Introduction to Natural Language Processing in Python

3.5
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Coursera Project Network
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在此指導項目中,您將:

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
Intermediate中級
Cloud無需下載
Video分屏視頻
Comment Dots英語(English)
Laptop僅限桌面

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.

您要培養的技能

Language ModelNatural Language ProcessingArtificial Intelligence (AI)Natural Language Toolkit (NLTK)Natural Language Generation

分步進行學習

在與您的工作區一起在分屏中播放的視頻中,您的授課教師將指導您完成每個步驟:

  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

指導項目工作原理

您的工作空間就是瀏覽器中的雲桌面,無需下載

在分屏視頻中,您的授課教師會為您提供分步指導

常見問題

常見問題

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