Course Overview

This comprehensive course on Natural Language Processing (NLP) and Large Language Models (e.g., Chat-GPT, Llama, Claude) is designed to provide students, Ph.D. candidates, and researchers with a deep understanding of the principles, techniques,
and applications of NLP in conjunction with state-of-the-art approaches of text analysis and generation. Participants will explore the fundamental concepts and gain hands-on experience in utilizing NLP tools and techniques to solve real-world problems.
Participants will engage in interactive sessions, case studies, and group activities to explore the ethical challenges and develop strategies for responsible implementation and use of NLP models. Participants will have access to relevant NLP libraries and
frameworks, enabling them to gain practical experience in implementing NLP techniques. Guest lectures for industry experts and company researchers will provide additional insights into real-world applications and challenges.
Discussions will encourage critical thinking and ethical decision-making when utilizing large language models for generative tasks and task-driven interactions.

Learning Outcomes

Upon completion of this course, participants will:
1. Develop a solid understanding of the core concepts, methodologies, and applications of Natural Language Processing.
2. Acquire knowledge about large language models and their significance in NLP tasks.
3. Gain hands-on experience in implementing NLP techniques using popular libraries and frameworks.
4. Develop the ability to analyze and preprocess textual data for NLP applications.
5. Build and evaluate various NLP models, including sentiment analysis, text classification, named entity recognition, and more.
6. Explore the ethical considerations and challenges surrounding the use of large language models.
7. Understand the potential applications of NLP and large language models in various domains such as healthcare, finance, customer service, and social media analytics.
8. Be aware of the Ethical aspects concerning AI, NLP, and Large Language Models.

Course Content

1. Introduction to Natural Language Processing
• Overview of NLP and its applications
• Text preprocessing techniques
• Language modeling concepts
2. Large Language Models
• Introduction to large language models (e.g., GPT, BERT, T5)
• Pretraining and fine-tuning of large language models
• Transfer learning with large language models
• Prompt, Interact and evaluate LLMs answers
• Practical uses of LLMs in career-related tasks
• Keep care of model Hallucinations
• Bias and fairness in NLP models
• Privacy and security concerns
• Responsible use of large language models

3. Text Classification and Sentiment Analysis
• Techniques for text classification
• Sentiment analysis using large language models
• Building sentiment analysis models
4. Named Entity Recognition and Information Extraction
• Introduction to named entity recognition
• Techniques for extracting information from text
• Building named entity recognition models
5. Text Generation and Summarization
• Techniques for text generation
• Abstractive and extractive text summarization
• Building text generation and summarization models

6. Create your Chatbot using LangChain (https://python.langchain.com/)

Instructional Method

The course will be delivered through a combination of lectures, hands-on coding exercises, and practical projects. Participants will have the opportunity to apply the concepts learned to real-world datasets, experiment with different NLP techniques, and
develop their own applications using Large Language Models.

Materials

Participants will need the following materials for the course:
1. Personal computer with internet access
2. Python 3.10 programming environment (e.g., Anaconda, Jupyter Notebook)
3. NLP libraries and frameworks (e.g., NLTK, spaCy, Transformers)
4. Course handouts and lecture slides (provided by the instructor)
5. Additional readings and research papers (provided or recommended by the instructor)
Access to cloud computing resources (e.g. Google Colab) may be required for some advanced exercises, and instructions for setting up the environment will be provided.

Assessment

Hands-on project where participants are required to apply their knowledge of Natural Language Processing and large language models to solve a specific problem or task.
The assessment could include the following components:
1. Dataset preparation: Participants would need to preprocess the provided dataset, including tasks such as tokenization, removing stop words, and handling any noisy or missing data.
2. Model implementation: Participants would utilize a large language model framework (e.g., Hugging Face Transformers) to build a sentiment analysis model. They would fine-tune the pre-trained model on their prepared dataset.
3. Model evaluation: Participants would evaluate the performance of their sentiment analysis model using appropriate metrics such as accuracy, precision, recall, and F1 score. They would interpret the results and discuss any limitations or challenges encountered during the process.
4. Documentation and presentation: Participants would be required to document their approach, explain their decisions, and present their findings. This could be in the form of a report, presentation slides, or a combination of both.

The assessment would assess participants' understanding of NLP concepts, their ability to apply large language models to real-world problems, and their proficiency in evaluating and interpreting model performance. It would also provide an opportunity
for participants to showcase their practical skills and critical thinking in the field of NLP and generative AI.