Mastering Big Data Analysis: From Basics to Deep Learning

Obsidian is not just a simple note-taking app, but a powerful second brain tool that allows you to systematically connect knowledge and information and utilize it long-term. However, many users give up midway due to difficulties with initial setup, the complexity of using plugins, and disorganized note structures. This course is designed to directly address these issues. Based on the instructor's experience of actually building knowledge management, project management, learning records, and productivity automation using Obsidian, the course provides step-by-step guidance on where to start, how to structure your system, and which features to use when. Starting from basic installation and setup, you'll learn to understand metadata, frontmatter, and markdown structure, and study daily, weekly, and monthly note systems, template automation, and core plugins like Tasks, Dataview, and QuickAdd through practical examples. The course also covers Zettelkasten-style note linking, project management, voice memo automation, and external service integration, so students can immediately apply what they learn to their own environment. Through this course, students will integrate scattered information into a single system and complete their own second brain that can be continuously utilized across learning, work, and projects.

This course is designed to help you learn MySQL databases and SQL step-by-step, from the absolute basics to a professional level. It covers database concepts, relational models, SQL syntax, queries, conditions, sorting, functions, joins, subqueries, and indexes, with a focus on practical examples. The curriculum is structured so that you can understand concepts many students find difficult (such as joins, subqueries, and group functions) by following along directly. With a flow of Theory → Practice → Summary, this course ensures you can complete it until the end without getting stuck. Whether you are new to SQL or want to brush up on the basics, you will gain database skills that can be applied immediately in the workplace.

Do you want to implement marketing automation but don't know where to start? Are you struggling with CRM, email marketing, and AI tools operating in silos, or seeing customer data pile up without an increase in conversion rates? This course guides you from start to finish on how to create a marketing automation system that "actually leads to sales" using CRM and Generative AI. By integrating practical tools such as ChatGPT, Zapier, Kit (ConvertKit), ElevenLabs, and HeyGen, you will build a hyper-personalized CRM workflow that segments customers → automatically delivers personalized messages → and maximizes conversion rates. Consisting of field-proven cases and step-by-step practices rather than just theory, even beginners with no marketing experience can build a complete CRM automation system by following this course.

This course is a comprehensive program that covers everything you need to know to utilize generative AI in practical work settings, from the basic principles of LLM (Large Language Models) to RAG (Retrieval-Augmented Generation), and even the latest technologies like AI Agent and MCP (Modular Command Protocol). It is structured to naturally teach you how generative AI understands, searches, judges information, and extends into AI Agents that take action, following the flow of technological development.

This is a lecture on implementing various artificial neural networks using PyTorch, a deep learning framework with very high utility in the field of artificial intelligence.

Analyze over 40 types of economic indicators and stock data with AI to create your own powerful stock price analysis model that predicts not only S&P 500 and QQQ ETF, but also individual stocks!

"Big Data + AI Full Stack Development" - The process of building big data that breathes life into AI is carried out in the form of a pilot project. We identify requirements, configure the platform step by step, and perform smart car data exploration and analysis.

This lecture is designed to give you a general understanding of AI without overwhelming you with difficult topics like mathematical formulas. It's perfect for learning with a light and easy mindset.

From core theories of deep learning and CNN to various CNN model implementation methods, and practical deep learning development know-how through real-world problems, If you want to become a deep learning CNN technology expert based on Pytorch, join us in this lecture :)

Python Crawling Master. This one is all you need for everything from installation to application. I've packed in only the essential content for real use.

From the basics of machine learning & deep learning! Creating and utilizing classification/regression/clustering/artificial neural networks with Python

This course is a hands-on masterclass that completely dissects the core technological evolution of generative AI, from LDM (Latent Diffusion Model) to DiT (Diffusion Transformer). We directly analyze the latent space-based learning principles of LDM, the structure of Stable Diffusion, and the implementation methods of the latest Diffusion Transformer through papers and code. Students will systematically learn the latest trends and structural evolution of generative models by directly implementing LDM, CFG (Classifier-Free Guidance), and DiT models using PyTorch.

You will learn Apache Airflow, the most widely used Orchestrator for creating software data pipelines.

This course explains how to perform data engineering, including real-time processing and external integration, which are the strengths of Logpresso. It also covers in depth how to assign reference data in real time, the precedence of logically related events, and CEP (Complex Event Processing) for detecting various actions and performing follow-up tasks based on collection intervals.

Get a clear overview of exam types and trends at a glance! Sharpen your practical skills with a total of 120 questions. The perfect preparation for the AWS AI Practitioner exam, designed for systematic learning even when studying alone.

Let's learn the principles properly by implementing algorithms from scratch without a deep learning library :)

This is a Python basics course for starters who want to grow as data analysts. - Those who want to do data analysis but don't know Python - Those who want to learn machine learning with Python - Those preparing for the Big Data Analytics Engineer practical exam - Those who want to develop machine learning-based applications with Python

Master the Big Data Analysis Engineer certification, an essential but never easy-to-self-study credential, in a short period by utilizing R, an easy-to-learn data analysis tool!!

🚀 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.

This course covers everything from the basics to hands-on practice of AI platforms, and is a program where you can learn AI image generation technology. Learners can understand the fundamental concepts of AI and develop the ability to generate AI images using various tools and technologies. Through this course, you can enhance your understanding of the AI field and learn how to directly generate AI images in real-world applications.

This course is an introductory program designed so that learners who are new to AI technology can easily understand and follow along. Through the lectures, you will learn the basic concepts and usage methods of AI platforms, and conduct hands-on practice directly generating images using various AI tools. Upon completing this course, you will become much closer to AI and acquire the capability to create AI images that can visually implement creative ideas.

Thoroughly prepare for the exam with the latest past questions for all subjects! Focus on the key points of each subject that will give you a boost!

From Multi-Head Attention to the Original Transformer model, BERT, the Encoder-Decoder based MarianMT translation model, and even Vision Transformer, you'll learn Transformer inside and out by implementing them directly in code.