Complete AI Agent Development All-in-One (w. LangGraph, Google ADK, CrewAI)

If Java has Spring and Python has Django, Node has NestJS! Learn Node's professional backend NestJS and Node's object-oriented programming and design patterns! This course is a membership subscription course, and if you purchase it once, you can take the continuously added NestJS techniques, DB, testing, etc.

Develop productively, robustly, securely, and scalably with Python! 🔥

We will develop a web app that collects data using concurrent/parallel programming in Python, loads it into MongoDB, analyzes it, and visualizes the collected data using FastAPI, a modern web framework. In the process, you will learn and understand Python concurrent/parallel programming using coroutines, multi-threading, and multi-processing, and understand how to use them to improve Python's performance. We will help you apply Python concurrent/parallel codes directly to your work. We will not only teach you how to develop and end, but also how to deploy and operate an actual app on the AWS cloud.

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!

"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.

The goal is to learn complex GPT API and Python in the easiest and most hands-on way possible. Using the latest ChatGPT API, we'll develop a YouTube video summarization AI and implement it as a web application using Streamlit.

Stop worrying about the dev team - I'll build it myself now! This course helps professionals in planning, marketing, sales, and other practical roles implement core web service features on their own using AI tools, without needing developer assistance.

A Complete End-to-End Developer Journey — Build Lightning-Fast APIs, Modern Frontend Interfaces, Real Projects, Authentication Systems, Dashboards & Full Deployments Using FastAPI, React, and Vibe Coding Techniques.

🔥 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 😎💻

Hello! Have you ever gotten tired of repeatedly writing the same code over and over again while doing data analysis? 😫 Now let's give your projects wings with PyCaret! 🚀 This course will teach you the secret to automating most of the tedious and complex machine learning processes with just 5 lines of code. Here's how this course will transform you! ✨ Master Real-World Projects: 📈Customer purchase prediction (classification), 📉Medical cost prediction (regression), 🧩Customer grouping (clustering), ⏳Airline passenger prediction (time series)! You can develop 'real' problem-solving skills by following along with 4 core projects hands-on. Acquire Expert Skills: But that's not all! 🤖 We'll also teach you expert skills like managing your models with MLOps and creating API servers with just a few clicks. Pretty amazing, right? Protect Your Work-Life Balance: Most importantly, we'll drastically reduce your repetitive tasks and help you focus only on core analysis. Leaving work on time is a bonus! 😉 Whether you're not yet comfortable with coding or you're a current professional wanting to explosively boost productivity, everyone is welcome! 👋 Shall we transform together into a '10x faster data scientist'?

After ChatGPT was released in 2022, AI has become a very close software in our lives. The performance of models is improving day by day, and many new software applications that didn't exist before are emerging using AI. In 2025, as AI agents become a hot topic, many people are starting to develop AI agents. You may have heard the term AI agent, but this is an introductory tutorial that will help you understand what an AI agent is, why it has suddenly become a topic of discussion among so many people, how to create an AI agent, and how to easily build one using ADK.

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We're back with massively upgraded content and completely transformed lectures!

Stop using print()! Learn easy and powerful logging with Python's loguru library. From basic concepts to file management, FastAPI web app integration and Slack notifications, we'll teach you step by step with hands-on practice.

Create a program using the GPT API and even generate images and videos!

Simply by entering a US company ticker, this system automatically collects financial accounting data, competitor analysis, recent news, and stock price information, then performs in-depth analysis using the OpenAI GPT API to generate professional investment reports. The final results are automatically uploaded to Notion. By learning fundamental financial accounting concepts and terminology, even engineers can utilize this for competitor benchmarking, persuading supervisors, and planning new solutions. Build high-performance API servers with Python and FastAPI while learning asynchronous programming, AI integration, and practical fintech skills. Even beginners can easily follow along to complete a system applicable to real investment and business scenarios, helping them grow into developers suited for the AI era.

This course proceeds with Python theory and practical sessions that even non-majors can easily understand. This is the first step in the overall roadmap built for application development - the Python programming introduction course. [Complete-Roadmap] Python Basics->Advanced Python->Automated Trading-Backtest Development->Automated Trading-Real-time Development->Automated Trading-LLM Application

Chatgpt is a program that reads and comments on posts written by my neighbors Lesson on developing marketing automation and neighbor management programs

During the Gold Rush, it was said that it was easier to become rich by selling gold mining tools than by mining gold yourself. How about selling programs in the ChatGPT Age of Exploration?

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

This course is a practical pre-training program designed to help you quickly complete the core technologies needed for **smart mirror** production in the form of "pre-missions." Rather than simple feature explanations, it aims to reach a level where you can immediately create an integrated demo (PoC) in the field. The course requires completion of the following 4 components: LLM API-based conversational response module Including role separation (system/user), conversation history maintenance, retry/timeout, and log handling, you'll learn patterns for creating short, card-style summary responses suitable for smart mirrors. Face recognition + filter application with MediaPipe FaceMesh Extract facial landmark coordinates and implement a PoC that tracks visual filters like sunglasses/masks/stickers in real-time. Raspberry Pi ↔ Arduino serial communication (Python) Transmit Arduino sensor values via serial and perform reception, parsing, and error handling on Raspberry Pi using Python (pyserial) to first complete the system backbone of "sensor → Pi → (UI/storage)" flow. Smart mirror frame/hardware 3D modeling Considering display, Pi, power supply, cables/heat dissipation, complete an assemblable frame (front/back, brackets/screw holes, etc.) as STEP/STL deliverables. Additionally, optional sections allow expansion based on team capabilities, connecting step-by-step: Weather data input on Pi, Firebase/Supabase DB integration, Responsive web UI (mobile/desktop), AWS deployment basics. As a result, upon completing the course, students will have not just "individual technology pieces," but an integrated portfolio (AI response + face recognition + sensor communication + hardware design) ready for immediate demonstration at makerthons.

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.

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!