Applications of NL(X) and LLM


Units: 6


With the rapid rise in popularity and adoption of Generative AI, the world of NLP has expanded beyond traditional boundaries, offering unprecedented opportunities in business and industry. This comprehensive course is meticulously designed to provide an in-depth understanding of the vast landscape of Natural Language (NL) processing, understanding, generation, reasoning, planning, and optimization (X-for processing, understanding etc.,). Our course covers the evolution of NLP and delves into the intricacies of deep learning, transformer models, and the applications of large language models.

The course introduces the foundational concepts of NL-X, diving deep into techniques for sentiment analysis, named entity recognition, and question answering. As the course progresses, students will explore the world of chatbots, conversational AI, word embeddings, vector databases, and the revolutionary transformer models like BERT. The latter part of the course delves into the capabilities and applications of large language models, emphasizing their role in tasks like retrieval augmented generation and their use as generative agents for reasoning, planning, and optimization.

Several hands-on examples and exercises are integrated throughout the course, offering students practical experience in applying the learned techniques. Guest lectures and practical LLM applications in healthcare and financial services will demonstrate how enterprises are building and using these modern technologies. The course culminates in a final project presentation, allowing students to showcase their mastery of the content. This course is an invaluable resource for those aspiring to delve deep into the world of NL-X and Generative AI, offering practical insights and knowledge that can be immediately applied in the real world. Join us and equip yourself with the skills to navigate the exciting world of Generative AI using NL-X.


Learning Outcomes

  • Understand the Evolution and Foundations of NLP: Students will trace the historical milestones in NLP, appreciate its significance in the modern world, and grasp foundational concepts including neural networks and their architectures.
  • Master Practical NLP Techniques and Tools: Students will be proficient in text mining techniques, understand popular NLP tools, analyze deep learning architectures (e.g., RNN, LSTM, Transformer) and apply advanced techniques like attention mechanisms to real-world scenarios. 
  • Develop and implement NLP algorithms for specific industries: Students will be able to develop and implement NLP algorithms for specific industries, such as financial services, healthcare, telecommunications, media, technology, retail, and manufacturing, to solve real-world problems.
  • Evaluate the capabilities and limitations of large language models: Students will be able to evaluate the limitations and capabilities of large language models, including their ability to develop emergent abilities and their potential for propagating errors or biases.
  • Fine-tune and adapt large language models for specific generative AI tasks: Students will be able to fine-tune and adapt large language models, such as GPT, LLaMA, PaLM for specific generative AI tasks, including document retrieval, Q&A, language generation, etc.
  • Implement generative agent models for reasoning, planning, and optimization: Students will be able to implement generative agent models for reasoning, planning, and optimization using large language models and agents to solve specific problems in various application areas, such as social analytics, report generation, chatbots, retrieval augmented generation, document processing, topic modeling, and named entity recognition.

Prerequisites Description

This course requires a basic background in data science and/or Artificial Intelligence. Basic level of Python programming is required for completing the assignments.


90803 or 17644 or 10601 or 95828