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