Introduction to Natural Language Processing in Python
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
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
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：
Become familiar with the NLP workflow
Understand the limitations of specific NLP techniques and how to overcome them by leveraging other techniques
Review a handful of open-source Python libraries that are useful for NLP-related tasks
Tokenize words in a sample text by hand using the Byte Pair Encoding (BPE) method
Utilize multiple noise removal techniques
Utilize several lexicon normalization techniques such as stemming and lemmatization
Make use of object standardization methods, named entity extraction, and Part of Speech Tagging
Learn how to utilize chunking and chinking methods
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
如果我購買指導項目，會得到什麼？
指導項目可在台式設備和移動設備上學習嗎？
指導項目的講師是誰？
我能在完成指導項目後從中下載作品嗎？
我能夠退款嗎？退款政策是如何規定的？
有助學金嗎？
我能旁聽指導項目並免費觀看視頻部分嗎？
我需要具備多少經驗才能做這個指導項目？
我能直接通過 Web 瀏覽器來完成此指導項目，而不必安裝特殊軟件嗎？
指導項目的學習體驗如何？
還有其他問題嗎？請訪問 學生幫助中心。
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