Méthodes pour la classification avec le Machine Learning

Modules de formation

Introduction

Get an overview of the course.

Cours :

Course Overview

Course Example

k-Nearest Neighbor Classification

Train k-nearest neighbor models and change hyperparameters like the number of neighbors and the distance metric.

Cours :

Overview of k-Nearest Neighbor Classification

Use k-Nearest Neighbor Classification

Quick Reference

Classification Trees

Train decision tree models for classification and modify the tree branching specifications.

Cours :

Overview of Classification Trees

Use Classification Trees

Quick Reference

Naive Bayes Classification

Train naive Bayes classification models with and without kernels.

Cours :

Overview of Naive Bayes Classification

Use Naive Bayes Classification

Quick Reference

Discriminant Analysis

Train linear and quadratic discriminant analysis classification models.

Cours :

Overview of Discriminant Analysis

Use Discriminant Analysis

Quick Reference

Support Vector Machine Models

Train binary and multiclass SVM classification models and change the kernel hyperparameter.

Cours :

Overview of Support Vector Machines

Use Support Vector Machine Classification

Quick Reference

Ensemble Learning

Combine multiple models into an ensemble by using techniques like bagging, boosting, and subspace methods.

Cours :

Overview of Ensemble Learning

Use Ensemble Models

Quick Reference

Classification with Neural Networks

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

Cours :

Overview of Neural Networks

Use Neural Networks for Classification

Quick Reference

Conclusion

Learn next steps and give feedback on the course.

Cours :

Additional Resources

Survey

Modules de formation

Introduction

k-Nearest Neighbor Classification

Classification Trees

Naive Bayes Classification

Discriminant Analysis

Support Vector Machine Models

Ensemble Learning

Classification with Neural Networks

Conclusion

Langue

Des exercices pratiques avec des corrections automatisées
Accès à MATLAB via votre navigateur Internet
Certificat de formation et rapport de progression partageable

Machine Learning Onramp

Méthodes pour la classification avec le Machine Learning

Modules de formation

Introduction

k-Nearest Neighbor Classification

Classification Trees

Naive Bayes Classification

Discriminant Analysis

Support Vector Machine Models

Ensemble Learning

Classification with Neural Networks

Conclusion

Langue

Des exercices pratiques avec des corrections automatiséesAccès à MATLAB via votre navigateur InternetCertificat de formation et rapport de progression partageable

Machine Learning Onramp

Des exercices pratiques avec des corrections automatisées
Accès à MATLAB via votre navigateur Internet
Certificat de formation et rapport de progression partageable