Projects
Assignments
Overview
The Applied Generative AI and Natural Language Processing course is 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
Module 1: Introduction to Natural Language Processing (NLP)Courses2024-08-20T09:34:34+05:30
- Overview of NLP concepts, word embeddings, and transformers.
- Basics of NLP and its applications in real-world scenarios.
Module 2: Huggingface Models and Fine-TuningCourses2024-08-20T09:36:29+05:30
- Introduction to Huggingface models and pre-trained networks.
- Techniques for fine-tuning models for specific NLP tasks and datasets.
Module 3: Vector Databases and Multimodal Vector DBCourses2024-08-20T09:37:08+05:30
- Understanding vector databases and their role in NLP.
- Implementing vector databases with ChromaDB and exploring multimodal vector databases.
Module 4: Advanced Prompt EngineeringCourses2024-08-20T09:38:11+05:30
- Strategies for effective prompt engineering, including few-shot and zero-shot prompting.
- Exploring Chain-of-Thought, Self-Feedback, and Tree-of-Thought techniques.
Module 5: Capstone Project: Chatbot DevelopmentCourses2024-08-20T09:38:49+05:30
- Building a chatbot to interact with PDF documents.
- Creating a web application for the chatbot using OpenAI and other tools.
