머신러닝을 사용한 회귀 방법

교육과정 모듈

Introduction

Get an overview of the course.

강의 내용:

Course Overview

Course Example

Linear Regression Models

Train linear regression models using linear, quadratic, and interaction terms.

강의 내용:

Overview of Linear Regression Models

Use Linear Regression Models

Quick Reference

Stepwise Linear Regression

Train a stepwise linear regression model by specifying the initial terms and setting an upper bound on the terms.

강의 내용:

Overview of Stepwise Linear Regression

Use Stepwise Linear Regression

Quick Reference

Regularized Linear Regression

Train regularized linear regression models by applying a penalty to the coefficient values.

강의 내용:

Overview of Regularized Linear Regression

Use Regularized Linear Models

Quick Reference

Support Vector Machine

Train SVM models for regression and change the kernel function hyperparameter.

강의 내용:

Overview of Support Vector Machine

Use SVM Models

Quick Reference

Decision Trees and Tree Ensembles

Train decision tree models for regression and change the minimum leaf size hyperparameter. Train ensembles of trees using techniques like bagging and boosting.

강의 내용:

Overview of Decision Trees and Tree Ensembles

Use Decision Trees and Tree Ensembles

Quick Reference

Neural Networks

Train neural networks for regression and change hyperparameters such as the number fully connected layers and the layer size.

강의 내용:

Overview of Neural Networks

Use Neural Networks

Quick Reference

Gaussian Process Regression

Train GPR models by setting the basis and the kernel functions.

강의 내용:

Overview of Gaussian Process Regression

Use Gaussian Process Regression

Quick Reference

Conclusion

Learn next steps and give feedback on the course.

강의 내용:

Additional Resources

Survey

교육과정 모듈

Introduction

Linear Regression Models

Stepwise Linear Regression

Regularized Linear Regression

Support Vector Machine

Decision Trees and Tree Ensembles

Neural Networks

Gaussian Process Regression

Conclusion

언어

자동화된 피드백이 제공되는 실습 연습문제
웹 브라우저를 통해 MATLAB 이용
공유 가능한 진도 보고서 및 교육과정 수료증

Machine Learning Onramp

머신러닝을 사용한 회귀 방법

교육과정 모듈

Introduction

Linear Regression Models

Stepwise Linear Regression

Regularized Linear Regression

Support Vector Machine

Decision Trees and Tree Ensembles

Neural Networks

Gaussian Process Regression

Conclusion

언어

자동화된 피드백이 제공되는 실습 연습문제웹 브라우저를 통해 MATLAB 이용공유 가능한 진도 보고서 및 교육과정 수료증

Machine Learning Onramp

자동화된 피드백이 제공되는 실습 연습문제
웹 브라우저를 통해 MATLAB 이용
공유 가능한 진도 보고서 및 교육과정 수료증