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Building Agentic AI Systems with Generative AI Models Course Overview

Building Agentic AI Systems with Generative AI Models Course Overview

Discover the transformative world of Building Agentic AI Systems with Generative AI Models in this comprehensive course. Designed for aspiring AI professionals, this program equips you with the skills to create intelligent systems that can interact, learn, and make decisions autonomously. Participants will explore essential topics such as generative models, neural networks, and reinforcement learning, enabling you to develop cutting-edge applications in various domains. By the end of the course, you will be able to implement and optimize agentic AI systems that enhance user experiences and efficiency. Join us to unlock the potential of AI and take a significant step towards mastering this revolutionary technology.

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  • Live Training (Duration : 48 Hours)
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Inclusions in Koenig's Learning Stack may vary as per policies of OEMs

  • Live Training (Duration : 48 Hours)
  • Per Participant
  • Classroom Training fee on request
Koeing Learning Stack

Koenig Learning Stack

Free Pre-requisite Training

Join a free session to assess your readiness for the course. This session will help you understand the course structure and evaluate your current knowledge level to start with confidence.

Assessments (Qubits)

Take assessments to measure your progress clearly. Koenig's Qubits assessments identify your strengths and areas for improvement, helping you focus effectively on your learning goals.

Post Training Reports

Receive comprehensive post-training reports summarizing your performance. These reports offer clear feedback and recommendations to help you confidently take the next steps in your learning journey.

Class Recordings

Get access to class recordings anytime. These recordings let you revisit key concepts and ensure you never miss important details, supporting your learning even after class ends.

Free Lab Extensions

Extend your lab time at no extra cost. With free lab extensions, you get additional practice to sharpen your skills, ensuring thorough understanding and mastery of practical tasks.

Free Revision Classes

Join our free revision classes to reinforce your learning. These classes revisit important topics, clarify doubts, and help solidify your understanding for better training outcomes.

Inclusions in Koenig's Learning Stack may vary as per policies of OEMs

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♱ Excluding VAT/GST

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

Inclusions in Koenig's Learning Stack may vary as per policies of OEMs

Request More Information

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Target Audience for Building Agentic AI Systems with Generative AI Models

Building Agentic AI Systems with Generative AI Models equips professionals with the skills to design intelligent systems capable of autonomous decision-making using advanced AI technologies.


  • AI/ML Engineers
  • Data Scientists
  • Software Developers
  • Product Managers
  • Business Analysts
  • Research Scientists
  • Systems Architects
  • Technology Consultants
  • UX/UI Designers
  • Digital Transformation Leads
  • Automation Engineers
  • C-suite Executives (CTOs, CIOs)
  • Educators and Trainers in AI/Tech
  • Startup Founders focusing on AI
  • Tech Enthusiasts and Hobbyists in AI


Learning Objectives - What you will Learn in this Building Agentic AI Systems with Generative AI Models?

Introduction

The Building Agentic AI Systems with Generative AI Models course empowers students to develop advanced AI systems that demonstrate autonomy and adaptability, focusing on harnessing cutting-edge generative AI technologies for real-world applications.

Learning Objectives and Outcomes

  • Understand the principles of generative AI and its applications in agentic systems.
  • Design and implement autonomous agents using generative AI models.
  • Evaluate the ethical implications of integrating AI into autonomous systems.
  • Develop skills in natural language processing and data analysis for AI modeling.
  • Create scalable AI solutions that adapt to dynamic environments.
  • Learn to utilize frameworks and tools for building agentic AI systems.
  • Understand the role of reinforcement learning in generative models.
  • Gain hands-on experience through practical projects and case studies.
  • Analyze performance metrics for assessing AI system effectiveness.
  • Collaborate with peers to improve AI system design and functionality.

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