Android and iOS app development using machine learning models

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!

Let's learn various data structures and algorithms and develop practical skills by implementing them through programming!

Let's become creators using ChatGPT and generative AI!

Let's understand the major algorithms of machine learning and deep learning, and get to know artificial intelligence!

Let's build the skills to develop web applications using React and cultivate the capability to apply them in practice!

Let's design, implement, and develop while learning the basic and object-oriented concepts of the C++ language!

Modern C++ Programming based on Object-Oriented Concepts for Computer Science Majors

Let's find out how to automate tasks using Python!

Understand and learn about Artificial Intelligence and AI ethics, and strengthen your AI digital capabilities by integrating them with your own field of study!

Let's cultivate professional expertise in programming, algorithms, and statistical analysis!

Java Programming Based on Object-Oriented Concepts for Computer Science Majors

Let's understand machine learning and deep learning model algorithms by using Keras!

Let's look into how to deploy Abyss for non-majors!

Let's understand data analysis tailored for advanced retail businesses and learn how to use the tools utilized in the industry!

It improves logical thinking skills and cultivates the capacity to converge one's major with information and communication technology in the era of the Fourth Industrial Revolution.

Let's learn how to collect data from the web and create a supervised learning model for machine learning!

Let's practice learning how to translate real-life and major-related problems into actual computer programs and utilizing Python-based AI tools!

Learn machine learning techniques using Python and improve your ability to extract information from real-world data and develop predictive models!

Let's acquire the Python coding skills necessary for artificial intelligence by learning the basics of the Python programming language!

It improves logical thinking skills, fosters the ability to understand how to use programming tools, and develops the capacity to design algorithms for problem-solving.

Learn how to build deep learning models yourself using intuitive and Pythonic PyTorch. This reflects the latest PyTorch version.

Many students who want to study programming will have studied from various books or blogs on the market. However, many of you may have felt that the learning speed was slow and that there was a lot of unnecessary content in the books or articles. Rather than focusing on the theoretical part, this lecture focuses on 'development' itself and only covers the core content of development.

Create an augmented reality AR app in the new SwiftUI environment. Create your own Snow app using ARKit, RealityKit, Core ML, etc.

Kotlin Programming Language, which you can learn by practicing with 76 examples. You have to develop in Kotlin starting tomorrow, but you don't have time to study the grammar? Let's quickly learn all the grammar of Kotlin with the examples provided in this lecture.

Learn Android mobile programs step by step through practical projects. While creating a mobile app that measures brain waves and plays music using a Bluetooth brainwave measurement device, learn the basics of mobile app development as well as useful techniques such as SQLite, Bluetooth, and Sound that can be used in real life.

This course teaches you how to quickly create various native app features for mobile environments using Javascript, a web service development technology, and walks you through the process of releasing them to the store.

This is a lecture that studies the physical information neural network, one of the next-generation innovative technologies of deep learning, and implements it directly using Pytorch. Let's learn the next-generation innovative technology of artificial intelligence with me!

AI that thinks and adapts? Now you can build it yourself. Master the core of reinforcement learning with intuitive explanations and code, not complex theories, and grow into a 'practical developer' through finance and industry projects.

Let's implement a deep learning framework directly with Excel! Let's solve supervised learning/unsupervised learning/reinforcement learning with the implemented framework!

Putting a neural network on an FPGA—the phrase is familiar, but few have actually done it. If you want to stay a step ahead in capstone projects, competitions, or graduation projects, you must be able to go beyond just training a model and actually build and prove how those computations run inside a real chip. This course bridges that exact gap between theory and physical hardware. You will complete a neural network accelerator (NPU) for MNIST digit recognition from start to finish—from reading specifications to Python golden models, Verilog RTL design, and FPGA verification. From Conv layers made with line buffers and parallel MACs, through ReLU, MaxPool, and Flatten, to fully connected layers and Argmax—you will integrate nine blocks into a single chip and prove for yourself that "the Python reference and the hardware output do not differ by even a single bit (Bit-True)." I have even included how to overcome those frustrating moments when the simulation runs fine but the results are subtly different. I will guide you through practical know-how that general deep learning textbooks don't cover, such as integer quantization, array axis reordering, and accumulator overflow prevention. I have distilled the trial and error I experienced over years of RISC-V CPU design and FPGA Bit-True verification into this course so that you don't have to repeat them. By the end of the class, you will have a completed hardware project that allows you to say, "I designed and verified it myself."

We will develop an iPhone-only RPG game using Xcode. We have prepared a lecture so that even those with no knowledge of app development can follow along.

You can learn the concepts of iPhone (IOS) game development by making your own version of the classic game, Brick Breaker.

Learn Springboot + Android(Compose) RestAPI Board Creation Faster Than Anyone Develop a CRUD server with Springboot and create your own board with Android!!

It’s okay if you don’t know how to code. This course focuses on the experience of using AI to build an ad-revenue-generating Android app from scratch and successfully launching it in just one day. Many people stop at these stages: “I built the app, but how do I monetize it?” “I tried adding ads but got stuck on AdMob, policies, and reviews.” “I gave up right before launch because of the privacy policy.” In this course, we will navigate through every "drop-off point": 👉 Idea → App Creation → Ad Integration → Store Launch Essentials → Actual Deployment. We let the AI do the work, and we make the decisions. The goal is to complete an app with a "functioning revenue structure" within a single day.

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.

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.

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.

Let's create a Tetris game in Swift.

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 is a lecture covering LLM theory and practical examples based on <Large Language Models, Just the Essentials!> (Insight, 2025).