"This intermediate-level course takes you beyond AI theory into the practical world of Natural Language Processing (NLP) powered by Transformer architectures. You’ll trace the evolution of language models—from traditional statistical methods and recurrent networks to attention-based systems like BERT, GPT, and T5—through engaging demos and real-world case studies.

Across four modules, you’ll gain a deep understanding of how Transformers work, why they outperform previous models, and how to use them for NLP tasks such as classification, summarization, translation, and sentiment analysis. Through guided coding labs and hands-on exercises with Hugging Face tools, you’ll learn how to tokenize data, fine-tune pretrained models, evaluate results, and deploy applications efficiently. Whether you’re a developer, data scientist, or AI enthusiast, this course bridges the gap between concept and implementation—helping you turn complex architectures into tangible, working AI systems. By the end of this course, you will be able to: - Understand and explain how Transformer architectures process and generate human language. - Fine-tune and deploy pretrained models using Hugging Face tools and APIs. - Apply NLP techniques to real-world use cases such as summarization and classification. - Evaluate and interpret model performance using key metrics and visualizations. - Design and deliver an end-to-end NLP project, from training to deployment." Disclaimer: This is an independent educational resource created by Board Infinity for informational and educational purposes only. This course is not affiliated with, endorsed by, sponsored by, or officially associated with any company, organization, or certification body unless explicitly stated. The content provided is based on industry knowledge and best practices but does not constitute official training material for any specific employer or certification program. All company names, trademarks, service marks, and logos referenced are the property of their respective owners and are used solely for educational identification and comparison purposes.