Introduction au NLP
Explorons les fondamentaux du Traitement Automatique du Langage Naturel (NLP) en vous plongeant dans les techniques de prétraitement de texte et divers modèles de texte utilisés pour représenter les données textuelles. Vous acquerrez des connaissances pratiques et une expérience pratique avec les outils et méthodes essentiels pour analyser et interpréter efficacement les données textuelles. Ce cours vous dote des compétences nécessaires pour transformer le texte brut en informations significatives, ouvrant la voie à des applications avancées en IA et apprentissage automatique.Show more
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Outcomes
Learning outcomes
Modules
Testimonials
Recommendations
Codefinity advantages
FAQs
What you will learn
- Comprendre les fondamentaux du prétraitement de texte, y compris la tokenisation et la suppression des mots vides.
- Comprendre le processus de stemming et de lemmatisation pour les données textuelles.
- Découvrir les modèles de texte de base et comment représenter efficacement l'information textuelle.
- Se familiariser avec les embeddings de mots et la mise en œuvre de Word2Vec.
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There are 6 modules in this course
Explorons les fondamentaux du Traitement Automatique du Langage Naturel (NLP) en vous plongeant dans les techniques de prétraitement de texte et divers modèles de texte utilisés pour représenter les données textuelles. Vous acquerrez des connaissances pratiques et une expérience pratique avec les outils et méthodes essentiels pour analyser et interpréter efficacement les données textuelles. Ce cours vous dote des compétences nécessaires pour transformer le texte brut en informations significatives, ouvrant la voie à des applications avancées en IA et apprentissage automatique.Nous commencerons notre voyage en apprenant et en mettant en œuvre les techniques de prétraitement de texte les plus courantes utilisées en NLP pour convertir le texte brut initial en une forme propre et standardisée.
- NLP et Ses ApplicationsPreview
- Comprendre le Prétraitement du TextePreview
- TokenisationPreview
- Défi : Tokenisation d'une PhrasePreview
- Tokenisation à l'Aide d'Expressions RégulièresPreview
- Défi : Tokenisation en Utilisant RegexPreview
- Suppression des Mots VidesPreview
- Appliquer le Prétraitement du Texte en PratiquePreview
Sans plus tarder, explorons la racine et la lemmatisation. Ces techniques peuvent améliorer l'efficacité et l'efficience de certaines tâches de NLP, surtout lorsqu'on travaille avec de grands corpus de texte et qu'on traite différentes formes de mots comme le même mot.
Le texte prétraité doit ensuite être transformé en une représentation numérique pour être utilisé dans des modèles d'apprentissage automatique ou d'apprentissage profond pour diverses tâches telles que la prédiction, la classification ou le regroupement. Ici, nous apprendrons à implémenter les modèles de texte les plus basiques mais populaires qui transforment les données textuelles en nombres.
Il est temps de libérer la puissance des incorporations de mots et de plonger dans des techniques avancées pour capturer les relations sémantiques entre les mots. Nous explorerons divers modèles d'incorporation tels que Word2Vec, GloVe et FastText, avec un accent particulier sur le modèle Word2Vec et son implémentation.
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