Introduction to Natural Language Processing in Python

31개의 평가
Coursera Project Network
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Learn a variety of methods for preprocessing methods for eliminating noise from text data, and lexicon normalization

Implement tokenization methods from scratch in Python code

Utilize open-source libraries such as NLTK to implement techniques such as Part Of Speech tags, Named Entity Recognition, and TF-IDF in Python code

Clock2 hours
Cloud다운로드 필요 없음
Video분할 화면 동영상
Comment Dots영어
Laptop데스크톱 전용

In this 1-hour long project-based course, you will learn basic principles of Natural Language Processing, or NLP. NLP refers to a group of methods for parsing and extracting meaning from human language. In this course, we'll explore the basics of NLP as well as detail the workflow pipeline for NLP and define the three basic approaches to NLP tasks. You'll get the chance to go hands on with a variety of methods for coding NLP tasks ranging from stemming and chunking, Named Entity Recognition, lemmatization, and other tokenization methods. You'll be introduced to open-source libraries such as NLTK, spaCy, Gensim, Pattern, and TextBlob. By the end of this course, you will feel more acquainted with the basics of the NLP workflow and will be ready to begin experimenting and prepare for production-level NLP application coding. I would encourage learners to experiment with the tools and methods discussed in this course. The learner is highly encouraged to experiment beyond the scope of the course. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

개발할 기술

Artificial Intelligence (AI)Python ProgrammingUser InterfaceNatural Language Processing

단계별 학습

작업 영역이 있는 분할 화면으로 재생되는 동영상에서 강사는 다음을 단계별로 안내합니다.

  1. Become familiar with the NLP workflow

  2. Understand the limitations of specific NLP techniques and how to overcome them by leveraging other techniques

  3. Review a handful of open-source Python libraries that are useful for NLP-related tasks

  4. Tokenize words in a sample text by hand using the Byte Pair Encoding (BPE) method

  5. Utilize multiple noise removal techniques

  6. Utilize several lexicon normalization techniques such as stemming and lemmatization

  7. Make use of object standardization methods, named entity extraction, and Part of Speech Tagging

  8. Learn how to utilize chunking and chinking methods

  9. Utilize methods such as WordNet, Bag of Words, and TF-IDF (Term Frequency — Inverse Document Frequency) to extract meaning from text

안내형 프로젝트 진행 방식

작업 영역은 브라우저에 바로 로드되는 클라우드 데스크톱으로, 다운로드할 필요가 없습니다.

분할 화면 동영상에서 강사가 프로젝트를 단계별로 안내해 줍니다.

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