NLP Foundation for Generative AI
About This Course
In this course, we will explore the foundational components of generative AI systems and learn how to solve specific tasks using these technologies. The integration of generative AI into the future of work is becoming increasingly prevalent. While generative AI is a relatively new field, it builds upon the established discipline of Natural Language Processing (NLP), historically considered a key aspect of artificial intelligence. NLP encompasses a variety of tasks, such as textual analysis— including word similarity, entity recognition, and sentence summarization—and applications like question answering and dialogue systems. Mastering these tasks requires a level of intelligence and serves as the groundwork for contemporary generative AI systems.
What You'll Learn
2. Discuss the strengths and weaknesses of the various computational theories in NLP
3. Identify the different components needed to solve an NLP task
4. Illustrate an NLP system pipeline for solving textual problems.
5. Apply the various techniques of textual analysis to achieve a practical NLP system
6. Evaluate the current system and fine-tune the system to fulfill a different task.
7. Collect and process data for training an NLP model
8. Design the training of NLP model from scratch
Entry Requirements
ICT133