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Natural Language Preprocessing Using spaCy
Rating: 4.3 out of 5(90 ratings)
15,604 students

Natural Language Preprocessing Using spaCy

Discover step-by-step Natural Language Processing (NLP) in Python using spaCy! Explore practical NLP project
Created byRiad Almadani • 70,000+ Students
Last updated 7/2025
English

What you'll learn

  • Introduction to NLP and Spacy
  • Working with Text Data
  • Tokenization and Part-of-Speech Tagging
  • How to use spaCy models
  • Rule-based matching

Course content

2 sections • 42 lectures • 6h 4m total length

  • Introduction14:33
  • How to do Pos tagging? python code7:54
  • What are adjuctives and how to find them using spaCy? python code10:44
  • What are Preposition and postposition and how to find them using spaCy?25:25
  • What are adverbs and how to find them using spaCy? python code5:05
  • What Is an Auxiliary Verb and how to find it using spaCy? python code12:00
  • What Are Determiners and how to find them using spaCy? python code7:56
  • What is an Interjection and how to find them using spaCy? python code2:15
  • What is a Noun and how to find it using spaCy? python code3:56
  • What is a Coordinating Conjunction and how to find it the spaCy? python code5:19
  • What is a Numeral and how to find it using spaCy ? python code2:02
  • What are Particles and how to find them using spaCy? python code1:56
  • What are a Pronoun and how to find it using spaCy ? python code6:10
  • What are subordinating conjunctions ?2:32
  • What are Symbol , Verb , X tags ?0:38
  • what is inside Tags attribute ?python code16:02
  • What is the dependency parsing ??12:52
  • what is morphology ? python code5:09
  • rule based lemmatizer vs lookup lemmatizer14:45
  • what is lookups class and how to use it ? python code12:34
  • load vs blank function python code4:51
  • Named Entity Recognition part1 python code14:15
  • Named Entity Recognition part2 python code10:19
  • tokenizaion9:34
  • How to Customize spaCy’s Tokenizer Class for Enhanced Text Processing ???14:51
  • Modifying existing rule sets7:10
  • Hooking a custom tokenizer into the pipeline12:20
  • Training with custom tokenization7:01
  • Using pre-tokenized text3:41
  • How to merge the tokens ?15:00
  • How to split the tokens ?8:29
  • Updating Custom Token Attributes9:31
  • How to Segment Sentences ?8:29
  • Mappings & Exceptions9:23
  • Word vectors and semantic similarity12:15

Requirements

  • Python basics
  • Passion for learning

Description

                                                              <<WE WILL ADD MANY NEW TOPICS TO THIS COURSE>>

Unlocking Linguistic Insights with spaCy

Welcome to the world of linguistic analysis with our comprehensive Udemy course on using spaCy! If you've ever been curious about the underlying structure of language, fascinated by natural language processing (NLP), or eager to extract valuable information from text, this course is your gateway to the exciting field of computational linguistics.

Linguistic analysis plays a pivotal role in applications ranging from sentiment analysis to chatbots, and spaCy is a leading library that empowers you to explore and manipulate language data with ease. Whether you're a beginner or an experienced developer, our course provides a step-by-step journey through the core concepts, tools, and techniques of spaCy.

In this course, you will:

  • Gain a solid understanding of linguistic concepts.

  • Explore tokenization, part-of-speech tagging, and named entity recognition.

  • Dive into dependency parsing and text classification.

  • Build practical NLP applications using spaCy.

By the end of the course, you'll be equipped with the skills and knowledge to apply spaCy to real-world linguistic challenges. Join us today and start unraveling the secrets hidden within text!

Who Should Take This Course:

  • Aspiring data scientists and machine learning engineers interested in NLP.

  • Software developers keen on integrating NLP capabilities into their applications.

  • Analysts and researchers aiming to leverage NLP for data analysis and insights.

Who this course is for:

  • Students interested in NLP

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