This training focuses on NLP pipeline design, text preprocessing, tokenization, embeddings, named entity recognition, sentiment analysis, and machine learning workflows using Spark NLP.
Overview
Spark NLP Pipelines Training is a practical, hands-on program designed to equip learners with the skills required to build scalable natural language processing (NLP) solutions using Spark NLP and distributed data processing frameworks. This training focuses on NLP pipeline design, text preprocessing, tokenization, embeddings, named entity recognition, sentiment analysis, and machine learning workflows using Spark NLP. Participants will gain real-world experience in building production-ready NLP pipelines for large-scale text data processing in enterprise and AI-driven applications.
Learning Outcomes
Participants will gain strong practical expertise in Spark NLP, enabling them to build scalable natural language processing pipelines for real-world AI and data-driven applications.
Duration & Delivery Mode
17 hours
Target Audience
• Spark NLP architecture and pipeline framework
• Text preprocessing and normalization techniques
• Tokenization, stemming, and lemmatization workflows
• Named Entity Recognition (NER) implementation
• Sentiment analysis and text classification models
Pre-requisites
• Basic understanding of Python programming
• Familiarity with machine learning and data processing concepts
• Basic knowledge of Apache Spark is helpful
• Understanding of NLP fundamentals is recommended
Skillset Achieved
• Spark NLP architecture and pipeline framework
• Text preprocessing and normalization techniques
• Tokenization, stemming, and lemmatization workflows
• Named Entity Recognition (NER) implementation
• Sentiment analysis and text classification models
Course Outcome
Upon completion of this training, participants will be able to design and implement scalable NLP pipelines using Spark NLP. They will be capable of processing large-scale text data, building machine learning pipelines for NLP tasks, and deploying production-ready AI solutions in distributed environments.
Course Outline
Introduction to Spark NLP and Text Processing
• Overview of NLP and Spark NLP ecosystem
• Spark NLP architecture and pipeline components
• Text preprocessing techniques
• Tokenization and normalization workflows
Feature Engineering for NLP
• Stop words removal and stemming techniques
• Lemmatization and text cleaning methods
• Word embeddings overview
• Feature extraction for NLP models
Advanced NLP Pipeline Development
• Named Entity Recognition (NER) implementation
• Sentiment analysis workflows
• Text classification models in Spark NLP
• Pipeline building and optimization
Production NLP Systems and Best Practices
• Scaling NLP pipelines in distributed environments
• Model evaluation and tuning techniques
• Integration with Spark ML workflows
• Best practices for production deployment
Assessment Topics
• Spark NLP architecture and pipeline design
• Text preprocessing and feature engineering
• Named Entity Recognition (NER)
• Sentiment analysis and classification
• Word embeddings and NLP features
• Distributed NLP pipeline deployment
Evaluation
• Hands-on NLP pipeline development exercises
• Text classification and sentiment analysis tasks
• Named Entity Recognition implementation assignments
• Mini project on end-to-end NLP system
Course Materials
Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.
Certification
Participants who successfully complete the training will receive an AcadNXT Certification in Spark NLP Pipelines Training, validating their expertise in natural language processing, Spark NLP frameworks, text analytics, and scalable AI pipeline development.
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What Our Students Say
“The training gave me a strong understanding of Spark NLP pipelines and real-world text processing.”
“Excellent hands-on sessions covering NER and sentiment analysis workflows.”
“The course helped me build scalable NLP systems using Spark effectively.”
“Very structured training with strong focus on production NLP pipelines.”
“This course is perfect for learning distributed NLP system design.”