Natural Language Preprocessing Using spaCy

Natural Language Preprocessing Using spaCy, available at $19.99, with 37 lectures, and has 252 subscribers.

You will learn about Introduction to NLP and Spacy Working with Text Data Tokenization and Part-of-Speech Tagging How to use spaCy models Rule-based matching This course is ideal for individuals who are Students interested in NLP It is particularly useful for Students interested in NLP.

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Summary

Title: Natural Language Preprocessing Using spaCy

Price: $19.99

Number of Lectures: 37

Number of Published Lectures: 37

Number of Curriculum Items: 37

Number of Published Curriculum Objects: 37

Original Price: $109.99

Quality Status: approved

Status: Live

What You Will Learn

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

Who Should Attend

Students interested in NLP

Target Audiences

Students interested in NLP

<<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.

Course Curriculum

Chapter 1: Linguistic Features with spacy

Lecture 1: Introduction

Lecture 2: How to do Pos tagging? python code

Lecture 3: What are adjuctives and how to find them using spaCy? python code

Lecture 4: What are Preposition and postposition and how to find them using spaCy?

Lecture 5: What are adverbs and how to find them using spaCy? python code

Lecture 6: What Is an Auxiliary Verb and how to find it using spaCy? python code

Lecture 7: What Are Determiners and how to find them using spaCy? python code

Lecture 8: What is an Interjection and how to find them using spaCy? python code

Lecture 9: What is a Noun and how to find it using spaCy? python code

Lecture 10: What is a Coordinating Conjunction and how to find it the spaCy? python code

Lecture 11: What is a Numeral and how to find it using spaCy ? python code

Lecture 12: What are Particles and how to find them using spaCy? python code

Lecture 13: What are a Pronoun and how to find it using spaCy ? python code

Lecture 14: What are subordinating conjunctions ?

Lecture 15: What are Symbol , Verb , X tags ?

Lecture 16: what is inside Tags attribute ?python code

Lecture 17: What is the dependency parsing ??

Lecture 18: what is morphology ? python code

Lecture 19: rule based lemmatizer vs lookup lemmatizer

Lecture 20: what is lookups class and how to use it ? python code

Lecture 21: load vs blank function python code

Lecture 22: Named Entity Recognition part1 python code

Lecture 23: Named Entity Recognition part2 python code

Lecture 24: tokenizaion

Lecture 25: How to Customize spaCy’s Tokenizer Class for Enhanced Text Processing ???

Lecture 26: Modifying existing rule sets

Lecture 27: Hooking a custom tokenizer into the pipeline

Lecture 28: Training with custom tokenization

Lecture 29: Using pre-tokenized text

Lecture 30: How to merge the tokens ?

Lecture 31: How to split the tokens ?

Lecture 32: Updating Custom Token Attributes

Lecture 33: How to Segment Sentences ?

Lecture 34: Mappings & Exceptions

Lecture 35: Word vectors and semantic similarity

Chapter 2: Rule-based matching

Lecture 1: Token-based matching part1

Lecture 2: Token-based matching part2

Instructors

Riad Almadani
Machine Learning Engineer

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Frequently Asked Questions

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