Applied Generative AI and Natural Language Processing

Understand Generative AI, Prompt Engineering, Huggingface-Models, LLMs, Vector Databases, RAG, OpenAI, Claude, Llama2


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



Requirements
Python Basic knowledge
Basic knowledge on How Deeplearning works

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
Who this course is for:
Developers who want to apply NLP-models

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