Deep Learning NLP with spaCy

Introduction:

spaCy is a free, open-source library for advanced Natural Language Processing (NLP) in Python. spaCy is the best way to prepare text for deep learning. It interoperates seamlessly with TensorFlow, PyTorch, Scikit-learn, Gensim and the rest of Python’s awesome AI ecosystem. With spaCy, you can easily construct linguistically sophisticated statistical models for a variety of NLP problems. spaCy is designed to build real products, or gather real insights.

Course Objectives:

  • Non-destructive tokenization
  • Named entity recognition
  • Pre-trained word vectors
  • Deep learning integration
  • Part-of-speech tagging

Duration

7 hours, 1 Day Course

Mode of Delivery

Classroom-based, Instructor-led Training

Course Outlines

  1. Get Started spaCy
    1. What is NLP
    2. Applications of NLP
    3. Online NLP Demo
    4. What is spaCy
    5. Install spaCy and Download Models
  2. Linguistic Features
    1. spaCy nlp Process
    2. Tokenization
    3. Stop Words
    4. Stemming
    5. Lemmatizing
    6. Part of Speech Tagging
    7. Dependency Parsing
    8. Visualizing Dependency and POS
    9. Named Entities Recognition
  3. Processing Pipelines
    1. What is Processing Pipeline
    2. Default Pipeline Components
    3. Disable Pipeline Component
    4. Rename Pipeline Component
    5. Add Custom Pipeline Component
  4. Vectors and Similarity
    1. What is Word Vectorization
    2. Count Vectorization
    3. Word2Vec
    4. Word2Vec using Gensim
    5. spaCy Pre-trained Vectors
    6. Word Similarity
  5. Machine Learning with spaCy
    1. Machine Learning Frameworks
    2. Machine Learning Workflow
    3. Text Classification with SciKit Learn and spaCy