work capabilities, and even commercial services)

This is not a lecture that simply memorizes commands. It's a practical introductory course that helps you intuitively understand the 'flow' of technology - why containers emerged, how Docker creates and runs images based on what principles. Even if you're encountering Docker for the first time, after taking this course, you'll be able to explain "why containers are a necessary technology" on your own.

With v0, cursor AI, and various LLMs, from PRD to AI product recommendations! Complete your Next.js shopping mall through a practical project.

Flask is widely used as a Python backend framework. Flask_SQLAlchemy is a database orm used in Flask. If you know Flask_SQLAlchemy, you can do web development with concise code.

🔥 Vibe Coding with Streamlit 🔥 Real-time coding experience where you can instantly create your own web service with just one line of code! Visual-focused, hands-on lectures tailored to MZ sensibilities - no complex backend knowledge required. Learn the basics of Streamlit and awaken your development instincts by directly implementing practical apps. Complete your own "personal service" with code in a vibe way right now 😎💻