Based on the “Machine Learning ” crash course from Google for Developers: https://developers.google.com/machine-learning/crash-course
In this episode, we break down how machine learning models learn effectively by splitting data into training, validation, and test sets. Understand the purpose of each set, why this separation matters, and how it helps reduce overfitting while improving generalization.
Disclaimer: This podcast is generated using an AI avatar voice. At times, you may notice overlapping sentences or background noise. That said, all content is directly based on the official course material to ensure accuracy and alignment with the original learning experience.