Agentic AI Fundamentals Architectures, Frameworks, and Applications

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Free Download Agentic AI Fundamentals Architectures, Frameworks, and Applications
Released 10/2024
With David Linthicum
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Skill level: Beginner | Genre: eLearning | Language: English | Duration: 1h 15m 18s | Size: 201 MB

This course equips developers with the knowledge and skills to understand, design, and implement autonomous agents in various industries and applications.
Course details
Looking to dive into the world of agentic AI? The course covers the essential concepts, architectures, and frameworks that form the foundation of autonomous agents. Explore decision-making processes, machine learning techniques, and multi-agent systems and gain practical knowledge to develop and deploy agentic AI solutions. Instructor David Linthicum also addresses the ethical and societal implications of agentic AI, as well as security and robustness considerations. Check out this course to get equipped with the skills and understanding needed to leverage agentic AI in various industries and applications.
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Agentic AI Fundamentals: Architectures, Frameworks, and Applications
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 1h 15m | 210 MB
Instructor: David Linthicum​

Looking to dive into the world of agentic AI? The course covers the essential concepts, architectures, and frameworks that form the foundation of autonomous agents. Explore decision-making processes, machine learning techniques, and multi-agent systems and gain practical knowledge to develop and deploy agentic AI solutions.

Instructor David Linthicum also addresses the ethical and societal implications of agentic AI, as well as security and robustness considerations. Check out this course to get equipped with the skills and understanding needed to leverage agentic AI in various industries and applications.

Learning objectives

• Understand the fundamentals of agentic AI, including agent architectures, decision-making frameworks, and environment interaction.
• Learn how to implement machine learning techniques, such as reinforcement learning, to enable agents to learn and adapt.
• Gain knowledge of multi-agent systems, communication protocols, and negotiation strategies.
• Discover practical applications of agentic AI in industries such as robotics, finance, healthcare, and transportation.
• Develop an understanding of the ethical and societal implications of agentic AI and learn how to design secure and robust agents.

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