AI Essentials Series - How do AI Models Learn?

Welcome to Episode 9 of the AI Concepts Podcast, where we delve into the essential components of machine learning: training data, validation data, and testing data. Join host Shea as she breaks down these core elements using relatable analogies, making them easy to understand and visualize.

In this episode, Shea compares building an AI model to marathon training. Just as athletes train rigorously before a race, AI models learn patterns from training data. Validation data acts like a coach, fine-tuning the model and preventing overfitting. Finally, testing data evaluates the model's real-world performance.

Discover the importance of data splitting and the role of k-fold cross-validation in ensuring model generalization. Shea also discusses challenges like data leakage and imbalanced data, providing insights into overcoming these hurdles.

As a special message to women listeners, Shea encourages them to take up space confidently and unapologetically in all areas of life. Tune in for an enlightening and empowering episode on mastering AI concepts.