课程
Natural Language Processing with spaCy
中级技能水平
更新时间 2025年7月
PythonMachine Learning4小时15 视频53 道练习4,450 XP8,260成就证明
创建您的免费帐户
继续使用 Google显示更多选项或
继续操作即表示您接受我们的《使用条款》和《隐私政策》，并同意您的数据存储在美国。
深受数千家公司学习者的喜爱
需要团队培训？
企业版试用课程描述
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.先决条件
Supervised Learning with scikit-learnPython Toolbox1
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. You’ll 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.
2
spaCy Linguistic Annotations and Word Vectors
Learn about linguistic features, word vectors, semantic similarity, analogies, and word vector operations. In this chapter you’ll 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.
3
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.
4
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. You’ll 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.
Natural Language Processing with spaCy
课程完成 加入超过19百万学习者，今天就开始Natural Language Processing with spaCy！
创建您的免费帐户
继续使用 Google显示更多选项或
继续操作即表示您接受我们的《使用条款》和《隐私政策》，并同意您的数据存储在美国。
通过 DataCamp for Mobile 提升您的数据技能
随时随地通过我们的移动课程和每日 5 分钟编程挑战提升技能。