Natural Language Processing (NLP) with Python spaCy Training Course

Course Code

spacy

Duration

14 hours (usually 2 days including breaks)

Requirements

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

Audience

  • Developers
  • Data scientists

Overview

This instructor-led, live training (online or onsite) is aimed at developers and data scientists who wish to use spaCy to process very large volumes of text to find patterns and gain insights.

By the end of this training, participants will be able to:

  • Install and configure spaCy.
  • Understand spaCy's approach to Natural Language Processing (NLP).
  • Extract patterns and obtain business insights from large-scale data sources.
  • Integrate the spaCy library with existing web and legacy applications.
  • Deploy spaCy to live production environments to predict human behavior.
  • Use spaCy to pre-process text for Deep Learning

Format of the Course

  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.

Course Customization Options

  • To request a customized training for this course, please contact us to arrange.
  • To learn more about spaCy, please visit: https://spacy.io/

Course Outline

Introduction

  • 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

Troubleshooting

Summary and Conclusion

Testimonials

★★★★★
★★★★★

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