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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

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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Overview: Designing a Chatbot with spaCy

Overview: Designing a Chatbot with spaCy

Let's look at what we will be learning in this section.

We'll cover the following...

In this chapter, we will use everything we have learned so far to design a chatbot. We will perform entity extraction, intent recognition, and context handling. We will use different ways of syntactic and semantic parsing, entity extraction, and text classification.

First, we'll explore the dataset we'll ...