[Renewed] Beginner’s Python Machine Learning Bootcamp (Easy! Learn by Solving Real Kaggle Problems) [Data Analysis/Science Part 2]

This is a full-stack series aimed at beginners, with the goal of developing web and app services to a level that can be genuinely commercialized, based on real-world experience developing actual web services. As the first step (part1-1), this course provides an in-depth explanation of intermediate Python for full-scale development, backend implementation with Flask [플라스크], and essential web technologies such as HTTP, REST API, and the MVC pattern.

This course teaches the fundamental skills needed to learn the entire Python data analysis process for beginners in data analysis. As an e-commerce planner and developer who actively uses data analysis techniques in the field, I designed it to help you easily learn the full Python data analysis process and apply it right away.

For those who want to learn data science, big data, and web scraping, (1) we summarize the most essential Python syntax in a short time, and (2) help you become familiar with Python and web scraping through practical scraping programming.

Master intermediate-to-advanced SQL and build solid SQL data analysis skills by solving 101 real-world data analysis problems together.

Through this course, you will learn about SQL and databases, SQL syntax, database design for practical use, how to use databases through programming, and real-world applications of data analysis.

A full-stack series course aimed at beginners, based on real-world experience developing actual web services, with the goal of building web and app services to a level suitable for actual commercialization. As Part 2 of this series, this course thoroughly covers the latest HTML5, CSS3, and Vanilla JavaScript (Javascript) + ES6, including responsiveness and compatibility, among the many available frontend technologies.

This is Part 1 of a roadmap course that neatly organizes, in a short amount of time, the essential computer science (CS) knowledge you need to excel at development and data. This course has also been used for years as an in-house course at top companies in Korea. Based on 20 years of experience in the field, ranging from systems software to web and app development and data science, this course systematically and thoroughly covers the core concepts you need to excel at development and data—while keeping them easy to understand.

Learn NoSQL technology for handling big data, one of the fundamental technologies used in the full-stack and data science fields at modern startups. MongoDB is one of the easiest NoSQL technologies to learn and use quickly. In this course, you will learn the basics of MongoDB in a short time, as well as how to work with and utilize MongoDB using Python.

This course has been newly designed to help you gradually learn the mathematics and theory needed to understand deep learning, PyTorch-based implementation, transfer learning, and the core GPT Transformer, based on the instructor’s experience of struggling when first learning deep learning.

A FastAPI course for modern full-stack and backend development that is easy to use and delivers high performance. It covers backend concepts step by step, the core syntax of FastAPI, and asynchronous database usage, all organized clearly through well-structured materials and projects.

This course covers everything from the basics of Dart programming to Flutter’s fundamental and intermediate features, and even integrating the backend Firebase to build a real-world project! It is a comprehensive course that lets you learn Flutter step by step.

This course is Part 3 of the Full-Stack course series, designed to help you thoroughly master the latest server technologies and Docker. To enable you to genuinely incorporate Docker into your tech stack, the course meticulously tests Docker’s various options, covers related technologies such as Linux that you need to understand for Docker, and is structured so that you can even build complex real-world services.

This course teaches the fundamental skills needed to learn the entire Python data analysis process for beginners in data analysis. As an e-commerce planner and developer who actively uses data analysis techniques in the field, I designed it to help you easily learn the full Python data analysis process and apply it right away.

Learn how to understand advanced machine learning models without math and implement them easily in Python.

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

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.

If you ask an AI to "build a model with this data," the first few lines might look plausible, but as soon as things get slightly complex, it starts using the wrong columns, introduces data leakage, and results in a model that performs worse despite a higher score. This course does not teach you better prompts. Instead, it covers how to directly design a loop (harness) where an agent understands data, plans, writes code, validates its own results, and backtracks when wrong. After manually walking through the five-piece loop of DS-STAR, you will assemble components one by one, from data profile cards, hooks, and skill hierarchies to independent validators and ablation.

This is a lecture on data visualization using the Matplotlib library.

In the [Learn Machine Learning with AutoKeras] lecture, you can learn about AutoKeras, which was developed from the Keras library previously used in machine learning, and use it directly through examples.

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.

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 learn machine learning, you learn how to improve accuracy. But the moment you try to put that model onto an actual chip, questions no one has answered remain. How many multiplications does this model perform? How much does it need to remember, and for how long? Can the chip specifications you are targeting handle those requirements? Machine learning textbooks talk about accuracy, and semiconductor textbooks talk about circuits, but materials that bridge the gap between them are rare. I repeatedly encountered this gap while implementing RISC-V CPUs and NPUs on FPGAs. The model with the highest accuracy often turned out not to fit on the board. In this course, you will build six algorithms directly in Python, measuring the number of multiplications, comparisons, memory usage, and accuracy each time. Then you will convert those figures into the resources of an actual FPGA board. Saying that something requires a certain number of multiplications is not enough to gauge its size, but knowing that it uses a certain percentage of the board makes the decision clearer. After nine Labs, when the six models are brought together in a single table, an unexpected picture emerges. We spent nine lessons counting multiplications, but multiplications were not what held us back. After that, we reduce floating-point values to integers, manage bit widths, and even establish criteria for evaluating the circuit. You do not need an FPGA board. The course is conducted entirely in Python, and the board is used only as a reference for converting the measurements.

Through this course, you will learn about SQL and databases, SQL syntax, database design for practical use, how to use databases through programming, and real-world applications of data analysis.

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.

Learn NoSQL technology for handling big data, one of the fundamental technologies used in the full-stack and data science fields at modern startups. MongoDB is one of the easiest NoSQL technologies to learn and use quickly. In this course, you will learn the basics of MongoDB in a short time, as well as how to work with and utilize MongoDB using Python.

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.

We guide non-majors and beginners to quickly obtain the Big Data Analysis Certification (Practical Exam)! Keep the theory light and the practice solid—focusing on core points that are guaranteed to appear on the exam through past questions, without the need for complex background knowledge.

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.

Time Series: Become a Data Analysis Expert! Let's be analysts strong in theory & practice!

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.

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.

"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.