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

This 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. I created it as part of my Introduction to ML for SEOs course, to break complex concepts into digestible, actionable insights for search work.

What’s inside

The sheet covers six data types: numeric, textual, image, time-series, audio and video. For each one you get a short description, then rows with an SEO task example, typical data sources, the common machine learning task and possible algorithms. A few examples of what you’ll find:

• Numeric: traffic metrics from Google Analytics, Search Console or rank tracker data for regression (linear regression, gradient boosting such as XGBoost), and conversion or engagement statistics for clustering (K-Means, Gaussian Mixture Models).
• Textual: classifying blog pages into categories (Naïve Bayes, SVMs, BERT), topic modelling with LDA or K-Means, classifying queries by search intent, and sentiment analysis of user reviews.
• Image: generating alt text with vision transformers, classifying product images with CNNs or ResNet, and object detection with YOLO or Faster R-CNN.
• Time-series: forecasting seasonal search demand with ARIMA, Prophet or LSTMs, and spotting anomalies in analytics or server logs with Isolation Forests.
• Audio: speech-to-text plus entity extraction for call transcripts, and classifying podcast topics.
• Video: categorising YouTube or TikTok videos by type or topic, generating summaries and captions, and detecting objects or actions in frames.

How to use it

1. Start with the data you already have, and find its row group.
2. Read across to the SEO task example closest to your problem.
3. Note the machine learning task, such as regression, text classification or anomaly detection. That’s the vocabulary you’ll search with.
4. Use the algorithm column as a starting point when looking for existing notebooks, APIs or tools, then check feasibility with the When to use ML in SEO decision checklist.

Use this for

• Understanding which ML techniques apply to your SEO data and challenges
• Learning the connection between common SEO tasks and machine learning methods
• Identifying the right algorithms for everything from content classification to traffic forecasting
• Bridging the gap between technical ML concepts and practical SEO work
• Building foundational knowledge of ML applications without a data science background

Tips and limits

Treat this as a map. It’s deliberately beginner-level, so the algorithm lists are starting points to search with, far from a full catalogue, and you’ll often reach for a pre-trained API or a large language model before training anything yourself. Audio and video rows usually begin by converting to text, so most SEO projects end up as text problems.

This is perfect for SEO professionals looking to bring machine learning into their workflow, data analysts working on SEO projects, or anyone curious about how AI can support search work.

Learn it on MLforSEO

Read the characteristics of a machine learning problem for the task, data and solution framework behind this sheet, then go deeper in Introduction to Machine Learning for SEO.