Einführung in NLP
Lassen Sie uns die Grundlagen der Verarbeitung natürlicher Sprache (NLP) erkunden, während Sie sich mit Techniken zur Textvorverarbeitung und verschiedenen Textmodellen befassen, die zur Darstellung von Textdaten verwendet werden. Sie werden praktische Einblicke und praktische Erfahrungen mit den Werkzeugen und Methoden gewinnen, die für die effektive Analyse und Interpretation von Textdaten unerlässlich sind. Dieser Kurs stattet Sie mit den Fähigkeiten aus, Rohtext in bedeutungsvolle Informationen zu verwandeln und den Weg für fortgeschrittene Anwendungen in KI und maschinellem Lernen zu ebnen.Show more
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Outcomes
Learning outcomes
Modules
Testimonials
Recommendations
Codefinity advantages
FAQs
What you will learn
- Verstehen Sie die Grundlagen der Textvorverarbeitung, einschließlich Tokenisierung und Entfernung von Stoppwörtern.
- Verstehen Sie den Prozess der Stamm- und Lemmatisierung für Textdaten.
- Entdecken Sie grundlegende Textmodelle und wie man Textinformationen effektiv darstellt.
- Machen Sie sich mit Wort-Embeddings und der Implementierung von Word2Vec vertraut.
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There are 6 modules in this course
Lassen Sie uns die Grundlagen der Verarbeitung natürlicher Sprache (NLP) erkunden, während Sie sich mit Techniken zur Textvorverarbeitung und verschiedenen Textmodellen befassen, die zur Darstellung von Textdaten verwendet werden. Sie werden praktische Einblicke und praktische Erfahrungen mit den Werkzeugen und Methoden gewinnen, die für die effektive Analyse und Interpretation von Textdaten unerlässlich sind. Dieser Kurs stattet Sie mit den Fähigkeiten aus, Rohtext in bedeutungsvolle Informationen zu verwandeln und den Weg für fortgeschrittene Anwendungen in KI und maschinellem Lernen zu ebnen.Wir beginnen unsere Reise mit dem Erlernen und Implementieren der gängigsten Textvorverarbeitungstechniken, die in der NLP verwendet werden, um den ursprünglichen Rohtext in eine saubere, standardisierte Form zu bringen.
- NLP und Seine AnwendungenPreview
- Verstehen der TextvorverarbeitungPreview
- TokenisierungPreview
- Herausforderung: Tokenizing eines SatzesPreview
- Tokenisierung Mit Regulären AusdrückenPreview
- Herausforderung: Tokenisierung Mit RegexPreview
- Stoppwörter EntfernenPreview
- Anwendung der Textvorverarbeitung in der PraxisPreview
Ohne weitere Umschweife, lassen Sie uns Stemming und Lemmatisierung erkunden. Diese Techniken können die Effizienz und Effektivität einiger NLP-Aufgaben verbessern, insbesondere bei der Arbeit mit großen Textkorpora und der Behandlung verschiedener Wortformen als dasselbe Wort.
Vorverarbeiteter Text sollte dann in eine numerische Darstellung umgewandelt werden, um in maschinellen Lern- oder Deep-Learning-Modellen für verschiedene Aufgaben wie Vorhersage, Klassifikation oder Clustering verwendet zu werden. Hier werden wir lernen, die grundlegendsten, aber dennoch beliebten Textmodelle zu implementieren, die Textdaten in Zahlen umwandeln.
Es ist an der Zeit, die Kraft der Wort-Einbettungen freizusetzen und fortgeschrittene Techniken zur Erfassung semantischer Beziehungen zwischen Wörtern zu erkunden. Wir werden verschiedene Einbettungsmodelle wie Word2Vec, GloVe und FastText untersuchen, mit einem besonderen Fokus auf das Word2Vec-Modell und dessen Implementierung.
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