Applied Generative AI and Natural Language Processing

Unlock the power of Natural Language Processing (NLP) and Generative AI with this comprehensive course, perfect for both beginners and experienced professionals. Learn to tackle real-world challenges and harness the transformative potential of these technologies. This course provides a practical roadmap for implementing cutting-edge NLP solutions.

Here's a detailed look at what you'll learn:

• NLP Introduction: Build a strong foundation in the core principles and diverse applications of Natural Language Processing.
• NLP Fundamentals: Master essential concepts including Word Embeddings and the architecture of Transformers.
• Hugging Face Ecosystem: Dive into the world of Hugging Face and learn how to leverage pre-trained networks for various NLP tasks. We'll cover practical applications and best practices.
• Model Fine-Tuning: Discover how to adapt existing NLP models to your specific datasets and tasks through effective model fine-tuning techniques. Go beyond pre-trained models to achieve optimal performance.
• Vector Databases: Efficiently query and manage textual information using Vector Databases. Understand their functionality and practical implementation.
• ChromaDB Implementation: Learn to implement Vector Databases using ChromaDB, a popular and user-friendly option.
• Multimodal Vector DB: Explore the exciting possibilities of Multimodal Vector Databases that handle various data types.
• OpenAI API & ChatGPT: Unlock the potential of OpenAI with ChatGPT! Learn to integrate the OpenAI API into your Python workflows for powerful NLP applications.
• Prompt Engineering: Become proficient in Prompt Engineering – the art of crafting effective prompts for optimal results.
  • Advanced Prompt Engineering: Explore advanced techniques for maximizing prompt effectiveness.
  • Few-Shot Prompting: Learn to guide models with minimal examples.
  • Chain-of-Thought: Enable step-by-step reasoning in your models.
  • Self-Consistency: Improve reliability by generating multiple responses and selecting the most consistent one.
  • Prompt Chaining: Break down complex tasks into a series of prompts.
  • Reflection: Enhance model reasoning through self-evaluation.
  • Tree-of-Thought: Explore a branching reasoning approach.
  • Self-Feedback & Self-Critique: Implement mechanisms for model self-improvement.
• Retrieval-Augmented Generation (RAG): Master Retrieval-Augmented Generation (RAG), a powerful technique for grounding LLMs in external knowledge.
• Capstone Project: Chatbot Development: Apply your skills to build a functional chatbot capable of interacting with PDF documents and deploying it as a web application.
• Open Source LLMs: Explore and utilize powerful Open Source LLMs, including:
  • Meta Llama 2
  • Mistral Mixtral
• Data Augmentation: Enhance your NLP models with effective Data Augmentation techniques. Learn both theoretical approaches and practical implementation.
• Miscellaneous: Explore various other important aspects of NLP.
• Claude 3: Learn how to utilize the capabilities of Claude 3.
• Tools and LLM-Function: Discover useful tools and how to leverage LLM-Function for enhanced NLP workflows.