Working with Categorial Data in Machine Learning
Description
Based on the “Machine Learning ” crash course from Google for Developers: https://developers.google.com/machine-learning/crash-course
This episode explores how categorical data is transformed into usable features for machine learning models. From understanding one-hot encoding to tackling real-world labeling challenges and applying feature crosses, we break down key techniques to handle non-numeric data effectively. Perfect for anyone aiming to build better models using human-defined categories.
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.
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