Utilisez des modèles supervisés non linéaires

بواسطة: OpenClassrooms

Overview

Dans le cours Entraînez un modèle prédictif linéaire, vous avez appris à construire des modèles linéaires de classification binaire ou multi-classe et de régression.

Mais ceux-ci peuvent ne pas être adaptés à la nature de vos données. Dans ce cours, vous apprendrez à entraîner des modèles supervisés non-linéaires sur vos données.

Vous comprendrez comment construire un modèle non-linéaire grâce à une redescription des données, et saurez utiliser les méthodes à noyaux, qui permettent d’étendre les notions de SVM et de régression ridge au cas non-linéaire.

Prérequis :

Ce cours fait partie du parcours Data Scientist. Il se situe au croisement des mathématiques et de l'informatique. Pour en profiter pleinement, n'hésitez pas à vous rafraîchir la mémoire, avant ou pendant le cours, sur :

  • Python pour le calcul numérique (numpy) et la création de graphiques (pyplot), que nous utiliserons dans les parties TP du cours,
  • Quelques notions d'algèbre linéaire : manipulation de vecteurs, multiplications de matrices, normes, et valeurs/vecteurs propres,
  • Quelques notions de probabilités et statistiques, telles que distribution de loi de probabilité et variance,
  • Les notions de régression linéaire ridge et SVM.

Syllabus

Part #1 - Utilisez des noyaux pour appliquer des algorithmes linéaires au cas non-linéaire
1. Transformez un problème non-linéaire en un problème linéaire
2. Classifiez vos données avec une SVM à noyau
3. Apprenez des étiquettes réelles avec une régression ridge à noyau
Quiz: Partie 1

Part #2 - Entraînez un réseau de neurones artificiels
1. Entraînez un réseau de neurones simple
2. Empilez les perceptrons
Quiz: Partie 2

Taught by

Chloé-Agathe Azencott

Utilisez des modèles supervisés non linéaires
الذهاب الي الدورة

Utilisez des modèles supervisés non linéaires

بواسطة: OpenClassrooms

  • OpenClassrooms
  • مجانية
  • French
  • متاح شهادة
  • متاح في أي وقت
  • الجميع
  • N/A
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