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Mastering spaCy

In this spaCy NLP course, you will learn about core tasks like tokenization, NER, and POS tagging and advanced topics such as custom model training and complex NLP pipelines.

5.0

103 Lessons

30h

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LEARNING OBJECTIVES

An understanding of spaCy’s architecture and its various components for NLP tasks
The ability to customize and train your statistical models for NLP tasks
The ability to work with advanced NLP features
An understanding of how to build and optimize NLP pipelines
Hands-on experience using spaCy for real-world NLP applications

Learning Roadmap

103 Lessons10 Quizzes2 Assessments2 Challenges

Getting Started

Get familiar with spaCy and Python for efficient natural language processing.

Introduction to the Course

Overview: Getting Started

NLP and Python

Python String Operations

High-level Overview of the spaCy Library

Visualization with displaCy

Summary: Getting Started

Quiz: Getting Started

Exercise: Getting Started

Solution: Getting Started

Core Operations with spaCy

Uncover the core operations of spaCy, including tokenization, lemmatization, and pipeline concepts.

Overview: Core Operations with spaCy

Overview of spaCy Conventions

Introducing Tokenization

Customizing the Tokenizer and Sentence Segmentation

Understanding Lemmatization

spaCy Container Objects

More spaCy Features

Summary: Core Operations with spaCy

Quiz: Core Operations with spaCy

Exercise: Core Operations with spaCy

Solution: Core Operations with spaCy

Linguistic Features

10 Lessons

Break apart spaCy's linguistic features: POS tagging, dependency parsing, and named entity recognition.

Rule-Based Matchmaking

9 Lessons

Identify entities using spaCy's rule-based matching, improving natural language processing accuracy.

Working with Word Vectors and Semantic Similarity

9 Lessons

Deepen your knowledge of word vectors, semantic similarity, and spaCy's pre-trained vector models.

Putting Everything Together: Semantic Parsing with spaCy

10 Lessons

Tackle semantic parsing with spaCy to build effective NLP applications like chatbots.

Assessment: spaCy Features

Assessment

Auto-Tagging System for Content Categorization

Project

Premium

Customizing spaCy Models

8 Lessons

Build on customizing spaCy models through data preparation, annotation, and bespoke component training.

Text Classification with spaCy

10 Lessons

Learn how to use spaCy and Keras for effective text classification and sentiment analysis.

spaCy and Transformers

11 Lessons

Get started with integrating transformers and BERT in spaCy for advanced NLP tools.

10.

Putting Everything Together: Designing a Chatbot with spaCy

10 Lessons

Master the steps to design and implement a chatbot using spaCy's advanced features.

11.

Appendix

4 Lessons

Enhance your skills in installing spaCy, visualizing with displaCy, and managing custom models.

12.

Conclusion

Optimize spaCy in real-world NLP by mastering its architecture and advanced applications.

Conclusion

Assessment - Machine Learning with spaCy

Assessment

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

Developed by MAANG Engineers

ABOUT THIS COURSE

This course extensively introduces the widely used Python library spaCy for natural language processing (NLP). It covers spaCy basics, such as tokenization and part-of-speech tagging, as well as advanced topics like custom model training and NLP pipeline creation. The course has three parts: Part 1 focuses on spaCy’s fundamentals, architecture, installation, and setup. It teaches common NLP tasks like tokenization, named entity recognition (NER), part-of-speech (POS) tagging, and dependency parsing. Part 2 delves into spaCy’s features, covering syntax and semantics. It explores pattern matching and semantics via word vectors and thoroughly discusses statistical information extraction techniques. Part 3 examines advanced topics, including developing complex NLP models that require expertise, analysis, and practical experience. Multiple experiments with various NLP tasks are conducted, including customizing statistical models to meet specific needs.

ABOUT THE AUTHOR

Packt

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