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Course Outline
Introduction
- Spark NLP versus NLTK versus spaCy
- Overview of Spark NLP features and architecture
Getting Started
- Setup requirements
- Installing Spark NLP
- Core concepts
Using Pre-trained Pipelines
- Importing required modules
- Default annotators
- Loading a pipeline model
- Transforming texts
Building NLP Pipelines
- Understanding the pipeline API
- Implementing NER models
- Selecting embeddings
- Utilizing word, sentence, and universal embeddings
Classification and Inference
- Document classification use cases
- Sentiment analysis models
- Training a document classifier
- Integrating other machine learning frameworks
- Managing NLP models
- Optimizing models for low-latency inference
Troubleshooting
Summary and Next Steps
Requirements
- Familiarity with Apache Spark
- Experience with Python programming
Audience
- Data scientists
- Developers
14 Hours
Testimonials (2)
I liked that it was practical. Loved to apply the theoretical knowledge with practical examples.
Aurelia-Adriana - Allianz Services Romania
Course - Python and Spark for Big Data (PySpark)
The fact that we were able to take with us most of the information/course/presentation/exercises done, so that we can look over them and perhaps redo what we didint understand first time or improve what we already did.
