인프런 영문 브랜드 로고
인프런 영문 브랜드 로고
AI

/

Deep Learning & Machine Learning

Machine Learning (Basic Theory) That Even My Mom Can Do

First Steps in Learning AI: We've created the best curriculum for AI beginners! The first step is to get familiar with the basic theory of AI! Learn the basic concepts of machine learning through various examples and take a quiz to review the theory!

30 students are taking this course

Machine Learning(ML)
Scikit-Learn
Thumbnail

This course is prepared for Beginners.

What you will learn!

  • ⭐ What is Machine Learning?

  • ⭐ Understanding the working principles and evaluation indicators of classification, regression, clustering, and recommendation system models

  • ⭐ Master the basic theory with various examples and animations!

  • ⭐ Check what you learned with a note test!

📢 This lecture is for non-majors.

Artificial intelligence, solved very easily!

I have excluded statistical and mathematical concepts as much as possible!

Theory lectures, don't be afraid!

Machine Learning That Even Our Mom Can Do (Basic Theory)

Artificial Intelligence (AI)

Machine Learning

Scikit-Learn

Course Introduction

  • Theory is the stepping stone for various code applications.

  • As a non-major, I won the grand prize and excellence award in contests, the first prize in competitions, and the grand prize and excellence award for projects in just 5 months .

  • You need to know the principles to be able to apply them to various situations and data.

  • When learning about artificial intelligence for the first time, various terms are thrown around, and the learning order felt unfamiliar while learning according to the curriculum at the institution. We thought a lot about it and modified and arranged the order so that even beginners can follow along with as little inconvenience as possible .

  • Rather than simply explaining mathematical and statistical concepts, when related references are made to models or indicators, the necessity of the corresponding formula or concept is mentioned, making it much easier to understand and convince, making learning smoother.

Lecture outline

  • We use only the minimum mathematical and statistical concepts necessary for understanding, and even then, we have organized them all into examples for easy understanding.

  • By using various visual aids and animations, we have minimized unnecessary lines of text in the material to make it less boring even though it is a theoretical lecture.


  • Those who do not know about machine learning will be able to learn systematically and broadly without feeling burdened, and those who know about machine learning will be able to establish the concept accurately once again.

  • This is a basic course that covers almost all parts of machine learning that can be intuitively understood, and concepts such as SVM, ROC-AUC, and dimensionality reduction natural language processing (NLP) are covered in the advanced machine learning theory.

  • Since all lectures focus on future deep learning lectures, we recommend this course to those who want to build a solid foundation from machine learning .

Lecture Features

🎯 This course consists of only theory lectures without any code practice.

🎯 PPT and animation materials provided

🎯 Theory note test provided for review

Step-by-step learning content

This course is the first of five curricula. The remaining curricula will be released sequentially.

Lecture Preview

Some animated material on Similarity Search. (No audio description)

Mean-Shift clustering among the three clustering methods

One of the slides.

Among the four recommendation systems, content-based filtering

One of the slides.

One of the example slides explaining the CART model.

One of the slides on model validation and evaluation metrics.

Part 2 of the animation on the differences between simple linear regression models and multiple linear regression models. (No audio explanation)

Understand what you've learned through code, without coding.

Understanding gradient descent in a step-by-step approach.

Recommended for
these people!

Who is this course right for?

  • ⭐ Artificial Intelligence Basics

  • ⭐ People who want to study artificial intelligence but don't know where to start

  • ⭐ People who find it difficult to complete the course due to the high entry barrier caused by formulas and complex terms

  • ⭐ People who want to study with a systematic curriculum

  • ⭐ People who want to take a machine learning concept and theory course

Need to know before starting?

  • (No prior knowledge of Python or data tools is required for the theoretical lectures.)

Hello
This is

인공지능 사관학교 5기 수료

시계열 농산물 가격 예측 프로젝트 대상

케글 경진대회 1등 (200 )

객체 탐지, RAG 기반 모의면접 프로젝트 우수상

한국인공지능협회 주관 AI활용 사회문제 해결 공모전 최우수상

호남 ICT이노베이션 디지털 신기술 공모전 우수상

Curriculum

All

21 lectures ∙ (4hr 11min)

Published: 
Last updated: 

Reviews

Not enough reviews.
Become the author of a review that helps everyone!