SKILL BENCHMARK

Natural Language Processing: Text Mining and Analytics Literacy (Beginner Level)

  • 14m
  • 14 questions
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The Text Mining and Analytics Literacy (Beginner Level) benchmark measures your exposure to natural language processing (NLP) concepts, such as the fundamentals of NLP, text mining, and analytics, as well as the various libraries and frameworks used for NLP. Learners who score high on this benchmark demonstrate that they have beginner-level knowledge of natural language processing.

Topics covered

  • categorize various linguistic features available to help in language processing
  • 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
  • describe deep learning approaches to solve NLP tasks
  • describe heuristic approaches to solve NLP tasks
  • describe machine learning approaches to solve NLP tasks
  • describe syntactic and semantic analysis for NLP
  • illustrate phonemes, morpheme, and lexemes
  • illustrate various fundamental tasks and components that are solved and explored using NLP
  • illustrate various tools in NLP used across different industries
  • provide a basic overview of the Natural Language Toolkit (NLTK) ecosystem
  • provide a basic overview of the spaCy ecosystem
  • recognize what spaCy models are and the various types of spaCy models
  • specify challenges with NLP in real world problem solving