Vous voulez en savoir plus sur Vertex AI Feature Store ? Vous souhaitez découvrir comment améliorer la précision de vos modèles de ML ? Comment déterminer les colonnes de données présentant les caractéristiques les plus utiles ? Bienvenue dans le cours "Feature Engineering", dans lequel vous apprendrez à distinguer les caractéristiques pertinentes de celles qui ne le sont pas. Vous verrez aussi comment les prétraiter et les transformer pour les utiliser de manière optimale dans vos modèles. Ce cours inclut du contenu et des ateliers portant sur l'extraction de caractéristiques à l'aide de BigQuery ML, Keras et TensorFlow.
Этот курс входит в специализацию ''Специализация Machine Learning with TensorFlow on Google Cloud en Français'
Feature Engineering en Français
Об этом курсе
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Специализация Machine Learning with TensorFlow on Google Cloud en Français: общие сведения
Qu'est-ce que le machine learning et quels types de problèmes permet-il de résoudre ? Quelles sont les cinq phases permettant de traiter un cas d'utilisation à l'aide du machine learning, et pourquoi chaque étape est-elle essentielle ? Pourquoi les réseaux de neurones sont-ils désormais si courants ? Comment définir un problème d'apprentissage supervisé et trouver une solution adaptée et généralisable à l'aide de la descente de gradient et d'une méthode pertinente de création d'ensembles de données ? Apprenez à créer des modèles de machine learning distribués qui pourront évoluer dans TensorFlow, à adapter l'entraînement de ces modèles pour bénéficier d'une évolutivité horizontale et à obtenir des prédictions très performantes. Convertissez les données brutes en caractéristiques de sorte que les processus de ML soient en mesure d'identifier les propriétés importantes dans les données et générez des insights qui ont du sens en rapport avec la problématique. Enfin, découvrez comment intégrer à la fois la combinaison de paramètres permettant d'obtenir des modèles précis et généralisés, et une connaissance de la théorie indispensable pour résoudre des types spécifiques de problèmes de ML.
Vous expérimenterez le ML de bout en bout en commençant par créer une stratégie centrée sur le ML, puis en progressant dans le processus d'entraînement, d'optimisation et de production de modèles grâce à des ateliers pratiques faisant appel à Google Cloud Platform. >>> En vous inscrivant à cette spécialisation vous acceptez les conditions d'utilisation de Qwiklabs décrites dans la FAQ et disponibles à l'adresse: https://qwiklabs.com/terms_of_service <<<
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Last Updated: September 1, 2015
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