GenAI Driven Data Analysis, Chatbot and OCR Mastery

1.1: Overview of AI, Machine Learning, and Data Science

• Introduction to Artificial Intelligence and its evolution.
• The relationship between AI, Machine Learning (ML), and Data Science.
• Overview of real-world applications of AI in various industries.

1.2: Introduction to Large Language Models (LLMs) and Their Applications

• What are Large Language Models (LLMs) and how do they work?
• Key applications of LLMs in data analysis and natural language processing.
• Ethical considerations and challenges of using LLMs.

2.1: Prompt Engineering Fundamentals and Techniques

• Understanding prompt engineering and its importance in AI.
• Techniques for designing effective prompts.
• Best practices and common pitfalls in prompt engineering.

2.2: Exploring GPT Capabilities for Data Analysis

• Introduction to GPT and its architecture.
• Using GPT for data preprocessing and analysis.
• Case studies and examples of GPT in action for data analytics.

3.1: Advanced Prompt Engineering for Data Analysis Tasks and Hands-on Projects

• Designing advanced prompts for complex data analysis tasks.
• Hands-on projects: Applying prompts to real datasets.
• Evaluating and refining prompt effectiveness.

3.2 : Natural Language Processing (NLP) and Introduction to Chatbots

• Fundamentals of Natural Language Processing (NLP).
• The role of NLP in chatbot development.
• Basic principles of building and deploying chatbots.

4.1: Retrieval-Augmented Generation (RAG) Techniques for Chatbots

• Introduction to Retrieval-Augmented Generation (RAG).
• Implementing RAG for more effective chatbots.
• Case studies of RAG in chatbot applications.

4.2: Building and Deploying a Chatbot Using RAG, LLMs, and Streamlit

• Overview of chatbot deployment platforms: Streamlit and others.
• Step-by-step guide to building a chatbot using RAG and LLMs.
• Deployment strategies and considerations for production.