Generative AI Specialty Course Overview

Generative AI Specialty Course Overview

Generative AI Specialty Course Overview

Our Generative AI Specialty course is designed to provide a comprehensive understanding of generative AI technologies over a 5-day (40-hour) period. Participants will begin with an introduction to Generative AI, including architectures and applications like GANs. The course covers Large Language Models (LLMs), focusing on their architecture, types, and varied applications such as text translation and image captioning. Prompt Engineering includes effective techniques for text, image, and code prompting. Advanced labs explore LangChain for creating LLM systems like retrieval-augmented generation and question-answering systems. The course also focuses on fine-tuning techniques and evaluation using MLflow. Practical labs ensure hands-on experience, empowering attendees to apply learned concepts in real-world scenarios.

Pre-requisite: Fundamentals of Python (Machine learning knowledge is an added advantage).

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  • Live Training (Duration : 40 Hours)
  • Per Participant

♱ Excluding VAT/GST

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Course Prerequisites

Minimum Required Prerequisites for the Generative AI Specialty Course

To successfully undertake the Generative AI Specialty course, we recommend the following minimum prerequisites:


  • Fundamentals of Python programming
  • Basic understanding of Machine Learning concepts (optional but beneficial)

These prerequisites ensure that you have the foundational knowledge necessary to get the most out of the course and participate effectively in the hands-on labs and exercises. If you have any questions about whether your current skill level is sufficient, please don't hesitate to contact us for further guidance.


Target Audience for Generative AI Specialty

Generative AI Specialty Course
This comprehensive 5-day course is designed for professionals and enthusiasts seeking to master Generative AI, with a focus on open-source platforms and hands-on labs. Ideal for those with a Python and machine learning background.


Target Audience and Job Roles:


  • Data Scientists
  • Machine Learning Engineers
  • AI Researchers
  • Software Developers
  • IT Professionals
  • Data Analysts
  • AI Enthusiasts
  • Python Programmers
  • Deep Learning Specialists
  • Technical Leads and Managers
  • UX/UI Designers interested in AI
  • Automation Engineers
  • Research Scholars
  • AI Product Developers


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

1. Brief Introduction: The Generative AI Specialty course by Koenig Solutions equips students with essential skills in generative AI, including understanding architectures, working with large language models, and mastering advanced techniques like prompt engineering and fine-tuning.

2. Learning Objectives and Outcomes:

  • Gain a comprehensive understanding of Generative AI and its architecture.
  • Explore applications of Generative AI using the Transformer Library and GANs.
  • Learn the architecture and types of Large Language Models (LLMs).
  • Get hands-on experience with text and image AI models and services.
  • Master prompt engineering techniques for various AI tasks.
  • Understand and implement Retrieval Augmented Generation (RAG) systems using LangChain.
  • Build advanced LLM systems for QnA using LangChain.
  • Learn fine-tuning techniques and quantization for optimizing model performance.
  • Evaluate open-source models using MLflow and deploy ML models.
  • Conduct practical labs to reinforce learning, including building chatbots, vector stores, and evaluation of models using Hugging Face LLMs.

Target Audience for Generative AI Specialty

Generative AI Specialty Course
This comprehensive 5-day course is designed for professionals and enthusiasts seeking to master Generative AI, with a focus on open-source platforms and hands-on labs. Ideal for those with a Python and machine learning background.


Target Audience and Job Roles:


  • Data Scientists
  • Machine Learning Engineers
  • AI Researchers
  • Software Developers
  • IT Professionals
  • Data Analysts
  • AI Enthusiasts
  • Python Programmers
  • Deep Learning Specialists
  • Technical Leads and Managers
  • UX/UI Designers interested in AI
  • Automation Engineers
  • Research Scholars
  • AI Product Developers


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

1. Brief Introduction: The Generative AI Specialty course by Koenig Solutions equips students with essential skills in generative AI, including understanding architectures, working with large language models, and mastering advanced techniques like prompt engineering and fine-tuning.

2. Learning Objectives and Outcomes:

  • Gain a comprehensive understanding of Generative AI and its architecture.
  • Explore applications of Generative AI using the Transformer Library and GANs.
  • Learn the architecture and types of Large Language Models (LLMs).
  • Get hands-on experience with text and image AI models and services.
  • Master prompt engineering techniques for various AI tasks.
  • Understand and implement Retrieval Augmented Generation (RAG) systems using LangChain.
  • Build advanced LLM systems for QnA using LangChain.
  • Learn fine-tuning techniques and quantization for optimizing model performance.
  • Evaluate open-source models using MLflow and deploy ML models.
  • Conduct practical labs to reinforce learning, including building chatbots, vector stores, and evaluation of models using Hugging Face LLMs.