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Course Outline
Introduction
- Spark NLP vs NLTK vs spaCy
- Overview of Spark NLP features and architecture
Getting Started
- Setup requirements
- Installing Spark NLP
- General 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
- Choosing embeddings
- Using word, sentence, and universal embeddings
Classification and Inference
- Document classification use cases
- Sentiment analysis models
- Training a document classifier
- Using other machine learning frameworks
- Managing NLP models
- Optimizing models for low-latency inference
Troubleshooting
Summary and Next Steps
Requirements
- Familiarity with Apache Spark
- Python programming experience
Audience
- Data scientists
- Developers
14 Hours
Testimonials (2)
pre-training survey and the application of its results.
Krzysztof - Alfa Laval
Course - Python and Spark for Big Data (PySpark)
Machine Translated
Enthusiastically engaging and eagerly explaining side topics.
Marek - Krajowy Rejestr Dlugow Biuro Informacji Gospodarczej S.A.
Course - Apache Spark Fundamentals
Machine Translated
