Building Intelligent Troubleshooting Agents
Learn to create AI-powered troubleshooting agents that can diagnose and resolve issues autonomously using NLP, decision-making algorithms, and ML models.
Learn to create AI-powered troubleshooting agents that can diagnose and resolve issues autonomously using NLP, decision-making algorithms, and ML models.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Microsoft AI & ML Engineering Professional Certificate program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
Instructors:
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What you'll learn
Define, describe, and design the architecture of an intelligent troubleshooting agent
Implement natural language processing techniques for user interaction
Develop decision-making algorithms for problem diagnosis and resolution
Optimize and evaluate the performance of AI-based troubleshooting agents
Apply fine-tuning techniques to enhance LLMs for specific tasks
Design and implement multi-agent systems for complex troubleshooting scenarios
Skills you'll gain
This course includes:
45 Hours PreRecorded video
42 assignments
Access on Mobile, Desktop, Tablet
FullTime access
Shareable certificate
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There are 5 modules in this course
This course focuses on the design and implementation of intelligent troubleshooting agents powered by AI technologies. Students begin with LLM fine-tuning techniques for task-specific adaptation, exploring data preparation, parameter-efficient fine-tuning, and evaluation metrics. The curriculum then covers fundamentals of AI agents, comparing single and multi-agent systems while establishing key requirements for troubleshooting applications. Natural language processing modules teach chatbot interface development, sentiment analysis implementation, and component integration for effective user interactions. Students gain hands-on experience implementing troubleshooting agents in Python, including classification models, decision-making algorithms, and robust error handling. The course concludes with comprehensive testing, optimization techniques, and real-world evaluation methods to ensure agent effectiveness in practical applications.
LLM fine-tuning for task-specific adaptation
Module 1 · 14 Hours to complete
Fundamentals of AI agents
Module 2 · 7 Hours to complete
Natural language processing for troubleshooting
Module 3 · 6 Hours to complete
Implementing the troubleshooting agent
Module 4 · 9 Hours to complete
Testing and optimizing the agent
Module 5 · 6 Hours to complete
Fee Structure
Instructor
Empowering Individuals and Organizations Through Technology
Microsoft's mission is "to empower every person and every organization on the planet to achieve more," reflecting its commitment to leveraging technology for global empowerment. The company aims to drive digital transformation through an integrated cloud approach, creating a robust platform that enhances productivity and accessibility for users worldwide. Its vision statement emphasizes the goal of democratizing artificial intelligence, ensuring that AI technologies are accessible and beneficial for everyone. This focus on empowerment and inclusivity underpins Microsoft's strategies and product development, positioning it as a leader in innovation within the technology sector. The company has consistently pursued excellence by fostering a culture of innovation, diversity, and corporate social responsibility, ultimately aiming to improve quality of life and facilitate progress across various fields, including education, healthcare, and business.
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Frequently asked questions
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