Deep Learning with PyTorch Part 1: Mastering Basic Algorithms from A to Z

: "From environment setup to 7 practical projects, a deep learning full-stack journey across data domains (35 lectures in total)" Go beyond simply following example code and learn how to cook various types of real-world data using deep learning. Starting from building CPU/GPU development environments, you will cover waste sorting classification (Image), stock price prediction (Time-series), ECG anomaly detection (AE), animation stylization (GAN), medical image segmentation (U-Net), and sound classification (Audio)! Systematically master core deep learning algorithms through hands-on projects.

This lecture is a lecture by instructor Jaeseong Yoon, who teaches Android lectures at App School: Like a Cool Lion.

I created a lecture by selecting the Java parts that are absolutely necessary for Android app development. The total lecture time is 13 hours and 30 minutes. It will be very helpful if you organize it once before developing Android. There are more lectures on the YouTube Soft Campus channel. I hope it was helpful. Instructor Jaeseong Yoon

Introduction to C language, the foundation language for software development

: "From environment setup to practical business projects, a data science journey to find answers for e-commerce sales through statistics (38 lectures in total)" Go beyond simply learning how to use analysis tools and learn how to process log data from actual e-commerce fields using statistical processes. After building a solid foundation in core theories—from setting up the development environment to descriptive statistics, probability distributions, and hypothesis testing—you will move on to comparative analysis of actual user app dwell time and purchase conversion rates, and even quantify performance contribution through multiple linear regression analysis! Systematically master the core data scientist competencies of 'data-driven decision-making and analysis automation' through practical projects.

"From environment setup to practical business projects, a data science journey to find revenue answers through data (39 lectures in total)." Go beyond simply learning how to use libraries and learn how to cook real-world e-commerce data through the data science process. Starting from development environment setup and Numpy/Pandas basics to data preprocessing, statistical analysis, visualization, and business scenario-based RFM segmentation and hypothesis testing! Systematically master the core competencies of a data scientist through practical projects.

"From the basics to 7 domain projects, completing the A to Z of deep learning (41 lectures in total)." Go beyond simply calling models and learn how to build clean, efficient deep learning pipelines using PyTorch Lightning, the current industry trend. Grow into a confident AI engineer capable of handling any data by directly implementing 7 projects, ranging from stock price prediction to generative AI, medical imaging, and sound analysis.

: "From Mathematical Foundations to the Latest Models: Completing the Deep Learning Pipeline with TensorFlow (44 Lectures Total)" The era of simply learning how to call model.fit() is over. From the very foundations of artificial neural networks—differentiation and gradient descent—to the essential use of TensorFlow and Keras in the industry, and even 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 enter the amazing world of deep learning, where artificial intelligence mimicking the human brain learns and makes decisions on its own.

"Beyond Data Analysis: Mastering Predictive Modeling with 5 Real-World Projects (45 Lectures Total)" Have you finished learning data analysis but feel stuck when it's time to actually build a model? Beyond simply learning how to call libraries, this course will help you fully master the inner workings of algorithms and optimal model validation strategies—ranging from Titanic survival prediction to spam text classification. Systematically conquer projects that wield the most power in the industry, from linear models to the latest ensemble algorithms and the basics of Natural Language Processing (NLP). Step into the world of AI modeling and start predicting the future based on analyzed data.

"Beyond Data Analysis: From Predictive Model Design to Optimization (29 Lectures in Total)" Have you finished your data analysis but feel stuck when it actually comes to building a model? We help you fully master not just how to call libraries, but the working principles of each algorithm and optimal model validation strategies.

* 100% Practical Curriculum: Focused on hands-on data handling rather than grammar-based learning * Essential Machine Learning Prep: A complete summary of data preprocessing techniques required before AI training * Data Analysis Tool Mastery: Conquer core functions of Numpy (numerical operations) and Pandas (data manipulation) * Deriving Visualization Insights: Transferring data visualization skills using Matplotlib and Seaborn * Building Statistical Foundations: Learning basic statistics and group analysis techniques to identify data characteristics

Currently, Android development can be done using both Java and Kotlin. You can develop the same Android applications regardless of which language you choose. This course covers most aspects of Android application development, from basics to advanced topics, and all code is written using Kotlin starting from the fundamentals. Additionally, it utilizes Google's latest UI library, Material 3. This course involves completing three projects throughout the entire Android curriculum. By working on these projects, you can master advanced techniques actually used in the industry, such as Hilt, Retrofit, Room Database, and MVVM. Furthermore, you can increase productivity using AI by performing "vibe coding" with Gemini within the Android development tools. Soft Campus will always strive to provide the most up-to-date lectures.

Android can be developed using the Java and Kotlin languages. You can develop the same Android application regardless of which language you use. This course covers most aspects of Android application development, from basic to advanced levels, and all code is written using the Kotlin language starting from the basics. Additionally, it utilizes Google's latest UI library, Material 3. This course primarily focuses on the action bar, data management, resource management, sensors, and networking within the overall Android development process.

Currently, Android can be developed using the Java and Kotlin languages. You can develop the same Android applications regardless of which language you use. This course covers most aspects of Android application development, from basics to advanced levels, and all code is written using the Kotlin language starting from the fundamentals. Furthermore, it utilizes Google's latest UI library, Material 3. This course primarily focuses on menus, Android application components, threads, and fragments within the overall Android curriculum, and more in-depth topics will be covered in the next course. Soft Campus will always strive to provide the latest lectures. Thank you.

You can create hybrid casual games using Unity 6.

Learn C# in the Unity environment. Learning C# will help you naturally learn the Unity development environment, which will be very beneficial for beginner developers.

This is the Android 15 App Development Part 1: UI Programming course featuring the latest Material 3 library.

With the latest version in 2024 and a running time of 17 hours and 48 minutes, you can completely conquer Kotlin with this one course.

This course teaches you how to develop front-end web using React. You will learn the core JavaScript grammar for React, CDN-based React development, and NPM-based React development methods.

Using Unity based on the C# language, you will learn how to create a defense game step by step.

: "From Mathematical Foundations to the Latest Models: Completing the Deep Learning Pipeline with TensorFlow (44 Lectures Total)" The era of simply learning how to call model.fit() is over. From the very foundations of artificial neural networks—differentiation and gradient descent—to the essential use of TensorFlow and Keras in the industry, and even 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 enter the amazing world of deep learning, where artificial intelligence mimicking the human brain learns and makes decisions on its own.

Finally, I feel that the AI security review (NIS/FSI) checklists, which have been vaguely positioned over the past 7–8 years, are now accurate from a practitioner's perspective, and I have established a clear framework for how to change, establish, and operate the management system. I will explain what AI security is and how it should be improved after June 2026, based on the standards of South Korea's top-tier companies (those with the highest level of information security). (This is not a basic or conceptual explanation*) I have developed this training divided into PART 1 (Basics & Overview) and PART 2 (Advanced & Security Review), so please refer to it.

Core Principles of n8n Automation and Workflow Design Basics for Non-Majors 5-Step Automation Built with n8n: API Connection Practice from Topic Input to Video Completion How to Automatically Create Videos in n8n Using AI APIs (ChatGPT, ElevenLabs, Creatomate)

1. Python coding study to obtain the AI certification, AICE Associate 2. Step-by-step Python coding study from basics to applications 3. Lectures by Professor Joongheon Kim of Korea University, a scholar in the field of AI

We will teach you about AI, especially Generative AI like ChatGPT, by learning about it from various angles and showing you how to use AI tools that can be applied in practice. It consists of content such as marketing planning utilizing Generative AI, and an introduction to and utilization of Generative AI tools helpful for marketing content development (ChatGPT, image Generative AI).

This course teaches you how to build AI agents from scratch to a production-ready level using OpenClaw. Beyond a simple introduction to features, you will master OpenAI, local LLMs, memory, sessions, Tools, and Skills in a single, cohesive workflow. Based on hands-on experience and know-how gained from developing AI and large-scale systems in Silicon Valley, the course explains architectures and operational methods applicable to real-world projects. By the end of the course, you will be able to design your own AI agents and implement automation by integrating them with various services. This is a practice-oriented course for those who want to go beyond just using AI and learn how to build and operate it themselves.

This course is not about non-developer office workers learning coding itself, but rather an education focused on developing the ability to solve business problems directly using AI agents. Previously, one had to rely on IT departments or outsourced development when problems arose, but we are now in an era where you can design alongside AI and create immediate prototypes to implement task automation. The core competencies required for this are problem definition, establishing solution strategies through AI collaboration, and the ability to build the necessary tools yourself. In particular, by utilizing Codex CLI, existing ChatGPT users can practice agentic coding immediately without additional costs, allowing non-developers to quickly gain practical experience. The curriculum is structured step-by-step, covering everything from understanding changes in the AI agent era to development basics, terminal usage, Codex CLI-based agentic coding, problem structuring, Django web app development, and the creation of actual task automation apps. Learners can secure task automation capabilities by directly building applications applicable to real-world work, such as To-Do lists, checklists, schedule management, meeting minutes, and summarization tools. The entire course is designed as a modular micro-learning system composed of short units, allowing for rapid learning of necessary content and immediate reuse during work.

Let's develop the ability to find rational decision-making models based on mathematical thinking by understanding various elements of AI and data, and solving real-world problems using ChatGPT and no-code programming!

In the era of smart factories and autonomous driving, is our production line truly safe? The remarkable advancement of Artificial Intelligence (AI) has brought innovative productivity and efficiency to the automotive manufacturing industry, but it is simultaneously creating unprecedented security threats to the Operational Technology (OT) environment within factories. This lecture breaks down "OT Security"—which can often feel vague and difficult—in a clear and easy-to-understand manner through vivid, real-world examples from the automotive industry.

In the second season of Professor Kim Seong-hun's "Deep Learning for All" course from Hong Kong University of Science and Technology, we will learn about "reinforcement learning."

Time is constantly flowing, and data is constantly accumulating. In this dynamic world, time series data plays a vital role in every aspect of our lives. From the volatility of financial markets to subtle signals of climate change, time series data captures it all. Now learn how to interpret and leverage this powerful data using the power of Python and deep learning!

This course is part of the 'Wrtn AI Literacy Voucher Program', a collaboration between Inflearn and Wrtn, and is available completely free for small business owners who are vulnerable to AI adoption. Are you busy running your store all day while also handling everything from Naver review responses to Instagram posts and marketing? This course is an introductory AI literacy program designed exclusively for small business owners to help AI share the burden of "marketing done by the owner themselves." Using generative AI and image, video, and no-code tools, you'll learn to handle everything from posters, menu boards, Instagram content, review responses, to DM management all at once. Without difficult coding, you'll create automated systems for reviews, trial groups, and DM responses using Google Sheets and automation tools (Make, Manychat, etc.)!

This is a 'Practical Linux Troubleshooting Course' where you analyze and solve problems together with generative AI, based on a Docker practice environment that reproduces 5 representative failures that occur in actual Linux operating environments. Even those who were overwhelmed about where to start examining problems can learn the problem-solving approach and thought process of field engineers through AI guidance.

Have you ever thought, "I wish I had Iron Man's perfect assistant, Jarvis..."?! Complete everyone's Jarvis to help you handle your work wisely!

If you only know LangChain, it's just a chatbot; but if you know LangGraph and Multi-Agent patterns, it becomes a service. An active AI instructor will share the know-how to build operational AI Agent workflows from start to finish—going beyond simple prompt calls to handling tools, maintaining memory, and incorporating human-in-the-loop verification—using 17 practice notebooks and 4 multi-agent patterns (Subagents, Handoffs, Router, and Skills).

Learn how to dramatically boost work productivity by automating the most frequently used MS Office tasks in your work using chatGPT.

Based on the experience of wondering how facial recognition works in offline environments on-site, this course will guide you through building the basic structure of a facial recognition-based access and attendance management system using React + Electron and Python's FastAPI-based DeepFace. Rather than focusing on perfection, we focus on understanding the operating principles and laying the foundation for you to expand into your own personal projects.

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

As the digital transformation of the manufacturing industry accelerates, production facilities, control systems, and industrial networks are becoming increasingly connected to external systems. Along with this, the possibility of cyberattacks leading to production shutdowns, quality degradation, technology leaks, and safety accidents is also growing. The [OT (Manufacturing) Security] Master Class for 4 Major Manufacturing Industries is a course that examines OT security threats that can occur in the automotive, semiconductor, chemical, and food/pharmaceutical industries, focusing on real-world manufacturing site cases. Participants will understand how production processes and facility environments differ across each industry and systematically learn about key security threats, causes of accidents, and response strategies specific to each sector. The course covers a wide range of topics in a single program, including smart factory and supply chain security in the automotive industry, protection of core processes and technical information in the semiconductor industry, control systems and safety accident response in the chemical industry, and quality, hygiene, and GMP data protection in the food and pharmaceutical industries. Rather than simply listing complex technologies or attack techniques, the course explains "what problems can occur," "why they occur," and "how to respond on-site" in a way that is easy for even beginners to understand. Through this lecture, you can compare the characteristics of the four major manufacturing industries and the differences in OT security, and learn the basic direction of security inspections and accident response required at manufacturing sites.

Have you been getting stuck on linear algebra formulas every time you try to study AI? Instead of memorizing formulas, this lecture shows you visually how vectors and matrices actually move in space. By the end of the course, you will be able to interpret the mathematics behind neural networks, PCA, and SVD on your own.