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.
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
Learning Roadmap
1.
Getting Started
Getting Started
Get familiar with spaCy and Python for efficient natural language processing.
2.
Core Operations with spaCy
Core Operations with spaCy
Uncover the core operations of spaCy, including tokenization, lemmatization, and pipeline concepts.
Overview: Core Operations with spaCyOverview of spaCy ConventionsIntroducing TokenizationCustomizing the Tokenizer and Sentence SegmentationUnderstanding LemmatizationspaCy Container ObjectsMore spaCy FeaturesSummary: Core Operations with spaCyQuiz: Core Operations with spaCyExercise: Core Operations with spaCySolution: Core Operations with spaCy
3.
Linguistic Features
Linguistic Features
10 Lessons
10 Lessons
Break apart spaCy's linguistic features: POS tagging, dependency parsing, and named entity recognition.
4.
Rule-Based Matchmaking
Rule-Based Matchmaking
9 Lessons
9 Lessons
Identify entities using spaCy's rule-based matching, improving natural language processing accuracy.
5.
Working with Word Vectors and Semantic Similarity
Working with Word Vectors and Semantic Similarity
9 Lessons
9 Lessons
Deepen your knowledge of word vectors, semantic similarity, and spaCy's pre-trained vector models.
6.
Putting Everything Together: Semantic Parsing with spaCy
Putting Everything Together: Semantic Parsing with spaCy
10 Lessons
10 Lessons
Tackle semantic parsing with spaCy to build effective NLP applications like chatbots.
7.
Customizing spaCy Models
Customizing spaCy Models
8 Lessons
8 Lessons
Build on customizing spaCy models through data preparation, annotation, and bespoke component training.
8.
Text Classification with spaCy
Text Classification with spaCy
10 Lessons
10 Lessons
Learn how to use spaCy and Keras for effective text classification and sentiment analysis.
9.
spaCy and Transformers
spaCy and Transformers
11 Lessons
11 Lessons
Get started with integrating transformers and BERT in spaCy for advanced NLP tools.
10.
Putting Everything Together: Designing a Chatbot with spaCy
Putting Everything Together: Designing a Chatbot with spaCy
10 Lessons
10 Lessons
Master the steps to design and implement a chatbot using spaCy's advanced features.
11.
Appendix
Appendix
4 Lessons
4 Lessons
Enhance your skills in installing spaCy, visualizing with displaCy, and managing custom models.
Certificate of Completion
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Complete more lessons to unlock your certificate
Course Author:
Developed by MAANG Engineers
Meet the Instructor
Packt
Software Engineer at Educative
Every Educative resource is designed by our team of ex-MAANG software engineers and PhD computer science educators — subject matter experts who've shipped production code at scale and taught the theory behind it. No videos, no fluff.
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