Gen AI Boxing Analysis: Mike Tyson vs Jake Paul Showdown
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Dive into AI-powered boxing analysis with a focus on the Mike Tyson vs Jake Paul showdown. Learn how AI can predict outcomes and enhance sports analysis.
At a Glance
Master prompt engineering and generative AI in this interactive project, where you’ll create an expert-level bot for boxing with minimal coding. Using models like GPT-4 and IBM Granite 3.0, participants will learn to optimize prompts for detailed boxing matches analysis. This hands-on project deepens understanding of AI capabilities, offering unique insights into AI-driven predictions for boxing matches. Ideal for beginners and AI enthusiasts alike, this experience builds essential skills in model evaluation, AI application, and specialized prompt tuning.
A Look At The Project Ahead
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What You’ll Achieve
- Master the basics of prompt engineering and see how it shapes AI responses.
- Explore various large language models to understand how different prompts yield unique insights.
- Build a boxing expert bot, creating detailed, data-backed analysis for upcoming matches.
- Sharpen your skills in AI application development and model evaluation, laying the groundwork for developing specialized AI tools.
Who Should Complete This Project?
- AI Enthusiasts and Beginners who want hands-on experience with large language models and Generative AI.
- Sports Fans interested in understanding how AI can analyze and predict boxing match outcomes.
- Data Science Students looking to enhance their knowledge of prompt engineering, model evaluation, and application design.
- Professionals in AI and Machine Learning who seek practical, low to no-code projects that focus on prompt optimization and AI-driven insights.
- Anyone intrigued by the potential of AI for real-world applications, particularly in sports analysis and prediction.
What You’ll Need
- Basic familiarity with AI concepts and terminology.
- Experience with web browsers (e.g., Chrome, Firefox, Edge, Safari).
- No extensive coding experience required; this lab is designed with a low to no-code approach, using pre-installed tools within the IBM Skills Network Labs environment.
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