Applied Generative AI and Natural Language Processing

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

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 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-IntroductionGain a solid understanding of the fundamental principles that govern Natural Language Processing and its applications.Basics of NLPWord EmbeddingsTransformersApply Huggingface for Pre-Trained NetworksLearn about Huggingface models and how to apply them to your needsModel Fine-TuningSometimes 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 DatabasesVector Databases make it simple to query information from texts. You will learn how they work and how to implement vector databases.TokenizationImplement Vector DB with ChromaDBMultimodal Vector DBOpenAI APIOpenAI 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 EngineeringLearn strategies to create efficient promptsRetrieval-Augmented GenerationRAG TheoryImplement RAGCapstone Project "Chatbot"create a chatbot to "chat" with a PDF documentcreate a web application for the chatbotOpen Source LLMslearn how to use OpenSource LLMsData AugmentationTheory and Approaches of NLP Data AugmentationImplementation of Data Augmentation

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

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