Introduction to Spacy 3 for Natural Language Processing | Free Udemy Course
Kick start your Data Science career with NLP. This course is about Spacy. NLTK is not taught in this course. | Free Udemy Course
- 4 hours hours of on-demand video
- 1 article
- Full lifetime access
- Access on mobile and TV
- Certificate of completion
- 1 additional resources
Hi There,Please take this course only if you have an introductory knowledge of Machine Learning and Python. This course is all about SpaCy. Spacy is fast and easy to use than NLTK. It is one of the fundamental building blocks of today's modern NLP. SpaCy is an open-source software library for advanced natural language processing, written in the programming languages Python and Cython. The library is published under the MIT license and its main developers are Matthew Honnibal and Ines Montani, the founders of the software company Explosion.Get things doneSpaCy is designed to help you do real work — to build real products or gather real insights. The library respects your time and tries to avoid wasting it. It's easy to install, and its API is simple and productive. We like to think of spaCy as the Ruby on Rails of Natural Language Processing.Blazing fastSpaCy excels at large-scale information extraction tasks. It's written from the ground up in carefully memory-managed Cython. Independent research in 2015 found spaCy to be the fastest in the world. If your application needs to process entire web dumps, spaCy is the library you want to be using.Deep learningspaCy is the best way to prepare the text for deep learning. It interoperates seamlessly with TensorFlow, PyTorch, scikit-learn, Gensim, and the rest of Python's awesome AI ecosystem. With spaCy, you can easily construct linguistically sophisticated statistical models for a variety of NLP problems.FeaturesNon-destructive tokenizationNamed entity recognitionSupport for 59+ languages46 statistical models for 16 languagesPretrained word vectorsState-of-the-art speedEasy deep learning integrationPart-of-speech taggingLabeled dependency parsingSyntax-driven sentence segmentationBuilt-in visualizers for syntax and NERConvenient string-to-hash mappingExport to NumPy data arraysEfficient binary serializationEasy model packaging and deploymentRobust, rigorously evaluated accuracyAnd so much more.Who this course is for:Data Scientist BeginnersWho wants to expand their career in NLP
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