Description
Natural Language Processing with spaCy, the Natural Language Processing with spaCy course is published by Datacamp Academy. In this course, you will learn how to use SpaCy, a growing 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, production-ready features across a wide range of natural language processing tasks. You will start by learning the main spaCy operations and how to use them to parse text and extract information from raw data. You will then work with spaCy classes such as Doc, Span, and Token and learn how to use various spaCy components to compute word vectors and predict semantic similarity.
You will practice writing simple and complex matching patterns to extract given phrases using EntityRuler, Matcher and PhraseMatcher from raw data. You will also learn how to create custom pipeline components and create training and evaluation data. Then you will learn spaCy models and how to use them for inference. During this course, you will work on real-world examples and consolidate your understanding of using spaCy in NLP projects.
What you will learn
- How to use the powerful spaCy library to perform various natural language processing
- Data analysis with spaCy
- Linguistic features, word vectors
- Customization of spaCy models
Description of Natural Language Processing with spaCy course
- Publisher: Datacamp
- teacher : Azadeh Mobasher
- English language
- Education level: all levels
- Number of courses: 4
- Training duration: 4 hours to complete the course
Chapters of the Natural Language Processing with spaCy course
Course prerequisites
- Supervised learning with scikit-learn
- Python Data Science
Pictures
Sample video
Installation guide
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English subtitle
Quality: 720p
download link
File(s) password: www.downloadly.ir
Size
87 MB