Introduction to Spacy 3 for Natural Language Processing

Name: Introduction to Spacy 3 for Natural Language Processing
Rating: 3.9 (128 reviews)

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

(128 ratings)

4,936 students

Created by Laxmi Kant

Last updated 7/2022

English

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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

Laxmi Kant

Principal Data Scientist at mBreath and KGPTalkie

4.3 Instructor Rating
1,808 Reviews
40,014 Students
7 Courses

I am a Principal Data Scientist at SleepDoc and a Ph.D. in Data Science from the Indian Institute of Technology (IIT). I had also co-founded a company, mBreath Technologies. I have 8+ years of experience in data science, team management, business development, and customer profiling. I have worked with startups and MNC. I have also taught programming at IIT for few years and then later started a YouTube channel, KGP Talkie with 20K+ subscribers. I am very well connected with industry and academia.