Introduction to Natural Language Processing in Python

3.6
estrelas
37 classificações
oferecido por
Coursera Project Network
2,402 já se inscreveram
Neste projeto guiado, você irá:

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
IntermediateIntermediário
CloudSem necessidade de download
VideoVídeo em tela dividida
Comment DotsInglês
LaptopApenas em desktop

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.

Habilidades que você desenvolverá

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

Aprender passo a passo

Em um vídeo reproduzido em uma tela dividida com a área de trabalho, seu instrutor o orientará sobre esses passos:

  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

Como funcionam os projetos guiados

Sua área de trabalho é um espaço em nuvem, acessado diretamente do navegador, sem necessidade de nenhum download

Em um vídeo de tela dividida, seu instrutor te orientará passo a passo

Perguntas Frequentes – FAQ

Perguntas Frequentes – FAQ

Mais dúvidas? Visite o Central de Ajuda ao Aprendiz.