Natural Language Processing with spaCy & Python - Course for Beginners
Why Stack Overflow recommends it
Stack Overflow users link it when answering spaCy questions or pointing newcomers to a starting course for NLP, especially around EntityRuler, word vectors and model setup.
Recommended by 2 people across 2 different months, 2022 to 2026.
What it is
A three-hour freeCodeCamp beginner course on natural language processing with the Python spaCy library, taught by Dr. William Mattingly. It covers installing spaCy, linguistic annotations, named entity recognition, word vectors, pipelines, EntityRuler, Matcher, custom components, regex, and a finance NER project.
What people said
Recommends Thomas · 2022-02 · Stack Overflow · on “Creating a Neural Machine Translation basics”
On the NLP side of things there's this course: Natural Language Processing with spaCy & Python - Course for Beginners [this video] and this older course: Natural Language Processing (NLP) Tutorial with Python & NLTK [LINK] on Free Code Camp [LINK] , which is generally a good place to start. Their courses provide in depth explanations of concepts and provide good examples. On the translation side of things, the DeepL [LINK] translator is easy to use in multiple languages and offers a free api. It also offers and incredibly easy to use python library [LINK] if that's the language you intend to use (which you should because python is the best out there for NLP). I hope this helps, but dennlinger [LI
Recommends Владислав Король · 2022-09 · Stack Overflow · on “How to remove/add entities in a custom entity ruler in spaCy 3.x”
Just like Polm stated, you can add your own rule-based patterns to overwrite the model. To do this, just create a list of dictionaries that have two pairs: pattern defines what exactly it should overwrite; label defines what label to give to the entity instead. When you add those patterns to your ruler with add_patterns() method, the nlp Doc object gets automatically updated, and you could enumerate through its named entities and see the corrections in play. #Patterns is a list of dictionaries of the token text and the expected label. patterns = [{"label": "NATION", "pattern": "Maya"}, {"label": "NATION", "pattern": "Aztecs"}, {"label": "DATE", "pattern": "BCE"}] #Ruler is the English entity
Cites christianaao · 2026-02 · Stack Overflow · on “Mapping word vector to the most similar/closest word using spaCy”
@Simone I am getting an KeyError [E058] for `en_core_web_sm`, though i seen someone use it fine in this youtube video - [this video]