Applied Generative AI and Natural Language Processing
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Applied Generative AI and Natural Language Processing

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With this extensive master's program, explore the revolutionary field of generative artificial intelligence. Get practical experience building AI models that can produce code, text, graphics, and more. Important topics covered in the program include deep learning, neural networks, diffusion models, language models like GPT, and moral AI practices. Learn cutting-edge methods for content generation, automation, and innovation applications using practical projects and technologies.

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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.

Key Features

  • Comprehensive Coverage
  • Ethics and Bias Mitigation
  • Generative AI Integration
  • Industry-Standard Tools

Curriculum

Study Plan

1Module 1: Introduction to Natural Language Processing (NLP)
  • Overview of NLP concepts, word embeddings, and transformers.
  • Basics of NLP and its applications in real-world scenarios.
2Module 2: Huggingface Models and Fine-Tuning
  • Introduction to Huggingface models and pre-trained networks.
  • Techniques for fine-tuning models for specific NLP tasks and datasets.
3Module 3: Vector Databases and Multimodal Vector DB
  • Understanding vector databases and their role in NLP.
  • Implementing vector databases with ChromaDB and exploring multimodal vector databases.
4Module 4: Advanced Prompt Engineering
  • Strategies for effective prompt engineering, including few-shot and zero-shot prompting.
  • Exploring Chain-of-Thought, Self-Feedback, and Tree-of-Thought techniques.
5Module 5: Capstone Project: Chatbot Development
  • Building a chatbot to interact with PDF documents.
  • Creating a web application for the chatbot using OpenAI and other tools.

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