Introduction to Natural Language Processing in Python

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In this Guided Project, you will:

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
IntermediateIntermediate
CloudNo download needed
VideoSplit-screen video
Comment DotsEnglish
LaptopDesktop only

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.

Skills you will develop

Artificial Intelligence (AI)Python ProgrammingUser InterfaceNatural Language Processing

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  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

How Guided Projects work

Your workspace is a cloud desktop right in your browser, no download required

In a split-screen video, your instructor guides you step-by-step

Frequently asked questions

Frequently Asked Questions

  • By purchasing a Guided Project, you'll get everything you need to complete the Guided Project including access to a cloud desktop workspace through your web browser that contains the files and software you need to get started, plus step-by-step video instruction from a subject matter expert.

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  • Yes, everything you need to complete your Guided Project will be available in a cloud desktop that is available in your browser.

  • You'll learn by doing through completing tasks in a split-screen environment directly in your browser. On the left side of the screen, you'll complete the task in your workspace. On the right side of the screen, you'll watch an instructor walk you through the project, step-by-step.

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