We are in the golden age of Generative AI and Sports Betting. This course teaches you how to integrate the two into a powerful prediction agent for gaming outcomes of professional and college sports matches. Starting with a foundation in machine learning models, we transform predictions into a holistic framework using Generative AI frameworks.

1. Sports Betting Deep Dive

Our first week provides an comprehensive exploration of the sports betting landscape, starting with foundational betting concepts such as odds types, bankroll management, and various betting markets. Participants will delve into Exploratory Data Analysis (EDA), learning to manipulate and visualize sports data to uncover patterns and insights. Through hands-on projects, learners will apply statistical techniques to assess betting strategies and evaluate their effectiveness. The session emphasizes understanding the nuances of different sports and how they influence betting outcomes. By the end of the session, students will have a solid grasp of the essential principles that underpin successful sports betting.

2. Machine Learning in Sports

In this session, we focus on the pivotal role of machine learning in transforming sports betting strategies. The session begins with Feature Engineering, teaching participants how to select, create, and optimize features from raw sports data to improve model performance. Attendees will learn techniques for handling various data types, including time-series data, categorical variables, and player statistics. Practical exercises will guide learners through the process of constructing meaningful features that capture the complexities of sports events. This foundation sets the stage for building robust predictive models tailored to the dynamic nature of sports betting.

3. Machine Learning Models

Week 2 begins with the development and application of machine learning models specific to sports betting. The first part, Model Training, covers the selection of appropriate algorithms, such as regression, classification, and ensemble methods, and the implementation of training procedures using real-world sports data. Participants will learn to optimize model parameters and validate model performance through cross-validation techniques. The second part, Model Predictions, focuses on generating accurate forecasts for game outcomes, player performances, and betting odds. Students will gain hands-on experience in deploying models to make informed betting decisions and assess their predictive accuracy.

4. Generative AI Deep Dive

This session introduces ChatGPT for Sports and demonstrates how language models can perform data analysis, interpret betting trends, and generate recommendations based on statistical outputs. Participants will receive detailed instruction on constructing precise prompts to steer AI responses and measure prediction accuracy. The course covers methods for fine-tuning generative models to align outputs with specific sports data and betting scenarios. Additionally, students will build their own GPT-based model using standard libraries and frameworks, systematically experimenting with and refining prompt formulations to assess the impact on model performance and predictive reliability.

5. Sports Betting Systems

Kicking off Week 3, we focus on the design and evaluation of sophisticated sports betting systems. Participants will engage in System Analysis and Trends, learning to dissect existing betting frameworks and identify key performance indicators. The curriculum covers the integration of machine learning models and generative AI to create automated betting strategies that adapt to changing trends and market conditions. Through case studies and practical exercises, learners will assess the effectiveness of different systems, refine their approaches, and implement robust methodologies for sustained betting success. The session emphasizes the importance of data-driven decision-making and continuous system improvement.

6. Generative AI Agents

In this final session, participants will develop and deploy Generative AI Agents tailored for sports betting. The focus is on building a Prediction Bot that leverages machine learning and generative techniques to autonomously generate betting recommendations and forecasts. Students will learn to program agents that can analyze real-time data, adapt to new information, and refine their predictive capabilities over time. The course covers the integration of APIs, automation tools, and user interfaces to create interactive and scalable AI-driven betting solutions. By the end of the session, learners will have the skills to create intelligent agents that enhance their betting strategies and operational efficiency.

Bonus

All students will receive Free Historical Odds Data for Pro and College sports, valued at over $500.