Challenge

Recruiting

AI Technology

Natural Language Processing

Artificial Intelligence using LLM

Learn how to apply tools that utilize artificial intelligence through a fundamental approach to AI.

ywjang23583

Machine Learning(ML)

Statistics

Big Data

classifier

40개 수업 학습

라이브 1 회

ywjang23583님과 함께해요!

I worked as a developer at LG Electronics, a telecommunications company, for about 27 years. Since retiring, I have been teaching introductory software coding courses at various universities, as well as lecturing at vocational schools and government offices. Currently, I am teaching an IoT course at a vocational training school.

I would like to record and share lectures on the following topics.

1. R Statistics Basic/Advanced Course

2. Arduino for the sensor data collection part of IoT technology techniques

3. Raspberry Pi Technology

4. Basic/Advanced Course for AI Utilization (Understanding Basic Algorithms and Tool Usage)

5.Systematic platform implementation techniques for smart farm configuration

6. Tableau and PowerBI visualization techniques

7. Six Sigma technical techniques in the field

8. Building a Big Data Analysis Hadoop Ecosystem

With 27 years of experience, now transform into an AI expert.

I worked as a developer at LG Electronics for 27 years.

After leaving the company, I continued teaching coding at universities and vocational schools. I am still teaching Internet of Things (IoT) courses today.

All of these experiences led to my AI lectures.

However, I wasn't able to teach AI from the very beginning.

Through my experience in the field and in teaching, I have contemplated how to convey AI in an easy and clear manner.

I now want to share with you the fascination of AI, which discovers patterns within data.

This course goes beyond simple theoretical learning and will help you develop practical data analysis skills using R and SPSS Modeler.

Notes before taking the course

Practice Environment

Operating System: Both Windows and macOS are supported.
Required Software: R and RStudio IDE are required.
Recommended Specifications: At least 8GB RAM and 20GB of free disk space are required.

Prerequisite Knowledge and Notices

It is helpful to have an understanding of basic AI and machine learning concepts.
Basic learning experience with the R programming language is helpful.
Since this course is designed for non-majors, it is fine even if you lack a mathematical background.

Learning Materials

Lecture slide PDF files are provided.
Example code and datasets required for the practice sessions are provided.
We provide additional materials for the key algorithms covered in the lecture.

LIVE

함께 소통할 수 있는 라이브가 예정되어 있어요!

07.02.목

오후 01:00

LLM활용 인공지능 알고리즘 학습

6월

30일

챌린지 시작일

2026년 6월 30일 오후 03:00

챌린지 종료일

2026년 7월 31일 오후 02:30

챌린지 커리큘럼

All

41 lectures ∙ (14hr 41min)

Course Materials:

Lecture resources

챌린지 전용 수업

Live

Section 1. Section 1 (Lectures 1-15): Part 1. Principles of Machine Learning Algorithms and NCS-based Core Learning

16 lectures ∙ (5hr 12min)

1. The Beginning of AI Algorithms: Understanding Concepts and Setting Up the Development Environment
21:04
2. Understanding R Data Analysis and Setting Up the R·RStudio Development Environment
20:09
3. The Core of Machine Learning Algorithms: Dataset Composition and Model Performance Optimization Strategies
17:16
4. R-based AI and Machine Learning Practical Course
26:25
5. Artificial Intelligence Algorithms: Creating Configuration Datasets
14:51
6. Artificial Intelligence Algorithm
13:50
7. Artificial Intelligence Algorithm
17:24
8. Artificial Intelligence Algorithm
16:50
9. Machine Learning for Numerical Prediction (Regression)
22:46
10. AI Algorithms in Practice: Mastering Numerical Prediction Models from Multiple Regression to Decision Trees
26:59
11. [R Machine Learning] Mastering Numerical Prediction Modeling Using Artificial Neural Networks (ANN) and Random Forest
24:12
12. A hands-on lecture on data preprocessing, artificial neural networks, and predictive analysis that even beginners can follow.
26:18
13. Master Class on Principal Component Analysis (PCA) Using R
24:18
14. [AI Algorithm in Practice] Finding Hidden Rules in Data: Principles and Key Metrics of Association Analysis (Market Basket Analysis)
21:27
15. [R-based AI Practice] From Numerical Prediction Using Machine Learning to Association Analysis
18:40
Live
Learning Artificial Intelligence Algorithms Using LLM
2026. 07. 02. 목요일 오후 1시 ~ 오후 2시

Section 2. Section 2 (Lectures 16-40): Part 2. Utilizing SPSS Modeler Algorithms and Business Data Applications

25 lectures ∙ (9hr 28min)

16. [AI Practice] Introduction to Data Mining and Predictive Modeling Using SPSS Modeler
15:45
17. Introduction to Data Mining: Business Data Analysis Practice with IBM SPSS Modeler
22:17
18. [Practical AI Modeling] Data Preprocessing and Machine Learning Analysis Using SPSS Modeler (Decision Tree & Artificial Neural Network)
17:09
19. [Practical AI Modeling] Algorithm Comparison Evaluation and Model Deployment Using SPSS Modeler
14:08
20. [AI in Practice] Introduction to Data Mining and Predictive Modeling Using SPSS Modeler
23:55
21. Introduction to Data Analysis for AI Modelers: A Guide to Essential Selection, Joining, and Sampling Nodes
18:03
22. Practical Application of SPSS Modeler AI Algorithms
21:44
23. [Practical AI] Data Analysis and Predictive Modeling Without Coding: SPSS Modeler Master Class
25:14
24. [AI Modeler] Mastering Data Restructuring Practice Using SPSS Modeler
24:15
25. Practical Data Exploration, Statistical Analysis, and Result Output for AI Modelers
28:43
26. [AI in Practice] Mastering Data Binning and Preprocessing Techniques Using SPSS Modeler
22:30
27. [IBM SPSS Modeler Practice] Data Analysis Preprocessing Expert Course: Mastering Reclassification and Restructuring
22:34
28. [Practical AI] Data Analysis and Predictive Modeling Without Coding: SPSS Modeler Master Class
31:38
29. [AI Modeler] Mastering SPSS Modeler Data Preprocessing and Practical Derived Variable Creation
26:31
30. Advanced Data Modeling and Practical Analysis Techniques Using Formulas
29:08
31. [AI Modeling Practice] Hidden Rules in the Shopping Cart: Mastering Association Rule Mining (ARM)
16:13
32. [AI Practice] Mastering Association Rule Mining Using SPSS Modeler
15:34
33. [IBM SPSS Modeler Practice] Data Classification and Prediction Using AI: Mastering Decision Trees
37:02
34. [AI in Practice] Predicting Returning Customers Using SPSS Modeler: A Decision Tree Analysis Case Study
22:22
35. [IBM SPSS Modeler Practice] The Essence of Data Mining: Predictive Modeling Using Neural Networks
23:01
36. [AI Practice] Automated Modeling and Ensemble Techniques Using SPSS Modeler
26:49
37. [AI Modeler] Mastering Practical Data Preprocessing and Automated Modeling Using SPSS Modeler
25:12
38. Exploring Data Patterns and Strategic Utilization Through Unsupervised Learning
17:04
39. The Core of Unsupervised Learning: From the Principles of the K-Means Algorithm to Practical Data Interpretation
25:18
40. AI Modeler: Advanced Clustering Techniques (Two-step Clustering & SOM)
16:47

챌린지에서 배워요

Supervised/Unsupervised algorithm design based on R/RStudio
Building predictive models using machine learning algorithms (regression, classification, ensemble)
Completion of a practical project using an AI GUI utilization system

Recommended for
these people

Who is this course right for?

Non-majors and job seekers preparing for a career transition into the AI field
Working data analysts who want to go beyond data analysis and build capabilities in constructing predictive models.
Full-stack and backend developers looking to integrate AI features into their own services

Need to know before starting?

Basic programming experience (understanding of basic R coding syntax)
High school level math knowledge (no prior study of calculus or linear algebra required)
Interest in AI technology and the determination to complete all 40 lectures

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Artificial Intelligence using LLM

ywjang23583님과 함께해요!

From Non-Major to AI Expert
Step-by-Step Mastery

What you can gain from this course

AI, now even non-majors can start with confidence.

You will be reborn as an expert equipped with practical AI capabilities.
I will be with you throughout your AI career journey.

With 27 years of experience, now transform into an AI expert.

Lecture Plan

First Steps in AI: From Basics to Application

Section 1

Fundamentals of Machine Learning Algorithms and Setting Up the Development Environment

Section 2

SPSS Modeler-based Predictive Modeling and Business Data Applications

Recommended Audience