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Building NLP Pipelines with spaCy

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Are you looking to learn more about the key features and functionalities built into spaCy? Look no further. It’s time to get up to speed with using the extensive spaCy library to solve complex natural language processing tasks. Join instructor Prateek Sawhney in this advanced skills development course, as he demonstrates business-critical approaches and techniques to solving problems in rule-based AI and machine learning.

Learn the basics of how to leverage the complex and advanced spaCy library to address real-world technical problems. From text processing to data analysis, processing pipelines, and training an artificial neural network, Prateek helps you get up and running quickly with results-driven problem-solving tasks. Along the way, you can test out your new skills in the exercise challenges at the end of each section.


01 - Introduction
  • 01 - Why use spaCy
  • 02 - Prerequisites of the course
  • 03 - How to install spaCy

  • 02 - 1. Text Processing with spaCy
  • 01 - Introduction to spaCy
  • 02 - spaCys statistical models
  • 03 - spaCys containers
  • 04 - Introduction to matching based on rules
  • 05 - Challenge Predicting linguistic annotations
  • 06 - Solution Predicting linguistic annotations

  • 03 - 2. Data Analysis Using spaCy
  • 01 - spaCys data structures
  • 02 - Similarity and word vectors
  • 03 - Integrating spaCys models and rules
  • 04 - Challenge Phrase matching
  • 05 - Solution Phrase matching

  • 04 - 3. Processing Pipelines with spaCy
  • 01 - Processing pipelines
  • 02 - Pipelines custom components
  • 03 - Extension attributes Part 1
  • 04 - Extension attributes Part 2
  • 05 - Performance and scaling
  • 06 - Challenge Processing streams and selective processing
  • 07 - Solution Processing streams and selective processing

  • 05 - 4. Training an Artificial Neural Network
  • 01 - Training and updating models
  • 02 - Training loop
  • 03 - Challenge Building a training loop
  • 04 - Solution Building a training loop
  • 05 - Training loop best practices
  • 06 - Challenge Training multiple labels
  • 07 - Solution Training multiple labels

  • 06 - Conclusion
  • 01 - Wrap-up
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    تاریخ انتشار: ۱۲ تیر ۱۴۰۲
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