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Easy Learning with Natural Language Preprocessing Using spaCy
Development > Data Science
6h 4m
£24.99 £12.99
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Language: English

Master Natural Language Processing with spaCy: A Practical Python Guide

What you will learn:

  • Introduction to NLP and spaCy
  • Advanced Text Processing Techniques
  • Part-of-Speech Tagging and Named Entity Recognition
  • Dependency Parsing and Semantic Similarity
  • Customizing spaCy's Tokenizer
  • Rule-Based Matching with Regular Expressions
  • Building Practical NLP Applications
  • Working with spaCy Models and Pipelines
  • Leveraging Word Vectors for Text Analysis
  • Text Classification with spaCy

Description

Join our comprehensive course on mastering Natural Language Processing (NLP) using the powerful spaCy library in Python!

This course provides a practical, step-by-step approach to understanding and applying NLP techniques to real-world problems. Whether you're a beginner or an experienced programmer, you'll learn how to extract valuable insights from text data. We cover core NLP concepts and equip you with the skills to build your own NLP applications.

You will delve into essential topics such as tokenization, part-of-speech tagging, named entity recognition, dependency parsing, and text classification. We use clear, concise examples and practical exercises throughout to reinforce your learning. Through hands-on coding, you'll gain confidence in utilizing spaCy's extensive features and capabilities.

This isn't just theory; you'll build practical NLP projects, taking your understanding beyond the fundamentals and into tangible applications. Learn efficient techniques for text preprocessing, and discover how to customize spaCy to meet your specific needs. We also explore advanced topics like rule-based matching and semantic similarity, enabling you to tackle complex linguistic challenges.

This course is perfect for:

  • Data scientists looking to add NLP skills to their toolbox.
  • Machine learning engineers seeking practical applications of NLP.
  • Software developers wanting to integrate NLP capabilities into their applications.
  • Anyone interested in unlocking the power of text data.

Enroll now and begin your NLP journey with spaCy!

Curriculum

Linguistic Features with spaCy

This section provides a comprehensive exploration of spaCy's linguistic features. Starting with an introduction to fundamental concepts, we move into practical application, showing you how to perform part-of-speech tagging, identify adjectives, prepositions, adverbs, auxiliary verbs, determiners, interjections, nouns, coordinating conjunctions, numerals, particles, and pronouns. We delve into dependency parsing, morphology, and lemmatization techniques using both rule-based and lookup methods. You'll learn how to effectively use the spaCy `lookup` class and the `load` vs. `blank` functions. The section concludes with Named Entity Recognition (NER), tokenization, and strategies for customizing the spaCy tokenizer, including merging and splitting tokens, sentence segmentation, handling mappings and exceptions, and finally, understanding and utilizing word vectors for semantic similarity calculations. Lectures include detailed Python code examples for each concept.

Rule-based Matching

This section focuses on mastering rule-based matching in spaCy. You'll learn how to perform token-based matching, creating both simple and complex patterns for text analysis. We cover the use of regular expressions for more advanced pattern matching, breaking down the process into three distinct parts to ensure a solid understanding. This section emphasizes practical implementation and will allow you to apply the skills you learn to effectively filter and extract specific information from textual data.