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
Shareable Certificate
Earn a shareable certificate to add to your LinkedIn profile.
Develop Your Specialized Knowledge
Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate
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.
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.
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.