LLMs for Sentiment Analysis Training in USA

This hands-on course focuses on using Large Language Models (LLMs) to perform sentiment analysis on text data. Participants will learn how LLMs like BERT, RoBERTa, and GPT can be fine-tuned or prompted for sentiment tasks. The course includes working with Hugging Face Transformers, preparing datasets, evaluating models, and deploying sentiment analysis systems in real-world applications.

Welcome to the official LLMs for Sentiment Analysis Training certification program. This comprehensive training is designed to elevate your professional skills and provide you with practical, industry-relevant knowledge in in USA. As a globally recognized corporate training provider operating in 55+ countries, ProgNXT ensures that our curriculum meets the highest standards of excellence.

Whether you are looking to upskill your team or advance your personal career, our expert-led sessions will guide you through the core concepts of this domain. Upon successful completion of the 21 Hrs program, participants will receive a globally accepted certification, demonstrating their proficiency and readiness to tackle complex challenges in the field.

• Proficiency with Python programming
• Familiarity with basic NLP and machine learning concepts
• Understanding of Transformers is helpful but not required
• Access to Google Colab or a local Jupyter environment with GPU support (recommended)

• Understanding how LLMs handle sentiment classification
• Fine-tuning transformer models for binary/multi-class sentiment tasks
• Prompt engineering for zero-shot/few-shot sentiment analysis
• Preprocessing and annotating datasets for LLM input
• Evaluating model accuracy, precision, recall, and F1-score
• Deploying sentiment models for real-time or batch use

By the end of this training, participants will be able to:

• Select and apply appropriate LLMs for sentiment tasks
• Train or prompt models for custom datasets
• Evaluate model performance and refine outputs
• Implement end-to-end sentiment analysis pipelines using LLMs
• Understand deployment and integration best practices