이 강좌는 머신 러닝에 관심이 있으며 데이터 분석 및 자동화에 머신 러닝을 적용하길 원하는 전문가를 위한 강좌입니다. 이 강좌는 금융, 의약품, 공학, 비즈니스 등 분야와 상관없이 머신 러닝 프로젝트에서 문제를 정의하고 데이터를 준비하는 방법을 소개합니다.이 강좌를 수료하고 나면 머신 러닝 문제를 두 가지 접근 방법으로 정의할 수 있을 것입니다. 또한 이용 가능한 데이터 자료를 조사하고 잠재적 ML 적용을 알아보는 방법을 알게 될 것입니다. 비즈니스 니즈를 파악하고 실용 머신 러닝에 적용하는 방법을 알게 될 것입니다. 그리고 머신 러닝을 효과적으로 적용하기 위해 데이터를 준비할 수 있을 것입니다.이 강좌는 Coursera와 Alberta Machine Intelligence Institute에서 준비한 첫 번째 실용 머신 러닝 전문 과정입니다.
실용 머신 러닝 소개
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There are 4 modules in this course
이번 주는 머신 러닝(ML)이 무엇인지 배우고, 다양한 문제 상황을 비교해보고, ML에 대해 흔히 무엇을 잘못 알고 있는지 알아볼 것입니다. 이 내용을 배우고 나면 머신 러닝 비즈니스 솔루션에 필요한 요소를 파악할 수 있습니다.
What's included
12 videos6 readings2 assignments3 discussion prompts
이번 주에는 비즈니스 니즈를 머신 러닝 문제로 변환하는 방법을 배울 것입니다. 잘 정의된 QuAM 질문을 만드는 방법을 알 수 있도록 몇 가지 적용 사례를 알아볼 겁니다. ML에 성공하려면 질문의 범위를 좁히고 학습에 필요한 데이터를 확보했는지 확인하는 것이 중요합니다!
What's included
8 videos4 readings1 assignment2 discussion prompts
이번 주는 전부 데이터에 관한 내용입니다. 데이터 수집과 학습 데이터의 다양한 출처에 대해 알아볼 것입니다. 데이터가 얼마나 필요한지, 윤리적 문제 등 어떤 실수를 하게 될 수 있는지 알아볼 것입니다.
What's included
9 videos2 readings1 assignment2 discussion prompts
이번 주는 머신 러닝 프로세스 라이프사이클(MLPL)에 대해 배울 것입니다. MLPL의 정의와 구성 요소를 이해하고 사례 연구를 통해 MLPL의 적용 사례를 분석합니다.
What's included
7 videos2 readings1 assignment2 discussion prompts
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Vanderbilt University
University of Virginia
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Johns Hopkins University
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