UDEMY [100% OFF] 2020 Introduction to Spacy for Natural Language Processing 1

UDEMY [100% OFF] 2020 Introduction to Spacy for Natural Language Processing

UDEMY [100% OFF]. Enroll for free – Introduction to Spacy for Natural Language Processing

Introduction to Spacy for Natural Language Processing
Introduction to Spacy for Natural Language Processing

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

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

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