Natural Language Processing with spaCy

Via DataCamp

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Overview

This hands-on course introduces learners to Natural Language Processing (NLP) using spa Cy, one of the leading Python libraries for NLP tasks. Students will learn how to perform tasks such as tokenization, part-of-speech tagging, named entity recognition (NER), and dependency parsing. The course also explores more advanced techniques like text classification and word embeddings. Through practical examples, learners will build NLP pipelines and apply spa Cy to solve real-world language processing challenges. This course is ideal for developers, data scientists,...

Syllabus

Introduction to NLP and spaCy

This chapter will introduce you to NLP, some of its use cases such as named-entity recognition and AI-powered chatbots. Youll learn how to use the powerful spaCy library to perform various natural language processing tasks such as tokenization, sentence segmentation, POS tagging, and named entity recognition.

spaCy Linguistic Annotations and Word Vectors

Learn about linguistic features, word vectors, semantic similarity, analogies, and word vector operations. In this chapter youll discover how to use spaCy to extract word vectors, categorize texts that are relevant to a given topic and find semantically similar terms to given words from a corpus or from a spaCy model vocabulary.

Data Analysis with spaCy

Get familiar with spaCy pipeline components, how to add a pipeline component, and analyze the NLP pipeline. You will also learn about multiple approaches for rule-based information extraction using EntityRuler, Matcher, and PhraseMatcher classes in spaCy and RegEx Python package.

Customizing spaCy Models

Explore multiple real-world use cases where spaCy models may fail and learn how to train them further to improve model performance. Youll be introduced to spaCy training steps and understand how to train an existing spaCy model or from scratch, and evaluate the model at the inference time.