Support Vector Machine Classification in Python
import the dataset and perform training/testing set splits
Apply feature scaling for normalization
Build an SVM classifier and make Predictions
Build a Confusion Matrix and Visualize the results
In this 1-hour long guided project-based course, you will learn how to use Python to implement a Support Vector Machine algorithm for classification. This type of algorithm classifies output data and makes predictions. The output of this model is a set of visualized scattered plots separated with a straight line. You will learn the fundamental theory and practical illustrations behind Support Vector Machines and learn to fit, examine, and utilize supervised Classification models using SVM to classify data, using Python. We will walk you step-by-step into Machine Learning supervised problems. With every task in this project, you will expand your knowledge, develop new skills, and broaden your experience in Machine Learning. Particularly, you will build a Support Vector Machine algorithm, and by the end of this project, you will be able to build your own SVM classification model with amazing visualization. In order to be successful in this project, you should just know the basics of Python and classification algorithms.
개발할 기술
- Machine Learning
- Python Programming
- Support Vector Machine (SVM)
- classification
- Supervised Learning
단계별 학습
작업 영역이 있는 분할 화면으로 재생되는 동영상에서 강사는 다음을 단계별로 안내합니다.
Understand the concept of building a Support Vector Machine classification algorithm with a real-world example
Import and explore the dataset and libraries: numpy, pandas and matplotlib
Split the dataset into training set and testing set
Apply feature scaling to normalize the input features
Fit the SVM classifier to the dataset and making predictions
Visualize training and testing sets results
안내형 프로젝트 진행 방식
작업 영역은 브라우저에 바로 로드되는 클라우드 데스크톱으로, 다운로드할 필요가 없습니다.
분할 화면 동영상에서 강사가 프로젝트를 단계별로 안내해 줍니다.
검토
SUPPORT VECTOR MACHINE CLASSIFICATION IN PYTHON의 최상위 리뷰
Straight to the point, take a little bit of time and it is very useful for anyone seeking more knowledge in this domain. Thumbs up to the instructor.
it was a little bit hard to get message, when he did not write the code for visualizing and copied it. My code never get executed even I copied.
Good Course. But voice of instructor was not clear enough and he was very slow. Overall, I have learned some new things. Thanks Instructor.
Gave me a good intuition for applying SVM classifier in python as well as visualising predictions, thanks for guiding me through this.
자주 묻는 질문
자주 묻는 질문
안내 프로젝트를 구매하면 어떤 혜택이 있습니까?
데스크톱과 모바일에서 안내 프로젝트를 이용할 수 있습니까?
안내 프로젝트 강사는 누구입니까?
작업 완료 후 안내 프로젝트에서 해당 작업을 다운로드할 수 있습니까?
환불 규정은 어떻게 되나요?
재정 지원을 받을 수 있나요?
안내 프로젝트를 청강하고 동영상 일부를 무료로 볼 수 있습니까?
이 안내 프로젝트를 수행하려면 경험이 얼마나 필요합니까?
소프트웨어를 별도로 설치하지 않아도 바로 웹 브라우저를 통해 이 안내 프로젝트를 완료할 수 있습니까?
안내 프로젝트를 통해 어떤 학습 경험을 할 수 있습니까?
궁금한 점이 더 있으신가요? 학습자 도움말 센터를 방문해 보세요.
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