Wrap-up
1. Wrap-up
Well done! We have reached the final video of this course. Let's briefly review what we have learned.2. Chapter 1 - Introduction to NLP and spaCy
In chapter one, we learned about NLP, some of its use cases, and how to use spaCy pipeline to perform various natural language processing tasks such as tokenization, sentence segmentation, and named entity recognition.3. Chapter 2 - spaCy linguistic annotations and word vectors
In chapter two, we learned about linguistic features, word vectors, analogies, and word vector operations. We also learned how to use spaCy to extract word vectors and find semantically similar terms.4. Chapter 3 - Data analysis with spaCy
In the third chapter, we learned multiple approaches for rule-based information extraction using EntityRuler, Matcher, and PhraseMatcher classes in spaCy and RegEx Python package. Examples of using Matcher and PhraseMatcher are shown.5. Chapter 4 - Customizing spaCy models
And finally, we learned when we may need to train a spaCy model, and how to prepare data, train spaCy models and use trained models at inference.6. Recommended resources
That was a summary of what we have learned about spaCy. DataCamp has many useful resources for you to continue your learnings in AI and NLP. This is a list of a few recommended courses.7. Congratulations!
It has been a pleasure to work with you, thank you for the time that you have dedicated to this course.Create Your Free Account
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