Getting Started with Machine Learning (Basic Principles + Beginner Practice)

In this course, we will mainly look at the animation-related features of Blender. You will learn how to create basic keyframe animations and some methods for modifying keys.

Through this course, learners will be able to acquire the skills necessary to obtain a Computer Application Skills certification. This course is structured according to the examination standards for obtaining the Computer Application Skills certification. You can learn and apply fundamental knowledge of Computer Application Skills subjects to prepare for certification acquisition. (Computer Application Skills Level 1 & 2 Practical - Spreadsheet Practice)

Through this course, learners will be able to acquire the skills necessary to obtain a Computer Utilization Proficiency certification. This course is structured according to the examination standards for obtaining the Computer Utilization Proficiency certification. You can learn and apply fundamental knowledge of Computer Utilization Proficiency subjects to prepare for certification acquisition.

This course is designed to provide basic knowledge for those who are interested in mechanical drawing and design, those who are aiming to obtain qualifications in the field, those who wish to advance into a related field, or those who wish to improve their expertise.

This is an assembler course provided by Smoothie TV.

This is a Photoshop course for beginners that will teach you the most basic and important functions of the graphic editing tool Photoshop.

Learn representative functions such as basic editing, color correction, and integration with other software using DaVinci Resolve.

We introduce good lectures based on the motto of TED(http://ted.com) ‘Ideas worth telling’.

This is a lecture on the application of Revit (Autodesk Revit) for implementing BIM (Building Information Modeling).

This course teaches you how to write safe and reliable code in the software development process. Rather than simply stopping at feature implementation, you'll learn programming principles and techniques that can prevent security vulnerabilities and ensure stability. Through hands-on practice and case studies, you can understand security threats that may occur in real development environments and acquire safe coding techniques such as coding rules, input validation, exception handling, and memory management to prevent them. By taking this course, developers will enhance the completeness of their code while gaining practical capabilities to develop software with security and reliability.

This is a series of video lectures for AWS beginners and college students enrolled in AWS Educate. We will briefly introduce the AWS registration process and free tier services.

This is a course where you can study data structures and algorithms along with the object-oriented language C++.

You can start creating games without difficult programming with Unreal Engine 4's Blueprint.

Let's create a shopping mall with WordPress

This is an Arduino lecture by Park Sung-jun on YouTube.

This is an introductory course in computer science and programming at MIT.

This is the Pen Tool series from the Photoshop Advanced Course provided by Photoshop Revolution Broadcasting.

This is a free Unity basics course by Unity Hub root owner Dong-geun Joo.

Run effective advertising with Google AdWords

This is a basic iOS language course taught by LAte Developer on YouTube.

The first step in learning AI: We've created the perfect curriculum for AI beginners! Second, match the theories and code you've learned! Now it's time for the real world! This is the stage to solidify the foundation and confidence in your knowledge by unraveling the theories you've learned into code.

There is no need to ignore or fear machine learning. Machine learning is easy, but let's think about where to apply machine learning. The direction of machine learning expansion in Java is expected to be similar to the current Python, R strength, and future coexistence of Python, R, and Java, like iOs. The purpose of this lecture is to build a DIKW collaboration system between domain experts and IT professionals.

If you want to be recognized as a machine learning expert in large-scale data environments—from understanding the core framework of Spark Machine Learning to SQL-based data processing through high-difficulty practical problems, and the ability to implement optimized machine learning models through business domain analysis—join this course.

See exam types and trends at a glance! Strengthen your practical skills with approximately 100 questions. The perfect preparation for the AWS AI Practitioner exam, allowing for systematic study even when preparing on your own.

Through this course, students will learn various reinforcement learning theories and implement them themselves, as well as create a reinforcement learning environment to test the reinforcement learning algorithm implemented using Unity Machine Learning Agents.

This is a comprehensive machine learning project course that learns various useful machine learning regression and classification projects along with theory using Python and TensorFlow 2 based on Boston Marathon big data.

Building a Full-Stack System Without Coding: Complete the entire pipeline from data collection to AI/ML analysis and web dashboards using only Korean prompts (Vibe Coding). Owning a 100% Automated Pipeline: Secure an independent stock analysis tool that sends S&P 500 screening and investment opinions via Telegram every morning. Acquiring a Practical Data-Based Investment Framework: Master institutional-level data analysis and risk management capabilities based on source code currently in actual operation.

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 lecture is designed for job seekers preparing for Evaluation and Analysis roles in Samsung Electronics' DS Division (Memory, S.LSI, Foundry, TSP, AVP) and PE, AE, and Solution PE roles at SK hynix, who are struggling with the differences between business units and job functions. You will learn how to categorize job titles by company based on the actual workflow of Product Evaluation roles and how to translate your major-related experiences into professional terminology suitable for resumes and interviews. We will help you establish an application strategy by connecting experiences in Analog circuit design, Verilog/FPGA, process experiments, and data analysis with key industry terms such as Yield, Wafer Map, Shmoo Plot, and Failure Analysis.

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

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.

Through a systematic curriculum of 40 lectures, you will learn everything from the basic theories of AI to the applied AI technologies of each algorithm. Through mathematical intuition and R coding algorithm learning and practice, you will develop immediately applicable AI model development capabilities, covering everything from designing algorithm models for data analysis, machine learning, and their application tools to selecting and utilizing them.

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

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.

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.

We provide guidance on what to study for those starting AI development.

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.

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.

"Throw away the cliché AI theories and chatbot examples scattered all over YouTube. This is Korea's one and only '7 Major Industry Integrated Manufacturing AI Bible' that understands the language of manufacturing and pierces through the essence of the process." Hello, I am Byte Detective, your knowledge sharer. Currently, countless companies and practitioners are calling for 'Manufacturing DX (Digital Transformation)' and 'AI Adoption.' However, the majority of courses on the market are limited to vague theories from IT developers or consultants who have no understanding of the harsh practical realities of the manufacturing field. This course is not a lecture for the sake of theory. By organically combining the actual domain knowledge of Korea's top 7 representative manufacturing industries with the latest AI frameworks, it provides a 'Practical Manufacturing DX Scenario Architecture' that can be applied to the field starting tomorrow.

The automotive and parts manufacturing industry is a field where innovation is essential amid fierce global competition and complex supply chains. This course focuses on how AI technology can be applied in the field, explaining actual cases including production efficiency improvement, quality inspection automation, equipment predictive maintenance, and supply chain optimization. From the foundational understanding needed for AI implementation to strategies and risk management considerations for field application, this course covers topics step by step, providing knowledge and insights that practitioners and managers can immediately utilize.