Practical Projects for Learning Business Automation, News Comment Scraping, and Sentiment Analysis with Cursor AI and Python

In the era of generative AI, it is difficult to solve practical problems simply by writing prompts. In real-world services, you need to be able to design AI systems that can make decisions autonomously, call tools, retrieve data, and collaborate across multiple agents. This course is a hands-on program for everyone from developers designing AI agents for the first time to engineers seeking to build systems that can be operated in production. You will learn step by step, from designing agent architectures with LangGraph to State Management, Tool Calling, API and MCP integration, natural-language database control, RAG implementation, semantic search, Few-shot Learning, Reflection, and Fine-tuning. You will also learn how to build multi-agent collaboration structures, use the Supervisor pattern and Actor-Critic strategies, implement asynchronous communication, create automated evaluation systems, and establish Quality Gates through real-world projects. Finally, you will learn the MLOps and AI Agent operations strategies required in real production environments, including monitoring based on Metrics, Logs, and Tracing; Shadow Deployment; Canary Deployment; Drift Detection; RCA automation; and A/B testing. By the end of the course, you will be able to directly design, develop, and operate high-performance AI systems that can be applied to real-world services using the latest AI agent technologies.

This course is a practical, hands-on guide for those who want to learn how to maximize development productivity in the AI era. Beyond simply learning how to use AI, we focus on how to utilize Claude Code in a real-world development environment and how to produce "proper results." Many developers use AI but still face issues such as: ❌ Inconsistent results ❌ Poor code quality ❌ Increasing project complexity To solve these problems, this course teaches you how to utilize AI as a "development partner" rather than just a tool through: ✔ Spec-based workflows ✔ Structured command systems ✔ Sub-agent and automation strategies Furthermore, you will gain the experience of completing and deploying a full application from start to finish by integrating with real-world services such as Firebase, Figma, GitHub, and Vercel.

Claude Code is rapidly growing as one of the most powerful AI coding agents available today. However, simply installing Claude Code is not enough to maximize productivity. To truly utilize Claude Code effectively, you must understand advanced features such as context management, memory systems, skills, subagents, and agent teams. In this course, we will learn step-by-step, starting from the installation and initial setup of Claude Code to advanced workflows used in real-world practice. Students will learn how to integrate Claude Code with VS Code and how to select and utilize various models. Furthermore, you will master how to perform complex development tasks efficiently using Context Engineering, Memory management, Skills, Subagents, and Agent Teams. After the course, you will be able to use Claude Code not just as a simple AI chatbot, but as an AI development partner that genuinely boosts development productivity. Based on real-world experience in AI development and software projects, the instructor explains how to apply Claude Code to practical work and helps students solve common issues such as context management problems, token waste, and inefficient prompt writing.

This is an introductory course designed for those learning Rust for the first time, structured to help you learn step-by-step from the basics. Many learners encountering Rust for the first time often find it difficult due to: - Unfamiliar syntax - The burden of memory concepts - Differences from existing languages (C/C++) To solve these problems, this course helps anyone understand Rust naturally through: 👉 Easy explanations 👉 Step-by-step curriculum 👉 Repetitive practical examples Rust is a modern systems programming language that offers both safety and performance, making it a very important technology in the future development landscape. Build a solid foundation of Rust's core basics through this course.

This course is designed step-by-step, from the basics to practical application, so that even those new to Claude AI can easily follow along. In this course, you will start with the basic concepts of Claude AI and how to write prompts, and then learn various use cases such as document writing, summarization, translation, brainstorming, and file and image analysis. You will also practice how to create documents, charts, diagrams, and simple applications using Claude Artifacts, as well as creating infographics using Claude Design, modifying results, and saving them as PDFs. Finally, we provide productivity-enhancing know-how that can be used immediately in the workplace, including advanced prompt engineering techniques, automating repetitive tasks using the Projects feature, Excel functions, and coding support. From beginners with no AI experience to office workers, students, and content creators who want to increase work efficiency, the course explains everything through practical examples so that anyone can learn easily and apply it right away.

This is a practical, step-by-step course designed to help everyone from learners using Google Gemini for the first time to professionals who want to actively apply Gemini in their work. Rather than simply introducing features, the course focuses on real-world applications and explains how to address challenges learners may encounter when writing prompts, researching information, creating documents, producing content, and more. Participants will progressively learn Gemini’s basic features, Google Workspace integration, Gem customization, Google Labs, and NotebookLM, gaining the skills to use AI effectively in both their work and daily lives. In particular, the course is systematically structured—from basic concepts to practical applications—so that even learners who are new to AI can follow along easily. It also covers safe ways to use AI, including information verification and personal data protection.

This is a practical, hands-on course where you will learn the principles of AI Agents—going beyond simple chatbots that just answer questions to systems that can perceive situations, make judgments, and control actual systems. The curriculum is structured to provide a step-by-step understanding, starting from the differences between AI Agents and chatbots to the core Agent Loop structure: Sense-Think-Act, state management, planning, and feedback loops. Moving beyond theory, you will set up Windows and Linux development environments and gain hands-on experience with the basic structure of Kubernetes and kubectl commands. Following this, you will implement a process where an AI Agent detects server issues, analyzes the causes, and automatically resolves errors. Finally, by directly analyzing the Python code and internal logic of the Agent controlling Kubernetes, you will understand how AI Agents connect to and operate within real-world systems. From developers new to AI Agents to those who want to design Multi-Agents that control actual systems, this course allows you to learn both the principles and the practical implementation process of Agents.

This course is designed to help learners build solid AWS cloud engineering and architecture skills through hands-on projects and practical demonstrations. Students will learn how to build secure AWS environments, manage scalable computing infrastructure using EC2 and Auto Scaling, configure networking via VPC and Load Balancers, and implement cloud-native storage and database solutions using S3, Aurora, and MySQL. Throughout the course, I will guide students step-by-step using hands-on labs, architectural explanations, and real-world deployment scenarios commonly used in modern cloud environments. I focus on simplifying complex AWS concepts into easy-to-understand lessons while helping students avoid common mistakes made during cloud deployment, security configuration, and infrastructure scaling. By the end of this course, students will not only gain the confidence to design secure, scalable, and cost-effective solutions on AWS but will also be prepared for the AWS Solutions Architect Associate certification exam.

This course is designed for developers, DevOps engineers, cloud engineers, and IT professionals who want to master advanced Kubernetes concepts used in real-world production environments. Throughout the course, students will engage in hands-on projects and real-world deployment scenarios, learning how to build secure, scalable, and production-ready Kubernetes infrastructure. Rather than focusing solely on theory, this course emphasizes practical implementation, troubleshooting, and best practices utilized by modern DevOps teams. One of the biggest challenges students face when learning Kubernetes is understanding how all components interact within large-scale environments. Many learners struggle with topics such as RBAC permissions, persistent storage, namespaces, network policies, StatefulSets, Kustomize, and secure cloud deployments. This course addresses these issues step-by-step through clear explanations, guided labs, and examples that mirror actual production environments. Students will learn step-by-step how to organize Kubernetes resources, secure workloads, manage application configurations, deploy stateful applications like MongoDB, and automate multi-environment deployments using Kustomize. Additionally, the course covers how to deploy applications to Google Kubernetes Engine (GKE) and configure Ingress, TLS certificates, monitoring, and scaling strategies. By the end of this course, students will have the confidence to design, secure, deploy, and manage advanced Kubernetes environments used in professional DevOps and cloud engineering workflows.

This course is designed for developers, AI enthusiasts, and professionals who want to develop real-world AI applications using LangChain, OpenAI, Hugging Face, and Python. Rather than focusing solely on theory, this course takes a practical, project-oriented approach. Throughout the course, students will directly build a total of 15 high-quality AI applications, including chatbots, CSV analysis tools, resume screening systems, invoice extraction bots, support assistants, marketing tools, and text-to-SQL applications. I created this course to make complex AI concepts easy to understand and to ensure modern LLM development is accessible to everyone. Many learners struggle to understand how tools like LangChain, embeddings, memory systems, vector databases, and AI agents interact in real-world applications. This course guides you step-by-step through clear explanations, hands-on coding sessions, and actual implementation workflows. By the end of the course, students will confidently understand how to design, build, and deploy AI-powered applications using the latest generative AI technologies.

While studying Spring Security, you've probably wondered at least once: "I've built the login, but how do I handle authorization?" "How are authentication and authorization implemented in real-world practice?" This course is a practical, project-oriented lecture designed to solve those exact concerns. Instead of just explaining concepts, we implement them exactly as they are used in real services by: 👉 Building on an actual TodoApp project 👉 Integrating with a database 👉 Managing user permissions 👉 Handling validation In particular, I will provide easy, step-by-step explanations for the parts many students find difficult: Method-level authorization Access control within templates Validation using regular expressions Through this course, I will help you confidently say, 👉 "I can actually use Spring Security." 🚀

This course goes beyond simple grammar explanations and focuses on developing C++ programming skills that can be applied immediately in the field. Throughout my teaching, I have directly addressed common challenges faced by many learners—such as struggling to understand the STL or feeling confused by file handling and exception handling concepts. Based on this experience, I provide step-by-step explanations to ensure even complex concepts are understood easily and clearly. Furthermore, this course is not just about delivering theory; it is structured around: 👉 "Why this concept is necessary" 👉 "How it is used in actual code" 👉 "How it is applied in professional practice"

This course is designed to help even those new to MySQL understand it easily, starting from the basic concepts of DBMS and progressing step-by-step to the ability to write SQL that can be applied in real-world practice. To solve the problems learners frequently encounter (e.g., SQL syntax is difficult, not knowing how to write queries, or not understanding the flow of data processing), the course is structured with repetitive theory and practice, helping you naturally build your skills through a hands-on approach. In particular, the focus is not on simple memorization of syntax, but on understanding "why it is written this way" through the process of handling actual data.

This course is designed for those who want to go beyond simple AI chatting and actually get work done using AI agents. Many students are already using ChatGPT or Claude, yet they still struggle with repetitive tasks, scattered workflows, excessive research, and low productivity. The problem usually doesn't lie with the AI itself, but in knowing how to organize work systematically, give clear instructions, and build reliable workflows. In this course, we provide a step-by-step guide on the practical application of Claude Cowork. Rather than focusing solely on theory, we practice through practical examples such as project management, browser automation, meeting preparation, persistent memory systems, skills, plugins, and desktop control. This course explains everything in a simple and practical way so that even beginners can easily follow along. By the end of the course, students will understand how to utilize Claude Cowork as a true productivity assistant for research, planning, organization, automation, and daily tasks.

This course explains everything from the basics of MySQL to its practical application in a step-by-step manner, making it easy to understand even for those learning databases for the first time. Rather than just providing simple theoretical explanations, the course focuses on real-world problem scenarios and teaches you how to write SQL and handle data through hands-on practice. The curriculum is systematically structured to cover the entire process, from DBMS concepts to MySQL installation, basic SQL syntax, data manipulation, and the use of functions. In particular, the goal is to help you understand "why it is written this way" through repetitive practice and to develop the ability to write queries on your own.

This course is a practical, hands-on guide to automating repetitive tasks and maximizing work productivity using ChatGPT and Python. You will learn step-by-step how to use AI to automate daily repetitive tasks such as Excel work, report writing, sending emails, and data organization. Starting as a non-developer myself, I have taught AI automation to thousands of students, and I have compiled only the methods that can be used immediately in actual business environments. This course focuses not on complex theories, but on **"automation techniques you can use right today."** 👉 The goal is to turn ChatGPT into more than just a tool, but into **your own personal work assistant (Jarvis)**.

In the era of generative AI, it is difficult to solve practical problems simply by writing prompts. In real-world services, you need to be able to design AI systems that can make decisions autonomously, call tools, retrieve data, and collaborate across multiple agents. This course is a hands-on program for everyone from developers designing AI agents for the first time to engineers seeking to build systems that can be operated in production. You will learn step by step, from designing agent architectures with LangGraph to State Management, Tool Calling, API and MCP integration, natural-language database control, RAG implementation, semantic search, Few-shot Learning, Reflection, and Fine-tuning. You will also learn how to build multi-agent collaboration structures, use the Supervisor pattern and Actor-Critic strategies, implement asynchronous communication, create automated evaluation systems, and establish Quality Gates through real-world projects. Finally, you will learn the MLOps and AI Agent operations strategies required in real production environments, including monitoring based on Metrics, Logs, and Tracing; Shadow Deployment; Canary Deployment; Drift Detection; RCA automation; and A/B testing. By the end of the course, you will be able to directly design, develop, and operate high-performance AI systems that can be applied to real-world services using the latest AI agent technologies.

This is a practical, hands-on course where you will learn the principles of AI Agents—going beyond simple chatbots that just answer questions to systems that can perceive situations, make judgments, and control actual systems. The curriculum is structured to provide a step-by-step understanding, starting from the differences between AI Agents and chatbots to the core Agent Loop structure: Sense-Think-Act, state management, planning, and feedback loops. Moving beyond theory, you will set up Windows and Linux development environments and gain hands-on experience with the basic structure of Kubernetes and kubectl commands. Following this, you will implement a process where an AI Agent detects server issues, analyzes the causes, and automatically resolves errors. Finally, by directly analyzing the Python code and internal logic of the Agent controlling Kubernetes, you will understand how AI Agents connect to and operate within real-world systems. From developers new to AI Agents to those who want to design Multi-Agents that control actual systems, this course allows you to learn both the principles and the practical implementation process of Agents.

This course focuses on developing immediately applicable visualization skills by utilizing Seaborn, a core tool for data visualization, and Plotly/Dash for building interactive dashboards. Through various hands-on exercises, you can simultaneously improve your ability to create insight-driven visual materials and implement web-based dashboards.

I have summarized how to create a profitable AdSense website. I have organized it so that even those without development experience can follow along, leaving only successful methods so that they can be replicated exactly.

This course is designed for developers, AI enthusiasts, and professionals who want to develop real-world AI applications using LangChain, OpenAI, Hugging Face, and Python. Rather than focusing solely on theory, this course takes a practical, project-oriented approach. Throughout the course, students will directly build a total of 15 high-quality AI applications, including chatbots, CSV analysis tools, resume screening systems, invoice extraction bots, support assistants, marketing tools, and text-to-SQL applications. I created this course to make complex AI concepts easy to understand and to ensure modern LLM development is accessible to everyone. Many learners struggle to understand how tools like LangChain, embeddings, memory systems, vector databases, and AI agents interact in real-world applications. This course guides you step-by-step through clear explanations, hands-on coding sessions, and actual implementation workflows. By the end of the course, students will confidently understand how to design, build, and deploy AI-powered applications using the latest generative AI technologies.

This is a process of developing a practical web service in collaboration with AI agents based on TARS prompt engineering principles. You will gain full-stack development experience covering everything from UI/UX component creation to CRUD functions, API integration, and data visualization, ultimately completing your own idea as a viable MVP.

This course focuses on developing practical visualization skills that can be immediately applied in the workplace by utilizing Seaborn, a core tool for data visualization, and plotly/Dash for building interactive dashboards. Through various hands-on exercises, you can enhance both your ability to create insight-driven visual materials and implement web-based dashboards.

If you could draw graphs but couldn't explain the data, this course is a process for building the "ability to read and explain" data. In PART 3, we perform Exploratory Data Analysis (EDA) focusing on real-world cases. ✔ Checking data distribution ✔ Analyzing relationships between variables ✔ Outlier detection and visualization interpretation You will learn the analysis structure using the Titanic and Iris datasets, and through a project utilizing TMDB 5000 movie data, you will directly experience the entire analysis process: Data cleaning → Setting analysis topics → Visualization interpretation. By the end of this course, you will possess the analytical capability to read and explain data.

"I knew how to analyze data, but I had no way to showcase the results." I was the same way at first. I would work hard on my analysis and create charts in Jupyter Notebook, but when it came time to share, I ended up just taking screenshots and pasting them into PowerPoint.. I couldn't even dream of showing things while changing filters in real-time. This course solves that frustration. Even if you don't have much coding experience or know anything about web development, as long as you have the basics of Python, you can build a functional data dashboard yourself!

If you ask an AI to "build a model with this data," the first few lines might look plausible, but as soon as things get slightly complex, it starts using the wrong columns, introduces data leakage, and results in a model that performs worse despite a higher score. This course does not teach you better prompts. Instead, it covers how to directly design a loop (harness) where an agent understands data, plans, writes code, validates its own results, and backtracks when wrong. After manually walking through the five-piece loop of DS-STAR, you will assemble components one by one, from data profile cards, hooks, and skill hierarchies to independent validators and ablation.

Prepare perfectly for the Goorm environment—the biggest challenge of the Big Data Analysis Certification practical exam—on the practice platform provided by Algorun. Practice past exam questions for Task Types 1, 2, and 3 repeatedly in a setting identical to the actual exam environment to gain an intuitive feel for the test rather than just memorizing code. This is a practice-oriented lecture designed to train your hands to move automatically in an environment that differs from Colab or Jupyter.

We explored the essential Python syntax necessary for big data analysis and, through various examples, presented methods such as big data file processing and visualization.

Here are some key competencies that are often overlooked by others in the fiercely competitive and complex semiconductor process job market.

This course is not just about "how to implement" a Transformer, but about dissecting why this architecture was created, what role each module plays, and how the entire model works from the designer's perspective. We deeply analyze the internal computation principles of Self-Attention and Multi-Head Attention, and directly verify through formulas, papers, and implementation code what limitations Positional Encoding, Feed-Forward Networks, and Encoder·Decoder structures were introduced to solve. Starting from Attention, we assemble the entire Transformer structure ourselves, and actually perform training to experience firsthand how the model operates. This course is the most structured and practical roadmap for "anyone who wants to completely understand Transformers."

This is a course on building your own stock and cryptocurrency automated trading programs using ChatGPT, even without prior coding knowledge. You will gain both AI development skills and financial investment knowledge through hands-on practice covering the entire process, from data collection to predictive model development, stock recommendation systems, and actual automated trading.

If you want to properly learn OCR technology that's truly used in practice, this one course is all you need! Aiming for over 98% accuracy even with unstructured documents and complex layouts, based on the latest SOTA models and real-world know-how, we build enterprise-level OCR projects together.

: "From Mathematical Foundations to the Latest Models, Completing the Deep Learning Pipeline with PyTorch (44 Lectures in Total)" The era of simply learning how to call model.fit() is over. From the very basics of artificial neural networks—differentiation and gradient descent—to essential PyTorch techniques used in the industry, and CNN/RNN for handling image and time-series data! We will help you systematically master the entire process of deep learning. Go beyond analyzed data and step into the amazing world of deep learning, where artificial intelligence mimicking the human brain learns and makes decisions on its own.

Now display Python results as web pages! You can create your own web page using only Python. This is an opportunity to create an Interactive Dashboard that allows you to check results created with Python, such as data analysis and machine learning, on a web page!

We guide non-majors and beginners to quickly obtain the Big Data Analysis Certification (Practical Exam)! Keep the theory light and the practice solid—focusing on core points that are guaranteed to appear on the exam through past questions, without the need for complex background knowledge.

Time Series: Become a Data Analysis Expert! Let's be analysts strong in theory & practice!