Development

Natural Language Preprocessing Using spaCy

Course Overview

  • Course Title: Natural Language Preprocessing Using spaCy
  • Instructor: Riad Almadani
  • Target Audience:
    • Aspiring data scientists
    • Machine learning engineers
    • Software developers
    • Analysts and researchers
  • Prerequisites:
    • Python basics
    • Passion for learning

Curriculum Highlights

  • Key Topics Covered:
    • Introduction to NLP and Spacy
    • Working with Text Data
    • Tokenization and Part-of-Speech Tagging
    • How to use spaCy models
    • Rule-based matching
    • Tokenization
    • Part-of-speech tagging
    • Named entity recognition
    • Dependency parsing
    • Text classification
  • Key Skills Learned:
    • Gain a solid understanding of linguistic concepts
    • Explore tokenization
    • Part-of-speech tagging
    • Named entity recognition
    • Dive into dependency parsing
    • Text classification
    • Build practical NLP applications using spaCy

Course Format

  • Duration: 6 hours on-demand video
  • Format: Self-paced online course
  • Resources:
    • Access on mobile and TV
    • Certificate of completion
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