Natural Language Processing: Linguistic Features Using NLTK & spaCy

Natural Language Processing    |    Intermediate
  • 13 Videos | 1h 10m 44s
  • Includes Assessment
  • Earns a Badge
Without fundamental building blocks and industry-accepted tools, it is difficult to achieve state-of-art analysis in NLP. In this course, you will learn about linguistic features such as word corpora, tokenization, stemming, lemmatization, and stop words and understand their value in natural language processing. Begin by exploring NLTK and spaCy, two of the most widely used NLP tools, and understand what they can help you achieve. Learn to recognize the difference between these tools and understand the pros and cons of each. Discover how to implement concepts like part of speech tagging, named entity recognition, dependency parsing, n-grams, spell correction, segmenting sentences, and finding similar sentences. Upon completion of this course, you will be able to build basic NLP applications on any raw language data and explore the NLP features that can help businesses take actionable steps with this data.

WHAT YOU WILL LEARN

  • discover the key concepts covered in this course
    categorize various linguistic features available to help in language processing
    provide a basic overview of the Natural Language Toolkit (NLTK) ecosystem
    provide a basic overview of the spaCy ecosystem
    classify the difference between spaCy and NLTK
    demonstrate how to use NLTK setup, word corpora, tokenization, cleaner, stemming, lemmatization, stop words, rare words, and spell correction in NLTK
    demonstrate the use of parts of speech, n-gram, named entity recognition, dependency parsing, chunking, parsers, and other language support in NLTK
  • recognize what spaCy models are and the various types of spaCy models
    install and import spaCy libraries, and extract basic NLP features such as parts of speech, morphology, and lemmatization
    demonstrate dependency parsing, named entities, and entity linking with spaCy
    work with spaCy to tokenize, merge, and split data
    demonstrate sentence segmentation and sentence similarity with spaCy
    summarize the key concepts covered in this course

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    44s
    UP NEXT
  • Playable
    2. 
    Linguistic Features in Language Processing
    2m 42s
  • Locked
    3. 
    Introduction to Natural Language Toolkit (NLTK)
    3m 48s
    FREE ACCESS
  • Locked
    4. 
    Introduction to spaCy
    3m 43s
    FREE ACCESS
  • Locked
    5. 
    spaCy verses NLTK
    1m 44s
    FREE ACCESS
  • Locked
    6. 
    Using Linguistic Features in NLTK - Part 1
    12m 11s
    FREE ACCESS
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    7. 
    Using Linguistic Features in NLTK - Part 2
    7m 6s
    FREE ACCESS
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    8. 
    Types of spaCy Models
    3m 5s
    FREE ACCESS
  • Locked
    9. 
    Using Linguistic Features in spaCy - Part 1
    10m 53s
    FREE ACCESS
  • Locked
    10. 
    Using Linguistic Features in spaCy - Part 2
    12m 45s
    FREE ACCESS
  • Locked
    11. 
    Using Linguistic Features in spaCy - Part 3
    7m 8s
    FREE ACCESS
  • Locked
    12. 
    Using Linguistic Features in spaCy - Part 4
    4m 11s
    FREE ACCESS
  • Locked
    13. 
    Course Summary
    44s
    FREE ACCESS

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

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