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Mohsen Hassan and Ilyass Tabiai

You will learn to:

  • Configure and manage agent teams for complex tasks

  • Integrate both cloud-based and local language models

  • Connect external tools and deploy agent workflows as APIs

  • Build practical projects such as a crypto blog generator

Throughout the course, you will engage in hands-on exercises and real-world projects that reinforce your understanding of each concept. You will also explore best practices for optimizing agent collaboration, troubleshooting common issues, and extending your workflows with additional tools and integrations.

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You will learn to:

  • Configure and manage agent teams for complex tasks

  • Integrate both cloud-based and local language models

  • Connect external tools and deploy agent workflows as APIs

  • Build practical projects such as a crypto blog generator

Throughout the course, you will engage in hands-on exercises and real-world projects that reinforce your understanding of each concept. You will also explore best practices for optimizing agent collaboration, troubleshooting common issues, and extending your workflows with additional tools and integrations.

This course is suitable for professionals, business users, and enthusiasts interested in AI automation, regardless of coding experience. All concepts are taught through step-by-step lectures, ensuring practical understanding and immediate applicability. By the end of the course, you will be proficient in using AutoGen Studio to create, deploy, and prototype no-code AI agent solutions.

Enroll now to unlock the potential of no-code AI development with AutoGen Studio 0.4.2 and transform your workflow automation capabilities.

Enroll now

What's inside

Learning objectives

  • Learn about agentic oriented programming in a no-code fashion
  • Become an expert at autogenstudio (microsoft)
  • Identify and act on issues that require ai to be solved
  • Develop teams of ai agents able to undertake complex multi-step tasks

Syllabus

Introduction
Intro to LLMs
Agents
Teams
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Career center

Learners who complete AutoGen Studio: No Code AI Agents will develop knowledge and skills that may be useful to these careers:

Reading list

We haven't picked any books for this reading list yet.
Provides a gentle introduction to machine learning, focusing on the most important concepts and algorithms. It good choice for readers who are new to the field.
Comprehensive guide to deep learning, covering topics such as neural networks, convolutional neural networks, and recurrent neural networks. It must-read for anyone who wants to learn about deep learning.
Classic introduction to reinforcement learning, covering topics such as Markov decision processes, value functions, and Q-learning. It valuable resource for anyone who wants to learn about reinforcement learning.
Provides a comprehensive overview of pattern recognition and machine learning, covering topics such as Bayesian inference, neural networks, and support vector machines.
Provides a gentle introduction to AI, focusing on the most important concepts and algorithms. It good choice for readers who are new to the field.
Dives deep into the complexities of systems with multiple interacting agents. It covers algorithmic, game-theoretic, and logical foundations, which are crucial for understanding how agents behave and coordinate in complex environments. It valuable reference for those looking to deepen their understanding beyond single-agent systems.
Reinforcement learning key paradigm for developing intelligent agents that can learn to make sequential decisions by interacting with their environment. is the classic text on the subject, providing a comprehensive introduction to the core concepts and algorithms used in training agents. It must-read for anyone focusing on learning agents.
Provides a solid introduction to the field of multiagent systems, covering key concepts, architectures, and applications. It's more accessible than some of the deeper theoretical texts and serves as an excellent starting point for understanding the principles behind multiple interacting intelligent agents.
Delves into the logical foundations for reasoning about the properties and behavior of rational agents, particularly focusing on the Belief-Desire-Intention (BDI) model. It is more theoretical and suited for those who want to understand the formal underpinnings of agent systems.
This textbook presents AI as the study of intelligent computational agents, providing a unified vision of the field's foundations. It covers a wide range of AI topics through the lens of agents, making it highly relevant for understanding the subject broadly. The latest edition includes updates on recent AI advances like deep learning.
Offers a practical approach to designing and implementing single and multi-agent systems, particularly in the context of generative AI. It helps bridge the gap between theoretical concepts and real-world deployment of AI agents. It is highly relevant for understanding contemporary applications.
Focusing on building LLM-powered autonomous agents, this book provides a practical framework for developing agents that can handle real-world tasks. It covers using tools like the OpenAI Assistants API and LangChain, making it very relevant for contemporary agent development.
Provides a comprehensive overview of AI, covering topics such as machine learning, natural language processing, and computer vision. It is also written in a clear and concise style, making it accessible to readers of all levels.

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