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