[7일 완성] 한 번에 합격하는 MS AI-900 자격증

The electronics and semiconductor industry is a field where data-driven management and innovation are particularly crucial due to ultra-precision processes and complex supply chains. This course covers practical strategies that can be directly applied in electronics and semiconductor manufacturing, including defect detection, process optimization, predictive maintenance, and supply chain management using AI technology. Along with real-world cases from global companies, the course also presents low-cost, high-efficiency AI implementation methods that small and medium-sized enterprises can realistically utilize. Through this, students will be able to understand and apply AI-based manufacturing strategies that not only improve productivity and reduce costs, but also build future competitiveness.

LangGraph, packed with a major corporation's AI Agent lead's know-how. We deliver knowledge gained from real-world challenges.

Easy, Usable Computer Vision Projects: Step-by-step learning of practical CV projects. Boost skill with real examples.

LLM and Agent, the fundamental technologies behind AI like ChatGPT. LLM is the technology that has come closest to AGI at this point in time. In this lecture, we explore LLM and Agent technologies from a general public perspective.

Learn about GAN (Generative Adversarial Networks) in an easy and accurate way.

PyTorch Fundamentals: Code-Based Clarity is a complete guide for anyone wanting to implement deep learning models using PyTorch, from deep learning beginners to developers aiming for practical application. Through 19 systematic lectures, you can perfectly master the core of deep learning, including PyTorch basics, deep learning algorithm implementation, overfitting problem resolution, and even CNN, RNN, GAN models.

The PyTorch_Project Edition, understood instantly with code, implements practical deep learning projects using image, video, and NLP data, understanding model structure and flow through code-centric learning over theory.

This course is a specialized program that covers practical application strategies for utilizing artificial intelligence (AI) in the construction and civil engineering fields. In the construction industry where digital transformation is accelerating, AI is driving innovation across design, construction, safety, and quality management. This course focuses on learning practical application cases that can be utilized in actual field sites, including smart construction, digital twins, AI-based equipment control, safety monitoring, and quality verification.

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.

🚀 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.

You can learn everything from basic usage of generative AI to automation and creating customized AI chatbots for practical work applications all in one comprehensive course.

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.

This course will help you become a deep learning-based computer vision expert who is needed in the field by providing a deep theoretical explanation of object detection and segmentation and practical examples that can be used immediately in the field.

Even if you don't know coding, you can read data - everything about no-code AI analysis that makes perfect sense! If you can use Excel, PPT, and Google Sheets, AI will now handle data analysis for you.

Still think M365 Copilot is just a ‘helpful tool’? Now is the ‘era of directly building’ M365 Copilot. “Have you tried Microsoft 365 Copilot?” Meeting summaries, document creation, email organization… While convenient, can we truly say AI is genuinely ‘working on your behalf’ with just this? If people still have to design processes, move files, and push buttons, AI is still just an assistant. How to move beyond being an M365 Copilot user and become someone who ‘directly builds’ M365 Copilot. We’ll show you how to design and deploy your own ‘actionable Copilot’ by combining the powerful features of Copilot Studio and Azure AI (such as AI Foundry, AI Search, Speech, Custom Models).

AI writes on my Slack and Notion for me? Work smarter with MCP that surpasses the limitations of existing AI!

Explaining how to connect to a Python-based AI web server using FastAPI from a Spring Boot project.

This course is designed to learn the fundamental theories and practical processes necessary for applying AI modeling and service planning to real-world work environments. Learners can acquire essential foundational knowledge to skillfully perform AI modeling projects and experience various practical procedures used in actual business settings. The course is specifically structured to provide step-by-step learning of the entire workflow from AI service environment analysis → goal setting → requirements analysis → service model design → scenario planning → utilization planning → execution plan development → performance evaluation planning. Through this approach, learners can develop systematic thinking and practical application skills for how to introduce and utilize AI from business and service planning perspectives, rather than limiting themselves to purely technical learning. Upon completing this course, learners will understand the overall planning and execution process of AI projects and will be able to strengthen their capabilities as AI service planners, PMs, and data-driven strategists in actual corporate environments.

We create an AI judge by combining CrewAI, facilitating LLM-based multi-agent technology, with knowledge graphs and inference systems.

Understand the basic concepts and latest trends of big data, and through technical understanding of big data analysis techniques, extract data patterns to present methods for business application. Through big data, you can create new businesses and develop insights into future business environments.