ML Data Characteristics Mapped to SEO & ML Tasks (Guide)

This comprehensive Google Sheets reference guide demystifies machine learning for SEO professionals by mapping different data types to practical SEO applications, ML tasks, and the algorithms that power them. Created by Lazarina Stoy as part of an Introduction to ML for SEOs course, this resource breaks down complex ML concepts into digestible, actionable insights tailored specifically for search engine optimization work.
The sheet systematically covers six major data types—numeric, textual, image, time-series, audio, and video—and connects each to real-world SEO scenarios. From predicting traffic patterns to classifying blog content, analyzing user reviews to detecting anomalies in server logs, this guide shows you exactly which machine learning approaches fit your specific SEO challenges. Each row includes concrete examples of data sources (like Google Analytics or Search Console), the ML tasks you’d use (such as regression, clustering, or text classification), and the specific algorithms that can tackle each problem.
Use this for:
‧ Understanding which ML techniques apply to your specific SEO data and challenges
‧ Learning the connection between common SEO tasks and machine learning methodologies
‧ Identifying the right algorithms for everything from content classification to conversion forecasting
‧ Bridging the gap between technical ML concepts and practical SEO implementation
‧ Building a foundational knowledge of ML applications without needing a data science background
This is perfect for SEO professionals looking to integrate machine learning into their workflow, data analysts working on SEO projects, or anyone curious about how AI can enhance search optimization efforts.