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Course Outline

Introduction

  • Overview of TextBlob features and architecture.
  • NLP fundamentals.

Getting Started

  • Installing TextBlob.
  • Importing libraries and data.

Building Text Classification Models

  • Loading data and creating classifiers.
  • Evaluating classifiers.
  • Updating classifiers with new data.
  • Using feature extractors.

Performing NLP Tasks using TextBlob

  • Tokenization.
  • WordNet integration.
  • Noun phrase extraction.
  • Part-of-speech tagging.
  • Sentiment analysis.
  • Spelling correction.
  • Translation and language detection.

APIs and Advanced Implementations

  • Sentiment analyzers.
  • Tokenizers.
  • Noun phrase chunkers.
  • POS taggers.
  • Parsers.
  • Blobber.

Troubleshooting

Summary and Next Steps

Requirements

  • Familiarity with NLP concepts.
  • Experience with Python programming.

Target Audience

  • Data scientists.
  • Developers.
 14 Hours

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