Introduction to Natural Language Processing with SpaCy:
Get started learning and implementing common NLP techniques in your creative applications. Your’ll explore all aspects of SpaCy, including its data model, part-of-speech tagging, its parser, and the core functionality of the SpaCy library, as well as core SpaCy concepts like tokens, sentences, and documents.
Learning Vector Space Models with SpaCy:
Fully understanding word embeddings can be challenging. Through a hands-on working example using a corpus of hotel reviews, you’ll build dense vector representations of text and train them using Gensim. You’ll learn how to use vector space models, translate text to vectors, and complete tasks such information retrieval, classification, and text generation using techniques from geometry, linear algebra, and deep learning.
Dependency Grammar and Tagging with SpaCy:
The notion of syntactic dependency is not obvious, and it can be difficult to understand how dependency propagates through a sentence. This course demonstrates how to build and query dependency trees. You’ll learn how to use dependency grammar and valency grammar and how to represent and parse natural language using the rules of morphology and syntax from natural language.
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