Generative AI Fundamentals Course Overview

Target Audience for Introduction to Generative AI

Introduction
The Introduction to Generative AI course by Koenig Solutions offers hands-on experience in Python, large language models, and RAG systems, designed for tech professionals aiming to enhance their AI skills.

Target Audience

• Data Scientists
• Machine Learning Engineers
• AI Researchers
• Software Developers
• IT Consultants
• AI Enthusiasts
• Data Analysts
• Research Scientists
• Tech Entrepreneurs
• Graduate Students in AI and Machine Learning
• IT Managers overseeing AI projects
• Innovation Officers
• Solution Architects
• Backend Developers interested in AI
• Python Developers
• CTOs and CIOs (Chief Technology/Information Officers)
• University Educators in AI fields

Learning Objectives - What you will Learn in this Introduction to Generative AI?

Introduction to Learning Outcomes and Course Concepts

The "Introduction to Generative AI" course provides a comprehensive understanding of Python programming and generative AI, focusing on large language models (LLMs) for text and image tasks, fine-tuning techniques, and building LLM applications using advanced frameworks.

Learning Objectives and Outcomes

• Refresh Python Programming Skills:

  • Understand and apply basic Python programming concepts.
  • Hands-on experience in creating simple generative AI applications using Python and the Hugging Face transformer library.

• Text-Based Large Language Models (LLMs):

  • Comprehend the architecture and types of LLMs.
  • Apply LLMs for tasks like translation, summarization, and sentence similarity.
  • Introduction to Ollama and its role in consuming text AI LLMs.
  • Perform role-based prompting and consume various LLMs using Ollama.

• Image-Based Large Language Models:

  • Understand different Image AI models and services.
  • Perform tasks such as object detection, image segmentation, image retrieval, image captioning, visual question answering, and zero-shot image classification.

• Fine-Tuning LLMs:

  • Introduction to the concept and techniques of quantization.
  • Optimize model