Course Outline


  • Spark NLP vs NLTK vs spaCy
  • Overview of Spark NLP features and architecture

Getting Started

  • Setup requirements
  • Installing Spark NLP
  • General concepts

Using Pre-trained Pipelines

  • Importing required modules
  • Default annotators
  • Loading a pipeline model
  • Transforming texts

Building NLP Pipelines

  • Understanding the pipeline API
  • Implementing NER models
  • Choosing embeddings
  • Using word, sentence, and universal embeddings

Classification and Inference

  • Document classification use cases
  • Sentiment analysis models
  • Training a document classifier
  • Using other machine learning frameworks
  • Managing NLP models
  • Optimizing models for low-latency inference


Summary and Next Steps


  • Familiarity with Apache Spark
  • Python programming experience


  • Data scientists
  • Developers
 14 Hours

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Testimonials (2)

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