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2024 Introduction to Spacy for Natural Language Processing


https://www.udemy.com/course/introduction-to-for-natural-language-processing/


Kick start your Data Science career with NLP. This course is about Spacy. NLTK is not taught in this course.


What you'll learn


Complete Spacy Lesson


Introduction to NLP


Tokenization in Spacy


NER and Dependency Parsing


Regular Expression


Emoji Detection for Sentiment Analysis


Requirements


Basics of python


Basics of Machine Learning


Have desire to learn


Description


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 done


SpaCy 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 fast


SpaCy 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 learning


spaCy 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.


 


Features


Non-destructive tokenization


Named entity recognition


Support for 59+ languages


46 statistical models for 16 languages


Pretrained word vectors


State-of-the-art speed


Easy deep learning integration


Part-of-speech tagging


Labeled dependency parsing


Syntax-driven sentence segmentation


Built-in visualizers for syntax and NER


Convenient string-to-hash mapping


Export to NumPy data arrays


Efficient binary serialization


Easy model packaging and deployment


Robust, rigorously evaluated accuracy


And so much more.


 


Who this course is for


Data Scientist Beginners


Who wants to expand their career in NLP


 


2024 Introduction to Spacy for Natural Language Processing


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