Natural Language Processing: Linguistic Features Using NLTK & spaCy

Natural Language Processing    |    Intermediate
  • 13 videos | 1h 10m 44s
  • Includes Assessment
  • Earns a Badge
Rating 4.7 of 11 users Rating 4.7 of 11 users (11)
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

  • 44s
  • 2m 42s
    In this video, you will learn how to categorize various linguistic features available to help with language processing. FREE ACCESS
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    3.  Introduction to Natural Language Toolkit (NLTK)
    3m 48s
    After completing this video, you will be able to provide a basic overview of the Natural Language Toolkit (NLTK) ecosystem . FREE ACCESS
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    4.  Introduction to spaCy
    3m 43s
    After completing this video, you will be able to provide a basic overview of the spaCy ecosystem . FREE ACCESS
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    5.  spaCy verses NLTK
    1m 44s
    In this video, learn how to classify the difference between spaCy and NLTK. FREE ACCESS
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    6.  Using Linguistic Features in NLTK - Part 1
    12m 11s
    In this video, you will learn how to use NLTK to setup word corpora, tokenization, cleaner, stemming, lemmatization, stop words, rare words, and spell correction. FREE ACCESS
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    7.  Using Linguistic Features in NLTK - Part 2
    7m 6s
    Learn about the use of parts of speech, n-gram, named entity recognition, dependency parsing, chunking, parsers, and other language support in NLTK. FREE ACCESS
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    8.  Types of spaCy Models
    3m 5s
    Upon completion of this video, you will be able to recognize what spaCy models are and the various types of spaCy models. FREE ACCESS
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    9.  Using Linguistic Features in spaCy - Part 1
    10m 53s
    In this video, you will install and import the spaCy libraries, and extract basic NLP features such as parts of speech, morphology, and lemmatization. FREE ACCESS
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    10.  Using Linguistic Features in spaCy - Part 2
    12m 45s
    Learn how to apply dependency parsing, named entities, and entity linking with spaCy. FREE ACCESS
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    11.  Using Linguistic Features in spaCy - Part 3
    7m 8s
    During this video, you will learn how to work with spaCy to tokenize, merge, and split data. FREE ACCESS
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    12.  Using Linguistic Features in spaCy - Part 4
    4m 11s
    Learn how to apply sentence segmentation and sentence similarity with spaCy. FREE ACCESS
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    13.  Course Summary
    44s

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

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