AI Development Part 5: Practical Deep Learning Projects

This course is a course to learn data analysis methods using the Pandas library. You will learn practical application methods by learning the library and analyzing sample data in parallel.

This course is a Java Android app development course consisting of a total of 4 parts. In the second lecture, we will look at the menu and the 4 major components through practical examples.

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

We have captured the entire experience of instructor Kim Young-sam, who is active as an emoticon creator on Kakao and LINE, in this lecture—covering everything from emoticon design to creating, submitting, and generating revenue on KakaoTalk and LINE.

This course covers everything from JSP basics to completing a vehicle purchase shopping mall project, enabling you to carry out real-world projects immediately upon completion. (Please note that this course does not provide a Q&A service.)

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 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.

: "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.

"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

When you study machine learning, you learn how to improve accuracy. But the moment you try to deploy that model onto an actual chip, you are left with questions no one ever taught you to ask: How many multiplications does this model require? What does it need to remember, and how much? Can the target chip specifications handle these demands? Machine learning textbooks focus on accuracy, while semiconductor textbooks focus on circuits, yet resources bridging the two are scarce. I repeatedly encountered this gap while implementing RISC-V CPUs and NPUs on FPGAs—facing situations where the model with the highest accuracy simply wouldn't fit on the board. In this course, you will build six algorithms from scratch in Python, measuring the number of multiplications, comparisons, memory usage, and accuracy for each. You will then translate these metrics into actual FPGA board resources. While knowing the number of multiplications alone doesn't give you a sense of scale, expressing it as a percentage of the board's resources makes it easy to assess. After completing nine labs and compiling the six models into a single table, an unexpected picture emerges. We spent nine sessions counting multiplications, only to find that multiplications weren't the actual bottleneck. From there, we move on to quantizing floating-point numbers to integers, managing bit widths, and even establishing criteria for grading circuits. An FPGA board is not required. The course can be completed using Python alone, with the board serving merely as a reference for translating the measured metrics.

By simply entering a US company ticker, this system automatically collects financial accounting data, competitor analysis, recent news, and stock price information to generate professional investment reports through in-depth analysis using the OpenAI GPT API. The final results are automatically uploaded to Notion. By learning the basic concepts and terminology of financial accounting, even engineers can utilize this for competitor benchmarking, persuading supervisors, and planning new solutions. You will build a high-performance API server using Python and FastAPI, learning asynchronous programming, AI integration, and practical fintech skills. We support beginners so they can easily follow along, complete a system applicable to real-world investment and business, and grow into developers tailored for the AI era.

This course aims to cover the latest Gemini API SDK (@google/genai) in a Node.js and TypeScript environment. You will learn how to use multimodal technology to process text, images, and video simultaneously. We will practice the entire process, from issuing an API key to project setup and model calling.

Messy code, a flood of requirements... If you're looking for a breakthrough with AI, Learn how to become a "high-performing developer" by leveraging AI in legacy environments, taught by the former VP of Engineering at Toss Payments. Legacy code isn't going anywhere, even in the age of AI. It’s no exaggeration to say that every company has legacy code, unless they were founded today. So, how should we utilize AI in this reality? In this course, the former VP of Engineering at Toss Payments, who led a large-scale payment service organization, demonstrates firsthand how to work effectively using AI in legacy environments. This isn't just a course about how to use AI or basic coding skills. The goal is to help you develop your "critical thinking" as a developer and truly understand what it means to work effectively.

This course is designed to teach the entire process of 'AI Training Data Construction,' which is essential for successfully executing AI modeling projects. Learners will not simply remain at the theoretical knowledge level, but will experience step-by-step the data construction processes required in actual practice. Specifically, the course covers the entire process from AI training data planning → acquisition → storage → preprocessing → labeling → pseudonymization and transformation through hands-on practice, structured to develop practical skills that can be immediately applied in the field. Additionally, learners will systematically study the most important data management capabilities in AI projects, such as data quality management and security issues, personal information de-identification processing, and dataset optimization. Through this, learners can grow from simple data collectors into data specialists who can proactively plan and execute AI projects. 👉 Through this course, learners will understand how to construct high-quality training data that determines AI model performance and acquire practical capabilities to effectively apply this in real work environments.

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

You don't need to know any coding at all. In just half a day of studying VIBE Coding, you'll be able to create programs without writing any code. Let's learn the latest AI coding methods by directly creating 10 step-by-step examples such as web scraping, TTS, chatbots, and games.

Explore → Plan → Implement → Commit. Master a proven AI development workflow with Claude Code. Learn everything from MCP and sub-agents to prompt engineering through 23 missions, and complete 3 real-world projects.

This will be the definitive guide to Claude Code. This course dives deep into how and why Claude Code works, teaches you how to directly apply the components of harness engineering, and even dissects actual production code. Take the know-how of a developer with over 10 years of experience and transform into a key talent in the era of AI agents.

Building an MVP with AI is no longer difficult. However, most projects stop at the next stage. 👉 You’ve built the features 👉 But development doesn't continue Why does this happen? The problem isn't the code; 👉 It's because there is no structure that allows AI to work continuously. --- In this course, based on an existing project, 👉 We will cover the process of building a structure 👉 That allows AI to continue development on its own. --- Instead of simply using AI tools, we will: * Create a docs structure * Define the SSOT (Single Source of Truth) * Execute development on a per-ticket basis * Connect QA and iteration cycles 👉 To complete a single "AI Development System." --- Through this process, you will: 👉 Create a structure where development continues 👉 Even without a human manually coding. In other words, you can: 👉 Build and understand a development system 👉 That operates AI like a team. --- This course is designed for: 👉 Those who want to build a structure applicable to their projects immediately 👉 Those who started development with AI but found it difficult to sustain 👉 Those who want to move to the next level after "Vibe Coding" --- Beyond just learning, we provide an experience where you: 👉 Take action 👉 Build a structure that actually works 👉 And walk away with a framework you can apply to your own projects.

This is a practice-oriented course for experienced developers with a computer science background transitioning to the latest AI technology stack. From the design to the implementation of LLM-based applications, you will establish AI solution adoption strategies by leveraging existing software engineering knowledge. Rebuild your career by integrating traditional development experience with modern AI technology.

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.

This course trains your frontend system design mindset through real-world service cases such as the Threads feed, Amazon shopping cart, Netflix streaming UI, Google Docs collaborative editing, Micro Frontend, Agentic UI, and Observability.

How did Assistant Manager Kim, who worked overtime every day, become a "leave-on-time pro" in just 1 hour? The secret to reclaiming your "clock-out time" Staying up all night because an Excel formula broke, or having an entire report ruined by a single typo—every office worker has experienced this at least once, right? This course was designed to put an end to that "recurring pain." You don't need to memorize complex coding syntax. We utilize Cursor AI, an AI-powered tool that writes code based on text commands, and Streamlit, which allows anyone to easily create websites. I have captured the entire process of solving the "pain points" I personally faced in the field—such as merging files by branch, automatically classifying items for repair, and generating infinite PDF certificates—all by "conversing" with AI without typing a single line of Python code yourself. By the end of this course, you will be reborn not as a mere office worker, but as a "Digital Transformation Expert" who revolutionizes your team's work efficiency.

This course not only expands your ideas on blog management by creating a Python-based automated posting program for CPA affiliate marketing on WordPress but also allows you to perfectly master practical Python through real-world application rather than just theory.

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.

This course explains the basic concepts of LLM and GPT in an easy-to-understand way for everyone. Students will build their own chatbot using the GPT API and deploy it on the web using Streamlit. By experiencing everything from the basics to hands-on practice, you can build a solid foundation for your first steps in utilizing AI.

"Are you still studying with coding books?" A course, rated 5.0/5.0 by MBA and business undergrads at a London management university. You can take AI data analysis lectures from home. Even for humanities-background non-majors: practical data analysis skills immediately usable in the field, plus a data analysis portfolio for job search completion. I am a British Professor (British Professor) teaching data analysis and project classes with ChatGPT, no coding knowledge required.