Applied Generative AI and Natural Language Processing

Understand Generative AI, Prompt Engineering, Huggingface-Models, LLMs, Vector Databases, RAG, OpenAI, Claude, Llama

Applied Generative AI and Natural Language Processing

Description

Join my comprehensive course on Natural Language Processing (NLP). The course is designed for both beginners and seasoned professionals. This course is your gateway to unlocking the immense potential of NLP and Generative AI in solving real-world challenges. It covers a wide range of different topics and brings you up to speed on implementing NLP solutions.

Course Highlights:

• NLP-Introduction
  • Gain a solid understanding of the fundamental principles that govern Natural Language Processing and its applications.
  • Basics of NLP
  • Word Embeddings
  • Transformers
• Apply Huggingface for Pre-Trained Networks
  • Learn about Huggingface models and how to apply them to your needs
• Model Fine-Tuning
  • Sometimes pre-trained networks are not sufficient, so you need to fine-tune an existing model on your specific task and / or dataset. In this section you will learn how.
• Vector Databases
  • Vector Databases make it simple to query information from texts. You will learn how they work and how to implement vector databases.
  • Tokenization
  • Implement Vector DB with ChromaDB
  • Multimodal Vector DB
• OpenAI API
  • OpenAI with ChatGPT provides a very powerful tool for NLP. You will learn how to make use of it via Python and integrating it in your workflow.
• Prompt Engineering
  • Learn strategies to create efficient prompts
• Advanced Prompt Engineering
  • Few-Shot Prompting
  • Chain-of-Thought
  • Self-Consistency Chain-of-Thought
  • Prompt Chaining
  • Reflection
  • Tree-of-Thought
  • Self-Feedback
  • Self-Critique
• Retrieval-Augmented Generation
  • RAG Theory
  • Implement RAG
• Capstone Project “Chatbot”
  • create a chatbot to “chat” with a PDF document
  • create a web application for the chatbot
• Open Source LLMs
  • learn how to use OpenSource LLMs
  • Meta Llama 2
  • Mistral Mixtral
• Data Augmentation
  • Theory and Approaches of NLP Data Augmentation
  • Implementation of Data Augmentation
• Miscellanious
  • Claude 3
  • Tools and LLM-Function

What you’ll learn

• Introduction to Natural Language Processing (NLP)
• model implementation based on huggingface-models
• working with OpenAI
• Vector Databases
• Multimodal Vector Databases
• Retrieval-Augmented-Generation (RAG)
• Real-World Applications and Case Studies
• implement Zero-Shot Classification, Text Classification, Text Generation
• fine-tune models
• data augmentation
• Prompt Engineering
• Zero-Shot Promping
• Few-Shot Prompting
• Chain-of-Thought (Few-Shot CoT, Zero-Shot CoT)
• Self-Consistency Chain-of-Thought
• Prompt Chaining
• Tree-of-Thought
• Self-Feedback
• Self-Critique
• Claude 3
• Open Source Models, e.g. LLama 2, Mistral

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