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Artificial Intelligence Basics and Career Advice for Non-Majors/High School Students

This is a lecture for non-majors or high school students who are studying data science and artificial intelligence for the first time. We will teach you the concepts of artificial intelligence and machine learning and what efforts you need to make to get a related job.

(5.0) 1 reviews

13 learners

  • coco
선형회귀분석
군집분석
입문
cnn
Machine Learning(ML)
Deep Learning(DL)

What you will learn!

  • Basic concepts of machine learning and artificial intelligence

  • Types of machine learning models

  • Deep Learning Model Types and Concepts

  • Career paths related to artificial intelligence

  • How to study artificial intelligence/data analysis

To become a DataScientist / AI researcher
Get a firm grip on the first step! 🤖

DataScientist/AI researcher
First step to becoming 👨‍💻

This course is for non-majors or high school students who are studying data science and artificial intelligence for the first time .
We'll help you understand the concepts of artificial intelligence and machine learning, and what you need to do to pursue a career in them.

Do you ever have these concerns?

"Data Science and AI are on the rise. Can I do that too?"
"Can liberal arts students do this? Is graduate school required? I don't know where to start."

Let me explain the basic concepts one by one.

Machine learning, you'll understand it perfectly after taking the course!

Therefore, this lecture focuses on the basic concepts of machine learning and artificial intelligence, rather than focusing on the theory and practice of machine learning, making it easy for beginners (high school students and non-majoring college students) to understand. We explain the concepts and types of AI and machine learning, and introduce related careers. We explain the skills required for these careers and what companies actually look for in them. Finally, we explain how to study for each career and what majors are advantageous.


Who would benefit from listening?

Basic concepts of machine learning and AI models
Anyone who wants to know

Career as a data scientist
Those who want to switch

Those who wish to pursue a career in AI/DS
High school students/non-major college students

The course is designed to enable students to at least plan an AI-related career after completing the course. It compares the requirements of actual companies and introduces the differences between AI-related jobs. Furthermore, it introduces relevant departments and majors to help students develop the skills necessary for pursuing a career in this field.

Main contents of the lecture
Check it out 📚

1️⃣ Basic concepts of machine learning and deep learning

To make it accessible even to complete beginners, we begin with "What is data?" We then explain what we can do with this data through machine learning and AI. We'll explain how machine learning models can be broadly categorized, how each model learns, and what results they produce.


2️⃣ Types of machine learning

(Decision Tree, kNN, Ensemble Learning, Clustering, SHAP Value)

We'll cover essential machine learning models. The lectures will be concise and conceptually focused for easy understanding. We'll explore the concepts and strengths and weaknesses of basic models, and we'll also introduce ShapValue, a tool used for explainable AI.


3️⃣ The concept of deep learning models and artificial intelligence generation models DALLE.2

We'll learn about the concepts and types of deep learning models. We'll introduce the capabilities of each model. We'll also experience DALLE.2, an AI-generated model that's recently become a hot topic.


4️⃣ How to evaluate the suitability of the model

How accurate is the model we've created? To determine the accuracy of our model, proper experimental design is crucial. This experimental design is crucial for data analysis. Since this is where many beginners make mistakes, I've tried to explain it in as much detail as possible, making it easy to understand.


5️⃣ What's important in data analysis

Data analysis doesn't simply stop at modeling. It involves a series of steps, starting with preprocessing raw data, generating derived variables, designing experiments, modeling, and finally, interpretation. We'll discuss the key and essential aspects of this process.


6️⃣ Data Science and Artificial Intelligence Related Jobs and Required Skills

Data scientists aren't the only AI-related profession. Broadly speaking, they can be categorized into four categories: data scientist, data analyst, AI/ML engineer, and AI researcher. While they may seem similar, their responsibilities are distinct, and each job requires different tools and skills. Let's examine what companies actually require for each position, introducing the necessary skills and related majors for each position.


Recommended for
these people

Who is this course right for?

  • Non-majors who want to learn artificial intelligence/data science

  • High School Students Want to Get a Taste of Artificial Intelligence

Hello
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Reviews

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Answers

4.4

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Courses

학부에서는 통계학을 전공하고 산업공학(인공지능) 박사를 받고 여전히 공부중인 백수입니다.

 

수상

ㆍ 제6회 빅콘테스트 게임유저이탈 알고리즘 개발 / 엔씨소프트상(2018)

ㆍ 제5회 빅콘테스트 대출 연체자 예측 알고리즘개발 / 한국정보통신진흥협회장상(2017)

ㆍ 2016 날씨 빅데이터 콘테스트/ 기상산업 진흥원장상(2016) 

ㆍ 제4회 빅콘테스트 보험사기 예측 알고리즘 개발 / 본선진출(2016)

ㆍ 제3회 빅콘테스트 야구 경기 예측 알고리즘 개발 / 미래창조과학부 장관상(2015)

* blog : https://bluediary8.tistory.com

주로 연구하는 분야는 데이터 사이언스, 강화학습, 딥러닝 입니다.

크롤링과 텍스트마이닝은 현재는 취미로 하고있습니다 :) 

크롤링을 이용해서 인기있는 커뮤니티 글만 수집해서 보여주는 마롱이라는 앱을 개발하였고

전국의 맛집리스트와 블로그를 수집해서 맛집 추천 앱도 만들었었죠 :) (시원하게 말아먹..)

지금은 인공지능을 연구하는 박사과정생입니다.

 

 

 

 

Curriculum

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22 lectures ∙ (4hr 52min)

Course Materials:

Lecture resources
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1 reviews

5.0

1 reviews

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    hj162kim7928

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