[Pearson / O'Reilly Media] Modern Automated AI Agents: Building Agentic AI to Perform Complex Tasks by Sinan Ozdemir [2025, ENG + Sub]

Modern Automated AI Agents: Building Agentic AI to Perform Complex Tasks
Год выпуска: January 2025
Производитель: Published by Pearson via O'Reilly Learning
Сайт производителя: https://learning.oreilly.com/course/modern-automated-ai/9780135414965/
Автор: Sinan Ozdemir
Продолжительность: 5h 37m
Тип раздаваемого материала: Видеоурок
Язык: Английский + субтитры
Описание:
Get started with automated AI agents
Overview
Modern Automated AI Agents introduces you to the concept of automated agents. It then helps you build a solid understanding of how to design, build, and optimize AI agents to tackle real-world challenges.
About the Instructor
Sinan Ozdemir is the founder and CTO of LoopGenius, where he uses state-of-the-art AI to help people create and run their businesses. Sinan is a former lecturer of Data Science at Johns Hopkins University and the author of multiple textbooks and videos on data science and machine learning. Additionally, he is the founder of the recently acquired Kylie.ai, an enterprise-grade conversational AI platform with RPA capabilities. He holds a master's degree in Pure Mathematics from Johns Hopkins University and is based in San Francisco, California.
Learn How To
• Build & use AI agents
• Evaluate AI agent frameworks
• Start to use CrewAI
• Design multi-step workflows with LangGraph
• Use large language models (LLMs)
• Integrate existing and custom tools
• Use thought, action, observation, and response components
• Test and evaluate agents, their responses, backstories, definitions, and rules
• Add planning and reflection to agents to bolster performance
Who Should Take This Course
Developers, data scientists, and engineers who are interested in building intelligent, autonomous AI agents capable of solving complex problems and adapting to dynamic environments
Course Requirements
• Python 3 proficiency with some experience working in interactive Python environments including Notebooks (Jupyter/Google Colab/Kaggle Kernels)
• Comfortable using the Pandas library and either Tensorflow or PyTorch
• Understanding of ML/deep learning fundamentals including train/test splits, loss/cost functions, and gradient descent Файлы примеров: отсутствуют
Формат видео: MP4
Видео: AVC, 1280×720, 16:9, 30.000 fps, 3 000 kb/s (0.017 bit/pixel)
Аудио: AAC, 44.1 KHz, 2 channels, 128 kb/s, CBR