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Mastering spaCy
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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.

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Overview

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. Pa...Show More

This course extensively introduces the widely used Python library spaCy for natural language processing (NLP). It covers spaCy b...Show More

WHAT YOU'LL LEARN

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

An understanding of spaCy’s architecture and its various components for NLP tasks

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TAKEAWAY SKILLS

Machine Learning

Natural Language Processing

Content

103 Lessons10 Quizzes2 Assessments2 Challenges

1.

Getting Started

10 Lessons

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

Introduction to the Course

Overview: Getting Started

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

2.

Core Operations with spaCy

11 Lessons

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

3.

Linguistic Features

10 Lessons

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

Overview: Linguistic Features

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

4.

Rule-Based Matchmaking

9 Lessons

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

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

5.

Working with Word Vectors and Semantic Similarity

9 Lessons

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

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

6.

Putting Everything Together: Semantic Parsing with spaCy

10 Lessons

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

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

Assessment: spaCy Features

Assessment

Auto-Tagging System for Content Categorization

Project

7.

Customizing spaCy Models

8 Lessons

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

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

8.

Text Classification with spaCy

10 Lessons

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

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

9.

spaCy and Transformers

11 Lessons

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

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

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.

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

11.

Appendix

4 Lessons

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

Installing spaCy

Installing spaCy's Statistical Models

More on Visualization with displaCy

Saving and loading custom models

12.

Conclusion

1 Lessons

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

Assessment - Machine Learning with spaCy

Assessment

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

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Frequently Asked Questions

Is spaCy a Python library?

SpaCy is a popular open-source Python library for natural language processing (NLP) tasks, such as tokenization, part-of-speech tagging, named entity recognition, and dependency parsing.

How to use spaCy sentencizer with new lines?

To use spaCy’s sentencizer with new lines, you can split a long text into sentences by iterating through the sentences generated by spaCy and inserting new line characters (\n) between them.

What is spaCy used for?

spaCy is used for advanced Natural Language Processing (NLP) tasks, such as tokenization, part-of-speech tagging, named entity recognition, and dependency parsing, enabling developers to extract insights and build applications from text data.

What are the advantages of spaCy?

spaCy offers advantages such as speed, accuracy, and ease of use, making it a popular choice for production-level NLP tasks, along with pre-trained models and a focus on efficiency.

Is spaCy better than BERT?

spaCy and BERT serve different purposes; spaCy is better for general-purpose NLP tasks that require speed and efficiency, while BERT is better for complex language understanding tasks that require deep contextual analysis.

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