This course provides a comprehensive introduction to Generative AI in data science. You'll explore the foundational concepts of generative AI, including GANs, VAEs, and Transformers, and discover how Microsoft Copilot leverages these models to streamline data science workflows.
Generative AI for Data Science with Copilot
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Generative AI for Data Science with Copilot
This course is part of Microsoft Copilot for Data Science Specialization
Instructor: Microsoft
Included with 
Gain insight into a topic and learn the fundamentals.
Beginner level
Recommended experience
6 hours to complete
3 weeks at 2 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Define and differentiate types of generative AI models
Use Microsoft Copilot to generate code, analyze data, and build generative models
Identify practical use cases for generative AI in data science, such as data augmentation and anomaly detection
Assess the strengths and weaknesses of different generative models and understand their ethical implications
Skills you'll gain
Details to know
Shareable certificate
Add to your LinkedIn profile
Recently updated!
September 2024
Assessments
6 assignments
Taught in English
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This course is part of the Microsoft Copilot for Data Science Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 3 modules in this course
This module provides a comprehensive introduction to generative AI, exploring its definition, key concepts like GANs, VAEs, and Transformers, and highlighting the role of Microsoft Copilot in enhancing data science workflows through code generation, data analysis, and bias mitigation. It also addresses the ethical implications of generative AI and provides practical guidance on integrating Copilot into existing data science practices.
What's included
12 videos5 readings2 assignments
This module dives into practical applications of generative AI in data science, demonstrating how tools like Microsoft Copilot can be used to augment data, uncover hidden patterns, detect anomalies, and simulate scenarios for enhanced decision-making and risk management.
What's included
3 videos5 readings2 assignments
This module dives into the data security and privacy challenges of generative AI, focusing on Microsoft Copilot. You'll learn about potential risks like data breaches and the creation of misleading information, while also exploring strategies and techniques to safeguard data and ensure responsible AI use.
What's included
6 videos3 readings2 assignments1 peer review
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