Course details

NLP for ML with Python: NLP Using Python & NLTK

NLP for ML with Python: NLP Using Python & NLTK


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description

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.



Expected Duration (hours)
1.0

Lesson Objectives

NLP for ML with Python: NLP Using Python & NLTK

  • 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
  • Course Number:
    it_mlnlpmdj_01_enus

    Expertise Level
    Intermediate