Support Vector Machines with scikit-learn
7,657명이 이미 등록했습니다.
7,657명이 이미 등록했습니다.
In this project, you will learn the functioning and intuition behind a powerful class of supervised linear models known as support vector machines (SVMs). By the end of this project, you will be able to apply SVMs using scikit-learn and Python to your own classification tasks, including building a simple facial recognition model. This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, and scikit-learn pre-installed. Notes: - You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want. - This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Support Vector Machine (SVM)
작업 영역이 있는 분할 화면으로 재생되는 동영상에서 강사는 다음을 단계별로 안내합니다.
작업 영역은 브라우저에 바로 로드되는 클라우드 데스크톱으로, 다운로드할 필요가 없습니다.
분할 화면 동영상에서 강사가 프로젝트를 단계별로 안내해 줍니다.
AP 제공2020년 7월 9일
Application-based course with detailed knowledge of SVMs along with an implementation in image classification
RD 제공2020년 9월 16일
The course is like a crash course on SVMs with good explanation of concepts.
MS 제공2020년 4월 22일
Learned about SVM.
Need t revisit the code and get most out of it.
Things were concise and that is the strength of the course.
AG 제공2020년 6월 17일
I am grateful to have the chance to participate in an online course like this!