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Course Outline
Introduction
- Comparison of Spark NLP, NLTK, and spaCy
- Overview of Spark NLP features and architecture for government
Getting Started
- Setup requirements for government
- Installing Spark NLP in a government environment
- General concepts for effective use in public sector operations
Using Pre-trained Pipelines
- Importing required modules for government applications
- Utilizing default annotators in government workflows
- Loading a pipeline model for government tasks
- Transforming texts for government analysis
Building NLP Pipelines
- Understanding the pipeline API for government use
- Implementing Named Entity Recognition (NER) models for government projects
- Choosing appropriate embeddings for government data
- Utilizing word, sentence, and universal embeddings in government applications
Classification and Inference
- Document classification use cases for government operations
- Sentiment analysis models tailored for government communications
- Training a document classifier for government documents
- Integrating other machine learning frameworks in government projects
- Managing NLP models for government compliance and efficiency
- Optimizing models for low-latency inference in government systems
Troubleshooting
Summary and Next Steps
Requirements
- Proficiency with Apache Spark for government applications
- Experience in Python programming
Audience
- Data Scientists
- Software Developers
14 Hours
Testimonials (3)
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.
Raul Mihail Rat - Accenture Industrial SS
Course - Python, Spark, and Hadoop for Big Data
very interactive...
