Building OCR that actually works in real-world scenarios, here's how to do it.

🚀 Practical AI Anomaly Detection with Databricks! 💡 Stop using expensive and slow models! From large-scale data optimization to real-time deployment, complete an anomaly detection AI that can be used immediately in corporate practice.

Through this course, you will be able to immediately carry out corporate Apache Spark projects.

This is an introductory course where you learn the core concepts of Claude Code through a 3D office analogy. From hiring a single robot in an empty office, giving it a brain and a heart, equipping it with tools, permissions, and memory, to eventually having multiple robots work together as a team—each concept unfolds as an office scene. You will master every concept by interacting with the digital course materials and complete your learning through hands-on practice on your own computer.

Conversation loops, tool calling, permissions, prompt assembly — learn the core principles of Claude Code by visualizing them through 3D interactive materials. Translate the structure you understood from the materials directly into code to complete your very own 'Mini Claude Code (Harness)'.

Do you have the data but feel overwhelmed about how to read and process it in Python? Don't worry. You can handle it with the magic of Pandas. Pandas is the most powerful, efficient, and useful data processing library. Skill-UP your data preprocessing with Pandas! Get instant insights!

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.

You can learn about Text-to-SQL systems that generate and query SQL statements in real time in response to user questions.

Learn how to use OpenCV and process images through a practical implementation of business card recognition. OpenCV is briefly explained, and the OpenCV functions required to implement business card recognition are explained in more detail. In other words, rather than a basic course, it provides an essence for the practical application of business card recognition. It also includes a method of utilizing Tesseract and Microsoft's OCR engine. The source code of this lecture is based on Python, but since you will learn the OpenCV classes and functions used and the image recognition and processing technology, it will not be difficult to apply it to Android and iOS based on this. Those who take the course will be provided with lecture slides and actual business card recognition source code. The lecture contents were further improved and verified through three offline seminars in January and February 2017, and the instructor has carefully recorded them again for online use. These are the OpenCV functions used in this lecture. cv2.Canny() cv2.GaussianBlur() cv2.VideoCapture() cv2.adaptiveThreshold() cv2.approxPolyDP() cv2.arcLength() cv2.boundingRect() cv2.boxPoints() cv2.contourArea() cv2.convertScaleAbs() cv2.createTrackbar() cv2.cvtColor() cv2.destroyAllWindows() cv2.dilate() cv2.drawContour s() cv2.findContours() cv2.getAffineTransform() cv2.getPerspectiveTransform() cv2.getTrackbarPos() cv2.imread() cv2.imshow() cv2.imwrite() cv2.minAreaRect() cv2.namedWindow() cv2.rectangle() cv2.resize() cv2.threshold() cv2.waitKey() cv2.warpAffine() cv2.warpPerspective()

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!

This course provides a complete theoretical overview of the operations of convolutional and pooling layers, which are the core modules of Convolutional Neural Networks, and helps you develop implementation skills by building them yourself.

This course is a hands-on masterclass that completely conquers the evolution of Diffusion Models through papers and code implementation. You'll learn the core models of generative AI, including DDPM (Denoising Diffusion Probabilistic Model) and DDIM, by studying the paper principles and implementing them directly. We analyze step-by-step the background of each model's emergence, mathematical formulations, network architectures (U-Net, VAE, Transformer), training processes (Noise Schedule, Denoising Step), and the ideas that led to performance improvements. Students will directly code all models using PyTorch, gaining not just paper comprehension but 'practical skills to reproduce and apply' them in real-world scenarios. Additionally, by comparing the differences between models and their developmental flow, you'll clearly understand how they expand and evolve. This course integrates theory, code, and practice into one comprehensive journey, providing researchers, developers, and creators alike with a systematic way to master the evolution of generative models. Beyond simply 'reading' papers, start your experience of 'understanding and recreating' through direct implementation now.

Let's create a web app that uploads an image and uses OpenCV to process it and detect faces.

AI Artificial Intelligence, it goes without saying that mobile is the trend. But how do you learn machine learning and deep learning and use them on mobile? Now, let's learn useful mobile Computer Vision projects that are used in daily life.

No more object detection! Learn the latest trends in pose estimation, the latest methods for estimating poses from images and videos, and learn deep learning techniques to train and utilize 2D pose estimation models from your own videos.

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.

Done in a single line of command. Hermes, the AI agent that remembers, automates, and grows on its own. Even if you are not a developer, you can become someone who 'delegates' work to AI starting today. SMARTFORK will be with you from installation to practical application.

Do the results of vibe coding keep turning out differently from what you intended? Learn a workflow that uses specification-driven development (SDD) and Spec Kit to define goals, scope, business rules, and completion criteria, then shares the same specifications with Claude Code and Codex to reduce repetitive revisions.

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.

This lecture examines the entire process of generating a photo mosaic from an algorithmic perspective. The input image is reduced to a set of representative colors matching the number of tiles through K-means clustering, then the Hungarian algorithm is applied to optimally match each color with a tile in a 1:1 correspondence. Through this, you will understand how image processing and combinatorial optimization algorithms combine to create a single visual output.

Build a Saju (Four Pillars of Destiny) analysis AI agent that integrates Manseryeok, Astrology, Zi Wei Dou Shu, Onomancy (Name Study), and MBTI using n8n workflows and Python algorithms. We will complete a production-level service system from start to finish where 13 specialized agents collaborate to generate professional-grade Saju reports.

This course covers implementing combat, UI, data, and server functions for a Unity idle game using CLI and MCP-based AI tools. You will learn the entire process of releasing a mobile game to Google Play, including the application of Firebase, advertisements, in-app purchases, and Addressables.

Learn deep learning through PyTorch. Ultimately, the course covers image classification.