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Course Outline
AI Fundamentals
- Examining the scope of AI and its significant influence on industry.
- Building a solid base in machine learning and deep learning concepts.
- Discussing the historical evolution of AI technologies and assessing their present-day capabilities.
Generative AI and Large Language Models (LLM)
- Gaining insight into generative AI mechanics and its diverse use cases.
- Investigating the underlying architecture and functionality of ChatGPT.
- Addressing critical privacy considerations associated with these technologies.
- Mastering the principles of Prompt Engineering.
Core Concepts of Retrieval Augmented Generation (RAG)
- Understanding the RAG methodology and its role in enhancing the performance of generative models.
- Learning how to integrate retrieval processes to improve contextual awareness in AI models.
- Practical workshop: Deploying a basic RAG model for efficient information retrieval.
Leveraging GPT and RAG for Unstructured Data Analysis
- Applying techniques to derive meaningful insights from unstructured data using GPT and RAG frameworks.
- Case study: Processing textual data to generate actionable business intelligence.
Extracting Insights from Structured Data with GPT
- Utilizing GPT models for the analysis of structured datasets.
- Developing strategies to format structured data effectively for GPT processing.
Essential AI Tools and Platforms
- ChatGPT
- Claud
- Copilot
- LlamaIndex
- Langchain
7 Hours
