Applied Generative AI and Natural Language Processing

Overview

TheApplied Generative AI and Natural Language Processing courseis a comprehensive guide designed for developers who want to master NLP techniques and implement generative AI models in real-world applications. The course covers essential topics such as word embeddings, transformers, Huggingface models, vector databases, prompt engineering, and advanced techniques like Retrieval-Augmented Generation (RAG) and Chain-of-Thought prompting. With hands-on projects and practical examples, this course provides a deep dive into the latest advancements in NLP and AI.

Objectives

By the end of this course, leaner will be able to:

• Understand and implement fundamental NLP concepts, including tokenization, word embeddings, and transformers.
• Apply and fine-tune pre-trained models using Huggingface for specific NLP tasks.
• Utilize vector databases and multimodal vector databases for efficient information retrieval.
• Develop advanced prompt engineering techniques, including zero-shot and few-shot prompting, Chain-of-Thought, and Tree-of-Thought.
• Implement Retrieval-Augmented Generation (RAG) and build a chatbot that interacts with documents.

Prerequisites

Basic knowledge of Python programming. An understanding of how deep learning works. A desire to implement and fine-tune NLP models for specific tasks. An interest in exploring advanced prompt engineering and generative AI techniques. Motivation to work on hands-on projects and develop practical NLP applications.

Course Outline