Descriptions
Natural language processing with spaCy, In The In this course, you will learn how to use spaCy, a rapidly 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 functionality for a wide range of natural language processing tasks.
You will First, you will learn about spaCy’s core features 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 various spaCy components to compute word vectors and predict semantic similarity.
You will practice Write Simple and complex matching patterns to extract specific terms and phrases from unstructured data using EntityRuler, Matcher and PhraseMatcher. You will also learn how to create custom pipeline components and create training/evaluation data. From there, you will move on to training spatial models and using them for inference. During the course, you will work on real-world examples and solidify your understanding of how to use spaCy in your own NLP projects.
What you will learn
- How to use the powerful spaCy library to perform various natural language processing
- Data Analysis with spaCy
- learn about linguistic features, word vectors
- Adjust SpaCy models
Specifying natural language processing with spaCy
- Publisher : Data Camp
- Teacher: Azadeh Mobasher
- Language: English
- Level: All levels
- Number of courses: 4
- Duration: 4 hours to complete the course
Content of natural language processing with spaCy
Requirements
- Supervised learning with scikit-learn
- Python Data Science
Pictures
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installation Guide
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Subtitles: English
Quality: 720p
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File size
87MB