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 such as the number of neighbors and the distance metric.

Cours :

Overview of k-Nearest Neighbor Classification

Perform k-Nearest Neighbor Classification

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Classification Trees

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

Cours :

Overview of Classification Trees

Perform Classification with Decision Trees

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Naive Bayes Classification

Train naive Bayes classification models with and without kernels.

Cours :

Overview of Naive Bayes Classification

Perform Naive Bayes Classification

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Discriminant Analysis Classification

Train linear and quadratic discriminant analysis classification models.

Cours :

Overview of Discriminant Analysis Classification

Perform Discriminant Analysis Classification

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Support Vector Machine Classification

Train binary and multiclass support vector machine (SVM) classification models and change the kernel hyperparameter.

Cours :

Overview of Support Vector Machines

Perform Support Vector Machine Classification

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Classification Ensembles

Combine multiple classification models into an ensemble by using techniques such as bagging, boosting, and random subspace.

Cours :

Overview of Ensemble Learning

Perform Classification with Ensemble Models

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Classification with Neural Networks

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

Cours :

Overview of Neural Networks

Perform Classification with Neural Networks

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Conclusion

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Cours :

Additional Resources

Survey

Modules de formation

Introduction

k-Nearest Neighbor Classification

Classification Trees

Naive Bayes Classification

Discriminant Analysis Classification

Support Vector Machine Classification

Classification Ensembles

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 Classification

Support Vector Machine Classification

Classification Ensembles

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