Introduction to NLP and LLMs: Principles and Practical Applications

Course details

Get up to speed with two of the most talked-about fields in tech today: natural language processing (NLP) and large language models (LLMs). In this course, designed uniquely for nontechnical and nonprogramming professionals, instructor Gwendolyn Stripling provides a comprehensive overview of how computers process and understand human language. Explore real-world applications of NLP and LLMs across various industries, from chatbots and virtual assistants to sentiment analysis and content generation. Along the way, discover some of the potential impacts of these powerful technologies while developing a critical perspective of their benefits, costs, and challenges.

Learning objectives
Define NLP and LLMs, describe the core functionalities of NLP tasks like machine translation and sentiment analysis, and explain the basic principles behind how LLMs work.
Recognize and discuss how NLP and LLMs are currently being used in different industries, such as customer service chatbots, personalized recommendations in ecommerce, automated content creation, and social media analysis.
Understand the potential benefits and challenges of NLP and LLMs, such as their impact on communication, automation, and potential biases.

Concepts

0. Introduction

01 - Principles and practical applications of NLP and LLMs
02 - What you should know

1. Introduction to NLP and LLMs

03 - What is natural language processing (NLP)
04 - Why process text data
05 - Understanding large language models (LLMs)
06 - Key elements of LLMs

2. NLP and LLMs Use Cases

07 - Identifying opportunities for NLP and LLM
08 - Quantifying the potential ROI of NLP and LLM projects
09 - Building a business case for NLP and LLM initiatives

3. Ethical Considerations and Responsible AI

10 - Bias and fairness in NLP and LLMs
11 - Privacy and data security
12 - Responsible AI practices

4. Leading NLP and LLM Projects

13 - Key roles in an NLP LLM team
14 - Team essential skills and competencies
15 - Project management and execution
16 - Metrics for model deployment and evaluation

5. Future Trends

17 - The evolving landscape of NLP and LLMs
18 - Prepare for the future - Build a NLP LLM strategy

Conclusion

19 - Next steps