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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 Summary 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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Using spaCy's Pre-trained Vectors

Using spaCy's Pre-trained Vectors

Let's learn how we can effectively use the spaCy pre-trained vectors.

We'll cover the following...

We installed a medium-sized English spaCy language model previously so that we can directly use word vectors. Word vectors are part of many spaCy language models. For instance, the en_core_web_md model ships with 300-dimensional vectors for 20,000 words, while the en_core_web_lg model ships with 300-dimensional vectors with a 685,000 word vocabulary. ...