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Artificial Intelligence Anyone Can Understand for All Office Workers

An introductory AI lecture tailored for those new to AI! Key concepts, from data to generative AI, are simply explained. The goal is practical understanding, enabling you to question and explain AI, not build it.

(4.9) 213 reviews

600 learners

  • jin0choi1216
기획자
실습 중심
AI
Machine Learning(ML)
Deep Learning(DL)
Recommendation System
Generative AI

Reviews from Early Learners

What you will learn!

  • You can easily understand what artificial intelligence is and how it has developed.

  • Quickly grasp core concepts used in real projects, from AI/ML/DL and recommendation systems to generative AI.

  • You can grasp the entire AI workflow, from data collection to preprocessing and model training, at a glance.

"Is artificial intelligence 'someone else's business'?
Starting tomorrow, it will be 'my work'."

Now is the time for everyone to understand AI.



AI services must know ‘artificial intelligence’.

📄 ex) Actual AI planner recruitment notice
· Establishment and planning of AI-based service policies
· AI-based service user flow and screen design
· Implementing commercialization strategies through discovering various service ideas using AI (AX)

What used to be the job of developers only has now become a task required of all workers .


' Artificial Intelligence' Mathematics, Understanding Without Code

"AI is said to be the trend, but are you at a loss as to where to start studying it?"
"What if communication with developers is difficult and the project schedule is delayed?"
"What if you don't understand what 'the model learns' means in a meeting?"

This course is an introductory course on artificial intelligence (AI) for all working professionals .
Explains how artificial intelligence works using analogies and examples .

I recommend this to these people.

For those who want to know AI in a practical way, just as much as necessary

“I was assigned to an AI project, but… I feel lost.”

For those who want to understand artificial intelligence (AI) from the very beginning

"AI is deep learning. There are many terms such as machine learning, but I don't know what the difference is."

Those who are interested in generative AI but now want to learn AI 'properly'

"Isn't ChatGPT AI?"

After class

  • You will understand key keywords such as ChatGPT, LLM, prompt, recommendation system, AI Agent, etc. in a flow-oriented manner.

  • This creates a standard for what ordinary office workers, not developers, should know.

  • Understand the AI project flow and communicate smoothly with developers.

  • In meetings, instead of being the “silent one,” become the “questioner and connecter.”

  • You will be able to explain machine learning, deep learning, generative AI, etc. on your own.


Features of this course

Please introduce the key features and differentiating factors.

Easy explanation that even non-specialists can understand

  • Explains AI concepts using everyday analogies without complex mathematical formulas.

  • It is structured around case studies and intuitive explanations rather than technical terms.

Explaining the entire process from basics to generative AI in a flow-like manner

  • The entire AI project flow from data collection → preprocessing → machine learning → deep learning → generative AI → AI agent is connected as a single story.


Learn about these things.

How AI learns, made easy.

  • The difference between supervised learning and unsupervised learning

  • Difference between regression and classification problems in machine learning

  • Difference between clustering and dimension reduction


We also look at the principles of deep learning and generative AI from a non-specialist's perspective.

  • Difference between machine learning and deep learning

  • How to understand images and text

  • How generative AI like ChatGPT, Gemini, and Claude work

  • Fine-Tuning, How to Create a Company-Customized LLM with the RAG Method

Who created this course

In 2017, after watching the match between Lee Sedol and AlphaGo, I decided to become a person who can handle artificial intelligence and started studying.


When I first started learning about artificial intelligence, I didn't understand the explanations along with the complex mathematics because I lacked knowledge of mathematics. I studied by making various examples to help myself understand, and through that experience, I became an instructor who thought, 'How can I convey it easily?' I think that education is meaningful only when the listener understands, and I am currently actively conducting education in the fields of artificial intelligence and data.


Progress lecture review

  • I liked that the difficulty level was set appropriately so that it wasn't too difficult, and it was very helpful because only the content necessary for practical work was selected and taught.

  • The structure of the course content was the most interesting of all the courses I have ever taken. The way the theory was learned through practice was also very helpful in increasing understanding.

  • The lecture content was informative and the instructor made it fun so I was able to listen without difficulty.

  • I felt the instructor's rich lectures and his passion and expertise in trying to teach as much as possible in one day.

  • I liked that it consisted of content that seemed applicable to actual work.
    I liked the instructor's appropriate humor and detailed, kind explanations.

  • Beyond simply 'asking good questions!', it was a depth of knowledge that I would never have known if it weren't for this lecture. I learned it in a fun way without losing interest until the end. The instructor was also cheerful and encouraged me to participate in the lecture with concentration until the end, which was great.

  • The theory/practice structure was good, and the instructor's communication skills were good.

  • The instructor's easy explanations helped me understand better.

Do you have any questions?

Q. How practical will this course be for me?

This lecture goes beyond simple explanation of concepts.
We focus on the context in which AI technology should be known .
For example, "We want to add a recommendation system to our service, but what kind of data should we prepare and how?"
It covers knowledge related to practical work-related concerns , such as "What terms should I know when talking to developers?"

Q. Is it okay if I really don’t know anything about AI?

It's really okay.
This course is designed for non-majors and does not contain any complex formulas or code explanations.
Don't worry if you come across terms you don't know.
It is structured so that even those who are new to AI can understand it, as it explains it step by step using everyday examples and analogies . The goal is to give beginners the realization , "Ah, this is it!"

Q. Does it also include the knowledge required to write an AI plan or proposal?

This lecture goes beyond simply explaining the concepts, and covers key questions that must be addressed when writing an AI plan.
“Do we really need AI for this problem?”
“What data can we obtain and utilize?”
It provides a practical framework for the question, “What is the standard for AI success?”


In addition , in line with the recent increase in the introduction of generative AI, ‘prompt engineering’ will also be covered as a separate session.
This article focuses on how to use ChatGPT or LLM to get the answers you want.

Recommended for
these people

Who is this course right for?

  • Office worker told to use AI, but overwhelmed where to start.

  • PM facing difficult developer communication and frequent AI project delays.

  • For those who need to include 'AI' in their proposals but don't know what to write.

  • For those with lots of data but unsure how to use it.

  • A practitioner who needs criteria to judge 'Is this AI really necessary?'

Hello
This is

607

Learners

213

Reviews

4.9

Rating

2

Courses

어려운 것을 쉽게, 쉬운것을 재미있게
링크드인 : https://www.linkedin.com/in/jin0choi/

 

전) 뮤팟 Data Scientist
현) 데이터 분석, 인공지능, 업무자동화, 생성형AI 활용 강사

  • 기업: 경남에너지, 국가보안기술연구소, 대상 주식회사, 메트라이프 생명보험, 멀린엔터테인먼트코리아, 세라젬, 시너스텍, 삼성카드, 삼성화재, 오뚜기, 카카오, 캐논코리아, 케이엔웍스, 중앙그룹, 한국투자금융지주, 현대코퍼레이션, SK 그룹

  • 기관: 경기과학진흥원, 경북ICT이노베이션스퀘어, 국토안전관원, 농림축산식품부, 문화체육관광부, 한국과학기술교육원, 한국데이터산업진흥원, 한국문화정보원, 한국능률협회

  • 부트캠프: DMC코넷, POSCO, 데잇걸즈, 멀티캠퍼스, 알파코, 청년취업사관학교, 코드스테이츠

  • 대학교: 강릉원주대학교, 강릉원주대학교, 경상과학기술대학교, 경상국립대학교, 대구대학교, 상지대학교, 전남대학교, 충남대학교, 충북대학교, 홍익대학교

 

Curriculum

All

18 lectures ∙ (3hr 4min)

Course Materials:

Lecture resources
Published: 
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Reviews

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213 reviews

4.9

213 reviews

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