Course Outline


  • Defining "Industrial-Strength Natural Language Processing"

Installing spaCy

spaCy Components

  • Part-of-speech tagger
  • Named entity recognizer
  • Dependency parser

Overview of spaCy Features and Syntax

Understanding spaCy Modeling

  • Statistical modeling and prediction

Using the SpaCy Command Line Interface (CLI)

  • Basic commands

Creating a Simple Application to Predict Behavior 

Training a New Statistical Model

  • Data (for training)
  • Labels (tags, named entities, etc.)

Loading the Model

  • Shuffling and looping 

Saving the Model

Providing Feedback to the Model

  • Error gradient

Updating the Model

  • Updating the entity recognizer
  • Extracting tokens with rule-based matcher

Developing a Generalized Theory for Expected Outcomes

Case Study

  • Distinguishing Product Names from Company Names

Refining the Training Data

  • Selecting representative data
  • Setting the dropout rate

Other Training Styles

  • Passing raw texts
  • Passing dictionaries of annotations

Using spaCy to Pre-process Text for Deep Learning

Integrating spaCy with Legacy Applications

Testing and Debugging the spaCy Model

  • The importance of iteration

Deploying the Model to Production

Monitoring and Adjusting the Model


Summary and Conclusion


  • Python programming experience.
  • A basic understanding of statistics
  • Experience with the command line


  • Developers
  • Data scientists
  14 Hours


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