NLP for ML with Python: NLP Using Python & NLTK

Machine Learning    |    Intermediate
  • 13 videos | 1h 1m 58s
  • Includes Assessment
  • Earns a Badge
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This course explores how natural language processing (NLP) is used for machine learning, and examines the benefits and challenges of NLP when creating an application that can essentially understand human language. In its 13 videos, learners will be shown the essential components of NLP, including parsers, corpus, and corpus linguistic, as well as how to implement regular expressions. This course goes on to examine tokenization, a way to separate a piece of text into smaller units, and then illustrates different tokenization use cases with NLTK (Natural Language Toolkit). You will learn to use stop words using libraries and the NLTK. This course demonstrates how to implement regular expressions in Python to build NLP-powered applications. Learners will examine the list of Python NLP libraries along with their essential capabilities, including NLTK, Gensim, CoreNLP, spaCy and PyNLPl. You will learn to set up and configure an NLTK environment to illustrate how to process raw text. Finally, this course demonstrates the use of filtering stopwords in a tokenized sentence using NLTK.

WHAT YOU WILL LEARN

  • discover the key concepts covered in this course
    define NLP, it uses, and the benefits and challenges associated with it
    recall essential NLP terms and the steps involved in natural language processing
    describe the rule-based and probabilistic parsing approaches and the different types of parsers that are used in NLP
    define corpus and corpus linguistic and describe the benefits associated with corpus linguistic
    implement regular expressions in Python
    list prominent Python NLP libraries and their capabilities
  • set up and configure the NLTK environment to illustrate how to process raw texts
    recognize the major components of NLP
    define tokenization and illustrate different tokenization use cases with NLTK
    demonstrate various tokenization use cases with NLTK
    filter stop words in a tokenized sentence using NLTK
    list NLP terminologies, recall Python NLP libraries, and filter stop words in a tokenized sentence using NLTK

IN THIS COURSE

  • 1m 27s
  • 6m 8s
    In this video, you will learn how to define NLP, its uses, and the benefits and challenges associated with it. FREE ACCESS
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    3.  Terminologies and Steps of NLP
    10m 38s
    Upon completion of this video, you will be able to recall essential NLP terms and the steps involved in natural language processing. FREE ACCESS
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    4.  Parsing Approach and Parser Types
    6m 11s
    After completing this video, you will be able to describe the rule-based and probabilistic parsing approaches and the different types of parsers that are used in NLP. FREE ACCESS
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    5.  Corpus and Corpus Linguistic
    6m 51s
    In this video, learn how to define corpus and corpus linguistics and describe the benefits associated with corpus linguistics. FREE ACCESS
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    6.  Regular Expressions in Python
    4m 45s
    During this video, you will learn how to use regular expressions in Python. FREE ACCESS
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    7.  NLP Libraries
    3m 44s
    After completing this video, you will be able to list prominent Python NLP libraries and their capabilities. FREE ACCESS
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    8.  NLTK Setup
    4m 6s
    In this video, you will set up and configure the NLTK environment to show how to process raw texts. FREE ACCESS
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    9.  Components of NLP
    3m 42s
    Upon completion of this video, you will be able to recognize the major components of natural language processing. FREE ACCESS
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    10.  Tokenization
    4m 26s
    In this video, you will learn how to define tokenization and illustrate different tokenization use cases with NLTK. FREE ACCESS
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    11.  Tokenization with NLTK
    3m 56s
    In this video, you will learn how to apply various tokenization use cases with the Natural Language Toolkit. FREE ACCESS
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    12.  Stop Words with NLTK
    3m 17s
    In this video, you will filter stop words from a tokenized sentence using NLTK. FREE ACCESS
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    13.  Exercise: NLP Terminologies and Stopworks
    2m 49s
    Upon completion of this video, you will be able to list NLP terminologies, recall Python NLP libraries, and filter stop words in a tokenized sentence using NLTK. FREE ACCESS

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