Generative AI Fundamentals Course Overview

Generative AI Fundamentals Course Overview

### Introduction to Generative AI Course Overview

Unlock the potential of Generative AI with Koenig Solutions' immersive Introduction to Generative AI course. Over two days (16 hours), dive deep into Python programming, and explore text-based and image-based Large Language Models (LLMs). Learn to apply Role-based prompting, optimize models through Quantization, and build practical applications like Chatbots using LangChain. The course features extensive hands-on labs utilizing open-source platforms and the Koenig Data Center. Elevate your AI skills with practical experiences in Object detection, Image captioning, Translation, and more. Join us to transform the way you perceive and implement AI technologies in real-world scenarios.

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Course Fee 850
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850 (USD)
  • Live Training (Duration : 16 Hours)
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  • Live Training (Duration : 16 Hours)
  • Per Participant
  • Classroom Training fee on request

♱ Excluding VAT/GST

You can request classroom training in any city on any date by Requesting More Information

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Course Prerequisites

Minimum Required Prerequisites for the Introduction to Generative AI Course:


  • Basic understanding of Python programming
  • Familiarity with basic programming concepts such as variables, loops, and functions
  • Interest in AI and Machine Learning concepts

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

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