RAG and Generative AI with Python 2024

Mastering Retrieval-Augmented Generation (RAG), Generative AI (Gen AI), Prompt Engineering and OpenAI API with Python


What you'll learn
Gain a solid foundation in information retrieval concepts, including tokenization, preprocessing, indexing, querying, and ranking.
Implement various retrieval models in Python, such as the Vector Space Model, Boolean Retrieval, and Probabilistic Retrieval, using real-world datasets.
Understand how text generation models work, including the principles behind transformers and attention mechanisms.
Acquire hands-on experience in using Python libraries to build, fine-tune, and deploy generative models like GPT for various text generation tasks.
Learn how to effectively combine retrieval and generative models to build robust Retrieval-Augmented Generation (RAG) systems.
Utilize Python for advanced RAG system components, such as tokenization, embedding creation, FAISS indexing, and context distance definition.
Explore the integration of OpenAI’s API in RAG systems to enhance retrieval and generation capabilities, including prompt engineering and embedding strategies.
Develop skills to process and integrate unstructured data formats (Excel, Word, PowerPoint, EPUB, PDF) into RAG systems using Python.
Learn to build multimodal RAG systems that combine text, audio, and image data using Python, leveraging models like CLIP and Whisper.
Master techniques to improve the accuracy, efficiency, and effectiveness of RAG systems, preparing you for real-world applications and advanced AI research.

Requirements
Python Proficiency (For loops, Functions)

Description
Are you struggling to build RAGs?
As the amount of digital content grows exponentially, it becomes increasingly challenging to create AI models that can efficiently sift through vast data to provide accurate and meaningful responses.
Traditional search engines and basic AI models often fall short in delivering the context-aware results needed in today’s fast-paced digital landscape.
RAG and Generative AI with Python
is designed to solve this problem by teaching you how to build powerful Retrieval-Augmented Generation (RAG) systems using Python. This course will guide you through the essentials of combining retrieval techniques with generative models to develop applications that are both highly responsive and contextually accurate.
Throughout this course, you will:
Understand RAG Systems
: Learn how to integrate retrieval and generation to enhance your AI models’ capabilities, making them more effective at understanding and generating relevant content.
Learn Practical Python Applications
: Gain hands-on experience with Python libraries and frameworks, enabling you to implement RAG systems and generative models from scratch.
Explore Generative AI and Prompt Engineering
: Delve into the mechanics of generative models and the art of prompt engineering to refine AI outputs, ensuring they meet specific user needs.
Utilize OpenAI’s API for Advanced Applications
: Discover how to leverage OpenAI’s API to enhance your models, adding a new layer of sophistication to your AI solutions.
Handle Various Data Formats in AI Systems
: Develop skills to manage unstructured data types, including text, images, and audio, and integrate them into multimodal RAG systems for comprehensive AI applications.
Optimize AI Models for Real-World Use
: Learn strategies to fine-tune your AI models for improved efficiency, accuracy, and performance in practical scenarios.
This course is perfect for data scientists, software developers, AI enthusiasts, and anyone with a basic knowledge of Python who wants to build smarter, more efficient AI systems.
If you're ready to overcome the limitations of traditional models and lead the charge in AI innovation, this course is for you.
Take the next step in your AI journey with
RAG and Generative AI with Python
and learn how to create the advanced AI tools that the world needs now. Enroll today and start transforming the way you build AI systems!
Who this course is for:
Data Scientists and Machine Learning Engineers looking to deepen their knowledge of generative AI systems.
AI Researchers and Enthusiasts interested in exploring the latest advancements in (RAG) and generative AI technologies.
oftware Developers and Programmers who want to expand their skill set to include AI and machine learning techniques.
Technical Product Managers and AI Strategists who manage AI projects and need a deeper technical understanding of how RAG systems work and their potential applications.
AI Consultants and Data Analysts aiming to add AI capabilities to their skillset
Entrepreneurs and business leaders in the tech space who want to understand the potential of RAG systems and generative AI to innovate.

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