Introduction to Generative AI
Learn the fundamentals of generative AI, from core concepts to practical applications, with hands-on experience in prompting and deployment.
Learn the fundamentals of generative AI, from core concepts to practical applications, with hands-on experience in prompting and deployment.
This comprehensive beginner-friendly course provides a solid foundation in generative AI technology. Students learn how generative AI works through interactive lessons and practical exercises, mastering effective prompting techniques and understanding model capabilities and limitations. The curriculum covers major generative models, prompt engineering fundamentals, and system building techniques like Retrieval Augmented Generation. Through hands-on labs and real-world examples, participants gain practical experience with both open-source models and cloud APIs, preparing them to navigate and utilize generative AI technologies confidently.
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What you'll learn
Understand generative AI fundamentals and model architectures
Master prompt engineering techniques for effective model interaction
Explore major foundation models and their capabilities
Build robust generative AI applications using modern tools
Deploy AI solutions on cloud platforms
Skills you'll gain
This course includes:
PreRecorded video
Quizzes, Labs, Discussions
Access on Mobile, Tablet, Desktop
Limited Access access
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There are 3 modules in this course
The course explores fundamental concepts of generative AI, including model architectures, training processes, and practical applications. Students learn prompt engineering techniques, system building approaches, and deployment strategies through hands-on experience with leading AI platforms and tools.
Foundations of Generative AI
Module 1
Interacting with Models
Module 2
Building Robust Generative AI Systems
Module 3
Fee Structure
Instructors
30 Courses
Pioneering Tech Leader & AI Educator Shaping the Future of Machine Learning
Noah Gift is a distinguished technology leader and founder of Pragmatic AI Labs with a remarkable 30-year career spanning film, TV, telecom, social networks, startups, and big data, currently serving as an Executive-in-Residence at Duke University. As an AWS ML Hero and Python Software Foundation Fellow, he has authored best-selling books through O'Reilly and Pearson on DevOps, MLOps, data engineering, and cloud computing that are widely adopted by major universities. His expertise in MLOps, data engineering, cloud architecture, and Rust programming has led him to teach thousands of students across prestigious institutions including Duke, Northwestern, UC Berkeley, University of San Francisco, and UC Davis, while also collaborating with Caltech and JPL on automated IT systems. Gift's impact extends beyond academia through his work as a startup CTO, his role in developing scalable distributed systems, and his contributions as a keynote speaker at conferences focused on cloud development and ethical AI use, holding certifications from AWS, Google, and Microsoft, and having published over 100 technical works while conducting workshops for organizations like NASA, PayPal, and PyCon.
20 Courses
A Technology Educator and Former Olympic Athlete Pioneering AI Innovation
Alfredo Deza embodies a unique combination of athletic excellence and technological expertise, transitioning from a distinguished career as Peru's first World Junior Champion in high jump and 2004 Olympian to becoming a leading voice in technology education and development. Currently serving as a Principal Cloud Advocate at Microsoft and Adjunct Assistant Professor at Duke University's Pratt School of Engineering, Deza has built an impressive career spanning nearly two decades in software engineering and education. His academic contributions extend through guest lectures at prestigious institutions including Oxford University, Georgia Tech, and Carnegie Mellon University, where he shares expertise in machine learning, cloud computing, and programming languages. As an accomplished author, he has co-authored several influential books with O'Reilly Media, including "Practical MLOps" and "Python for DevOps," while developing comprehensive courses on Coursera covering topics from large language models to Rust programming. His teaching portfolio at Duke includes graduate-level courses in machine learning operations and Python programming, reflecting his commitment to making complex technical concepts accessible. Deza's expertise encompasses a broad spectrum of technologies, including Azure, MLOps, DevOps, Python, Rust, and Databricks, which he leverages to bridge the gap between academic theory and industry practice. His unique perspective, shaped by his background as an Olympic athlete, influences his approach to teaching and technology, emphasizing the importance of continuous learning and knowledge sharing in the rapidly evolving field of artificial intelligence and cloud computing.
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