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Building Intelligent AI Agents with Python: From Reactive Bots to Scalable Autonomous Systems: A Comprehensive Guide to Designing, Developing, and Deploying Machine Learning-Powered AI Agents - Softcover

 
9798265752109: Building Intelligent AI Agents with Python: From Reactive Bots to Scalable Autonomous Systems: A Comprehensive Guide to Designing, Developing, and Deploying Machine Learning-Powered AI Agents

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Unlock the power of intelligent automation with AI agents that think, learn, and act in the real world.

In today’s fast-moving AI landscape, building reactive chatbots is no longer enough. The next frontier is autonomous agents systems capable of making decisions, adapting to new environments, and solving complex problems without constant human oversight.

In Building Intelligent AI Agents with Python, Andrew Mori takes you on a comprehensive journey from foundational concepts to advanced implementations. Whether you’re a developer, data scientist, or AI enthusiast, this book provides the knowledge and hands-on techniques you need to design, train, and deploy intelligent agents that scale.

Inside, you’ll discover how to:

  • Master the fundamentals of intelligent agents, reinforcement learning, and decision-making systems.

  • Build practical AI agents using Python and popular frameworks like PyTorch.

  • Implement learning strategies that enable agents to adapt to dynamic, real-world environments.

  • Design scalable autonomous systems that go beyond simple bots to handle complex workflows.

  • Apply AI agents to real-world applications, from automation and robotics to finance, gaming, and beyond.

Packed with practical examples, clear explanations, and code snippets you can use right away, this book bridges the gap between theory and practice. By the end, you’ll not only understand how intelligent agents work you’ll be able to create and deploy them in production systems.

If you’re ready to move from reactive bots to next-generation AI agents, this is your definitive guide.

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Mori, Andrew
Verlag: Independently published, 2025
ISBN 13: 9798265752109
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