[Free] Python Basics You Must Learn Before Learning Python

If you're planning to learn C language, pay attention to this lecture!!! It's completely free~ I highly recommend it for preliminary study before taking formal courses or individual learning.

From the basics of Numpy and Pandas to data preprocessing, visualization, and crawling, all in one place! This is an introductory Python course for data analysis.

We understand why Attention was needed and how it works by 'implementing it directly with code'. This lecture starts from the structural limitations of RNN and Seq2Seq models, experimentally verifies the information bottleneck problem and long-term dependency issues created by fixed context vectors, and naturally explains how Attention emerged to solve these limitations. Rather than simply introducing concepts, we directly confirm RNN's structural limitations and Seq2Seq's information bottleneck problems through experiments, and implement **Bahdanau Attention (additive attention)** and **Luong Attention (dot-product attention)** one by one to clearly understand their differences. Each attention mechanism forms Query–Key–Value relationships in what way, has what mathematical and intuitive differences in the weight calculation process, and why it inevitably led to later models naturally connects to their characteristics and evolutionary flow. We learn how Attention views sentences and words, and how each word receives importance weighting to integrate information in a form where formula → intuition → code → experiment are connected as one. This lecture is a process of building 'foundational strength' to properly understand Transformers, helping you deeply understand why the concept of Attention was revolutionary, and why all subsequent state-of-the-art NLP models (Transformer, BERT, GPT, etc.) adopt Attention as a core component. This lecture is optimized for learners who want to embody the flow from RNN → Seq2Seq → Attention not through concepts but through code and experiments.

From Python fundamentals to practical data analysis! You will learn practical Python skills for processing, visualizing, and analyzing data while solving business problems.

Learn the basic grammar of the Python language, practice writing simple logic using it, and learn about various libraries.

Anyone can easily create a Python game using the Pygame library, starting from zero base.

This is a basic Python course for non-major beginners. Theory textbooks are provided through Wikidocs, and practice materials are provided through Github.

You can quickly convert MATLAB code to PYTHON by extracting only the PYTHON grammar that matches the MATLAB grammar.

Create Your Own Data Analysis Portfolio with GEMINI CLI Vibe Coding - Start Without Any Charges Data Collection, Analysis, Reports, Dashboard Creation, and Work Automation Even if you're new to data collection, once you learn the principles, prompt writing methods, and how to use VSCode, you can perform data collection, analysis, visualization, reports, and dashboard creation just like a data analyst. From Python Data Collection to Analysis Report Writing with GEMINI CLI and VSCode Prompt Writing and Error Resolution Methods for Foolproof Data Collection and Analysis

Based on over 100 deep learning training sessions, this course systematically organizes the core foundational theories that students found most challenging. The course connects mathematical intuition, model learning principles, and code implementation step by step in a way that even non-majors can understand, deeply covering the fundamental structure and operating principles of how AI models learn, rather than just library usage. This is a practical introductory course designed to help you grow into a skilled engineer who understands AI principles by implementing core deep learning foundational technologies such as gradient descent, loss functions, optimization, perceptrons, multilayer neural networks, and backpropagation through both formulas and code.

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

If you are planning a service, a website is an essential element. Of course, in addition to the service, most of the currently provided program infrastructures operate based on the web. The purpose of this course is to learn how to create a website using Python, which is considered the easiest programming language, and how to set it up to an actual operational state.

This is a basic DFS lecture that explains the way a liberal arts major and working developer studied to get a job :) We have prepared a lecture where you can learn by solving problems yourself, with minimal boring theory lectures! After taking this lecture, you will be able to solve silver level DFS problems on your own according to Baekjoon standards.

A lecture consisting of special lectures by professors from Canadian national universities. It covers Python, Code, Scratch programming introduction/basics and practical applications. After completing the lecture, receive a global certificate from Codesprint : )

"Most similar to real practice! Capture employment, coding competitions, and skills all at once." Perfect preparation for corporate coding tests! Skill improvement from basics to intermediate level! This course contains only the essentials needed for coding test preparation. Structured step-by-step from basic concepts to practical problems, you can simultaneously develop programming skills and practical sense.

During our school days, the 'click' sound we heard in the computer lab... that was Minesweeper. In this course, we'll recreate those memories by implementing it with Python. Whether you're a beginner or even someone encountering Python for the first time, by following along line by line, you'll find that your very own Minesweeper game has been completed before you know it.

Prepare perfectly for the Programmers Coding Essential Competency Certification (PCCE), from Python basics to competency certification!

Leverage Python to learn machine learning techniques, improving real data information extraction and prediction model development skills!

Even office workers who know nothing about coding can easily learn the basic concepts and grammar of programming through practice, and learn file input/output, Excel data processing, automatic creation of MS-Word documents, graph drawing, web scraping, etc.

This course is a hands-on masterclass that completely conquers the evolution of Diffusion Models through papers and code implementation. You'll learn the core models of generative AI, including DDPM (Denoising Diffusion Probabilistic Model) and DDIM, by studying the paper principles and implementing them directly. We analyze step-by-step the background of each model's emergence, mathematical formulations, network architectures (U-Net, VAE, Transformer), training processes (Noise Schedule, Denoising Step), and the ideas that led to performance improvements. Students will directly code all models using PyTorch, gaining not just paper comprehension but 'practical skills to reproduce and apply' them in real-world scenarios. Additionally, by comparing the differences between models and their developmental flow, you'll clearly understand how they expand and evolve. This course integrates theory, code, and practice into one comprehensive journey, providing researchers, developers, and creators alike with a systematic way to master the evolution of generative models. Beyond simply 'reading' papers, start your experience of 'understanding and recreating' through direct implementation now.

This is a Python coding test lecture centered on problem solving, tailored to those who are new to coding tests.