Generative AI in Action: Industry Trends and Skills You Need in 2025

Overview

Join us to explore the dynamic world of Generative AI and gain insights into the emerging trends that are shaping the future. Equip yourself with the knowledge of essential tools and the key skills that will be in demand by 2025.

Stay competitive in a rapidly evolving job market across various industries by understanding how Generative AI can revolutionize the way we work and interact with technology.

This online event, available on YouTube, caters to anyone keen on advancing their career in artificial intelligence and computer science. Whether you're a seasoned professional or just starting, this is an opportunity to enhance your skill set and prepare for the future of work.

Syllabus

• Overview of Generative AI
Introduction to Generative AI Concepts
Historical Development of Generative Models
Key Differences Between Generative AI and Other AI Approaches
• Emerging Trends in Generative AI
Recent Breakthroughs and Innovations
Impact on Industries: Finance, Healthcare, Entertainment, etc.
Overview of AI Ethics and Policy Considerations
• Generative AI Tools and Technologies
Survey of Leading Generative AI Frameworks: GPT, GANs, etc.
Software and Platforms: TensorFlow, PyTorch, etc.
Tools for Model Deployment and Integration
• Skills for the Generative AI Workforce
Essential Programming Skills: Python, R, etc.
Understanding and Applying Machine Learning Algorithms
Data Analysis and Preprocessing for AI Models
Critical Thinking and Problem-Solving in AI Contexts
• Hands-on Practice with Generative AI
Building a Simple Generative Model
Implementing Projects in Different Domains: Art, Music, Text
Best Practices for Testing and Evaluating AI Systems
• Future of Work and Generative AI
Job Market Analysis and Emerging Roles
Interdisciplinary Skills and Continuous Learning
Creating a Personal Development Plan for AI Careers
• Capstone Project
Application of Learned Skills in a Real-World Scenario
Group Collaboration and Presentation
Feedback and Reflection on Learning Outcomes

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Subjects

Computer Science