Natural Language Processing (NLP) with Python and SpaCy | WSQ Courses

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spaCy v3.0 features all new transformer-based pipelines that bring spaCy’s accuracy right up to the current state-of-the-art. You can use any pretrained transformer to train your own pipelines, and even share one transformer between multiple components with multi-task learning.

Training is now fully configurable and extensible, and you can define your own custom models using PyTorch, TensorFlow and other frameworks. The new spaCy projects system lets you describe whole end-to-end workflows in a single file, giving you an easy path from prototype to production, and making it easy to clone and adapt best-practice projects for your own use cases

This two day course will teach you the fundamental of NLP using Pythoin and spaCy.

Course Highlights

  • Lexical Attributes – POS, Parsing, NER
  • Word Similarity Comparison
  • Pre-trained Statistical Language Models
  • Pipelines
  • Text Classification
  • Attention
  • Transformer Based Pipeline


All participants will receive a Certificate of Completion from Tertiary Courses after achieved at least 75% attendance.

Funding and Grant Applications

No funding is available for this course

For WSQ funding, please checkout the details at NICF – Natural Language Processing (NLP) with Python for Beginners


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