FeaturedUdemy
Natural Language Processing with spaCy
4.8(608)8k studentsINTERMEDIATEEnglish
Korshub's take
Industrial-Strength NLP with spaCy
spaCy is a high-performance Python library designed for production-ready Natural Language Processing. This intermediate course provides practical training in extracting structured insights from raw text, custom pipeline construction, and model training using spaCy's optimized architecture.
Who Is This Course For?
Ideal for Python programmers and data scientists who want to implement fast, scalable text processing and entity extraction in production environments.
Key Takeaways
- Core spaCy Operations: Parse text and manipulate core objects, including Doc, Span, and Token structures.
- Rule-Based Pattern Matching: Extract complex phrases and entity rules using Matcher, PhraseMatcher, and EntityRuler.
- Custom Pipelines & Training: Build custom pipeline components, prepare evaluation datasets, and train spaCy models for inference.
About this course
Meet spaCy, an Industry-Standard for NLP
In this course, you will learn how to use spaCy, a fast-growing industry-standard library, to perform various natural language processing tasks such as tokenization, sentence segmentation, parsing, and named entity recognition. spaCy can provide powerful, easy-to-use, and production-ready features across a wide range of natural language processing tasks.Learn the Core Operations of spaCy
You will start by learning the core operations of spaCy and how to use them to parse text and extract information from unstructured data. Then, you will work with spaCy’s classes, such as Doc, Span, and Token, and learn how to use different spaCy components for calculating word vectors and predicting semantic similarity.Train spaCy Models and Learn About Pattern Matching
You will practice writing simple and complex matching patterns to extract given terms and phrases using EntityRuler, Matcher, and PhraseMatcher from unstructured data. You will also learn how to create custom pipeline components and create training/evaluation data. From there, you will dive into training spaCy models and how to use them for inference. Throughout the course, you will work on real-world examples and solidify your understanding of using spaCy in your own NLP projects.Advertisement