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All LessonsFree Lessons (7)

Getting Started

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

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

Overview: Linguistic Features POS Tagging POS Tagging - Continued Introduction to Dependency Parsing Introducing NER Merging and Splitting Tokens Summary: Linguistic Features Quiz: Linguistic Features Exercise: Linguistic Features Solution: Linguistic Features

Rule-Based Matchmaking

Overview: Rule-Based Matchmaking Token Based Matching Syntax Support and Regex PhraseMatcher and EntityRuler Combining spaCy Models and Matchers Summary: Rule-Based Matchmaking Quiz: Rule-Based Matchmaking Exercise: Rule-Based Matchmaking Solution: Rule-Based Matchmaking

Working with Word Vectors and Semantic Similarity

Overview: Word Vectors and Semantic Similarity

Understanding Word Vectors

Using spaCy's Pre-trained Vectors

The Similarity Method

Advanced Semantic Similarity Methods

Summary: Word Vectors and Semantic Similarity

Quiz: Working with Word Vectors and Semantic Similarity

Exercise: Word Vectors and Semantic Similarity

Solution: Word Vectors and Semantic Similarity

Putting Everything Together: Semantic Parsing with spaCy

Overview: Semantic Parsing with spaCy Extracting Named Entities Using Dependency Trees For Extracting Entities Using Dependency Relations for Intent Recognition Semantic Similarity Methods for Semantic Parsing Putting It All Together Summary: Semantic Parsing with spaCy Quiz: Semantic Parsing with spaCy Exercise: Semantic Parsing with spaCy Solution: Semantic Parsing with spaCy

Course Assessment

Assessment: spaCy Features

Project

Auto-Tagging System for Content Categorization

Customizing spaCy Models

Overview: Customizing spaCy Models

Getting Started with Data Preparation

Updating an Existing Pipeline Component

Training a Pipeline Component From Scratch

Quiz: Customizing spaCy Models

Exercise: Customizing spaCy Models

Solution: Customizing spaCy Models

Text Classification with spaCy

Overview: Text Classification with spaCy Understanding the Basics of Text Classification Training the spaCy Text Classifier Sentiment Analysis with spaCy Text classification with spaCy and Keras Embedding Words Summary: Text Classification with SpaCy Quiz: Text Classification with spaCy Exercise: Text Classification with spaCy Solution: Text Classification with spaCy

spaCy and Transformers

Overview: spaCy and Transformers Transformers and Transfer Learning Understanding BERT Transformers and TensorFlow Using BERT for Text Classification Using Transformer Pipelines Transformers and spaCy Summary: spaCy and Transformers Quiz: spaCy and Transformers Exercise: spaCy and Transformers Solution: spaCy and Transformers

Putting Everything Together: Designing a Chatbot with spaCy

Overview: Designing a Chatbot with spaCy Introduction to Conversational AI Getting to Know the Dataset Entity Extraction Intent Recognition Classifying Text with a Character-level LSTM Differentiating Subjects from Objects Anaphora Resolution Summary: Designing a Chatbot with spaCy Quiz: Designing a Chatbot With spaCy

Appendix

Installing spaCy Installing spaCy's Statistical Models More on Visualization with displaCy Saving and loading custom models

Conclusion

Course Assessment

Assessment - Machine Learning with spaCy

Mastering spaCy/

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Summary: Word Vectors and Semantic Similarity

Summary: Word Vectors and Semantic Similarity

Let's revise what we've learned so far.

We'll cover the following...

Key takeaways

Key takeaways

In this chapter, you worked with word vectors, which are floating-point vectors that represent word semantics: ...