Technologies and platforms for Artificial Intelligence
This course is part of Artificial Intelligence: an Overview.
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 Artificial Intelligence: an Overview Specialization 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.
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What you'll learn
Understand AI implementation across cloud, edge, and IoT platforms
Explore machine learning and deep learning technologies for different environments
Learn about hardware accelerators and their role in AI processing
Grasp the challenges and opportunities in modern AI deployments
Master concepts of ML-as-a-service and edge AI solutions
Analyze real-world AI implementation case studies
Skills you'll gain
This course includes:
1.4 Hours PreRecorded video
3 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This comprehensive course explores the technological landscape of AI implementation across different computing paradigms. Students learn about hardware technologies for machine and deep learning, from IoT systems to large-scale data centers. The curriculum covers cloud-based AI services, edge computing solutions, and the evolution of hardware accelerators. Special emphasis is placed on understanding the practical applications and challenges of deploying AI across different platforms.
IT and AI
Module 1 · 49 Minutes to complete
AI on the Cloud
Module 2 · 51 Minutes to complete
Embedded and Edge AI
Module 3 · 52 Minutes to complete
Challenges and opportunities
Module 4 · 21 Minutes to complete
Fee Structure
Individual course purchase is not available - to enroll in this course with a certificate, you need to purchase the complete Professional Certificate Course. For enrollment and detailed fee structure, visit the following: Artificial Intelligence: an Overview
Instructor
Associate Professor and Expert in Embedded AI
Manuel Roveri earned his Dr. Eng. degree in Computer Science Engineering from Politecnico di Milano in June 2003, followed by an MS in Computer Science from the University of Illinois at Chicago in December 2003, and a Ph.D. in Computer Engineering from Politecnico di Milano in May 2007. He was a Visiting Researcher at Imperial College London in 2011 and currently serves as an Associate Professor in the Department of Electronics and Information at Politecnico di Milano. His research focuses on Embedded and Edge Artificial Intelligence, Tiny Machine Learning, Deep Learning, and Learning in nonstationary or evolving environments. A Senior Member of IEEE, Roveri has chaired and participated in various IEEE committees, holds one patent, and has published approximately 100 papers in international journals and conference proceedings. He has received notable accolades, including the 2018 IEEE Computational Intelligence Magazine "Outstanding Paper Award" and the 2016 IEEE Computational Intelligence Society "Outstanding Transactions on Neural Networks and Learning Systems Paper Award."
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Frequently asked questions
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