15 Topics
10 Days
71 Sessions
Programme Overview
Training Description
Who Should Attend
Session Objectives
- Understand the fundamentals of Natural Language Processing (NLP).
- Master text preprocessing techniques for big data.
- Implement sentiment analysis and opinion mining.
- Utilize topic modeling for text data clustering.
- Develop information extraction systems for structured data.
- Apply machine learning algorithms to NLP tasks.
- Deploy NLP models for real-world applications.
- Troubleshoot and debug NLP pipelines.
- Implement data security and access control in NLP workflows.
- Integrate NLP models with big data platforms.
- Understand how to monitor and maintain NLP models.
- Explore advanced NLP techniques for large datasets.
- Apply real world use cases for NLP in Big Data.
About the Course
This advanced training equips participants with the skills to design, develop, scale, and operationalize natural language processing (NLP) and text analytics solutions across enterprise-scale data pipelines. The course combines NLP techniques with big data engineering to transform large volumes of unstructured and semi-structured text into actionable insights.Participants will explore text preprocessing, feature engineering, information extraction, sentiment analysis, document classification, topic modelling, embeddings, transformer-based NLP, distributed processing, pipeline orchestration, and model deployment. Emphasis is placed on building scalable, reliable, and production-ready NLP workflows capable of processing high-volume enterprise text data.
Curriculum & Topics
15 Topics | 71 Sessions
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Workshop 1.1:
Fundamentals of NLP and text data analysis.
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Workshop 1.2:
Overview of NLP techniques and applications.
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Workshop 1.3:
Setting up a development environment for NLP.
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Workshop 1.4:
Introduction to NLP tools and libraries.
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Workshop 1.5:
Best practices for NLP.
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Workshop 2.1:
Tokenization and text cleaning techniques.
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Workshop 2.2:
Stop word removal and stemming/lemmatization.
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Workshop 2.3:
Handling large text datasets efficiently.
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Workshop 2.4:
Regular expressions and pattern matching.
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Workshop 2.5:
Best practices for text preprocessing.
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Workshop 3.1:
Implementing sentiment analysis algorithms.
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Workshop 3.2:
Utilizing lexicon-based and machine learning approaches.
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Workshop 3.3:
Analyzing customer reviews and social media data.
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Workshop 3.4:
Extracting opinions and emotions from text.
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Workshop 3.5:
Best practices for sentiment analysis.
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Workshop 4.1:
Implementing Latent Dirichlet Allocation (LDA).
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Workshop 4.2:
Utilizing Non-negative Matrix Factorization (NMF).
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Workshop 4.3:
Discovering hidden topics in large text datasets.
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Workshop 4.4:
Visualizing and interpreting topic models.
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Workshop 4.5:
Best practices for topic modeling.
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Workshop 5.1:
Implementing named entity recognition (NER).
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Workshop 5.2:
Utilizing part-of-speech (POS) tagging.
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Workshop 5.3:
Extracting relations and events from text.
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Workshop 5.4:
Building knowledge graphs from text data.
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Workshop 5.5:
Best practices for information extraction.
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Workshop 6.1:
Applying classification algorithms for text categorization.
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Workshop 6.2:
Utilizing sequence models for text generation.
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Workshop 6.3:
Implementing word embeddings (Word2Vec, GloVe).
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Workshop 6.4:
Utilizing transformer models (BERT, GPT).
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Workshop 6.5:
Best practices for machine learning in NLP.
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Workshop 7.1:
Deploying NLP models in production environments.
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Workshop 7.2:
Utilizing containerization and orchestration tools.
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Workshop 7.3:
Implementing API endpoints for NLP services.
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Workshop 7.4:
Monitoring model performance in production.
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Workshop 7.5:
Best practices for model deployment.
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Workshop 8.1:
Debugging NLP models and pipelines.
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Workshop 8.2:
Analyzing model errors and performance issues.
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Workshop 8.3:
Utilizing debugging tools and techniques.
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Workshop 8.4:
Identifying and resolving model biases.
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Workshop 8.5:
Best practices for model troubleshooting.
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Workshop 9.1:
Implementing data security in NLP workflows.
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Workshop 9.2:
Utilizing authentication and authorization.
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Workshop 9.3:
Implementing data encryption and masking.
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Workshop 9.4:
Auditing and compliance in NLP.
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Workshop 9.5:
Best practices for data security.
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Workshop 10.1:
Integrating NLP models with Hadoop and Spark.
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Workshop 10.2:
Utilizing cloud-based NLP services.
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Workshop 10.3:
Implementing real-time NLP pipelines.
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Workshop 10.4:
Best practices for integration.
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Workshop 11.1:
Monitoring model performance and drift.
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Workshop 11.2:
Implementing model retraining and updating.
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Workshop 11.3:
Utilizing model monitoring tools and techniques.
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Workshop 11.4:
Handling model versioning and rollback.
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Workshop 11.5:
Best practices for model maintenance.
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Workshop 12.1:
Implementing question answering systems.
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Workshop 12.2:
Utilizing semantic search and information retrieval.
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Workshop 12.3:
Implementing text summarization and generation.
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Workshop 12.4:
Utilizing conversational AI and chatbots.
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Workshop 12.5:
Advanced techniques for large-scale NLP.
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Workshop 13.1:
Utilizing cloud-based NLP services.
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Workshop 13.2:
Deploying NLP models on AWS, Azure, and GCP.
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Workshop 13.3:
Optimizing cloud resources for NLP.
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Workshop 13.4:
Best practices for cloud-based NLP.
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Workshop 14.1:
Implementing data governance policies in NLP.
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Workshop 14.2:
Utilizing metadata management tools.
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Workshop 14.3:
Implementing data lineage and data dictionary.
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Workshop 14.4:
Best practices for data governance.
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Workshop 15.1:
Emerging trends in NLP research and applications.
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Workshop 15.2:
Utilizing AI and automation in NLP workflows.
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Workshop 15.3:
Implementing explainable NLP models.
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Workshop 15.4:
Best practices for future NLP.
