ML Data Characteristics – Lesson Preview
Every machine learning project begins with one essential question: what kind of data are you working with? In SEO, understanding your data’s characteristics is not just technical detail, it’s the foundation for choosing the right tasks, models, and even APIs to automate your workflows.
This lesson breaks down the main data types you’ll encounter in your SEO practice; textual, numeric, image, time series, audio, and video and shows how each ties directly to real-world SEO tasks like classifying content, forecasting traffic, or generating alt text. By connecting data characteristics to machine learning methods, you’ll learn how to bridge the gap between the data at hand and the outcomes you want to achieve.
For SEO professionals, this means gaining clarity on what’s possible, avoiding wasted effort on mismatched models, by discovering new ways to analyze, classify, and predict performance with machine learning.
What you’ll learn (why it matters)
- Identify data types — because text, numbers, and visuals need different approaches.
- Map SEO data to ML tasks — because classification, clustering, or forecasting depends on input type.
- Ask key data questions — because format, labels, scale, and cleanliness shape your project.
- Recognize data limitations — because constraints affect feasibility and model accuracy.
- See real SEO use cases — because practical context makes ML workflows clear.
Key concepts (with mini-definitions)
- Textual data — website content, queries, or reviews in text form.
- Numeric data — numbers stored in tables or time series, like clicks or conversions.
- Image data — photos, product images, or diagrams used in SEO.
- Time series data — sequential values recorded over time, like traffic trends.
- Audio data — sound-based inputs such as call transcripts or podcasts.
- Video data — combined visual and audio content like YouTube or TikTok clips.
- Labelled vs. unlabelled data — determines if supervised or unsupervised learning is used.
- Data scale — the number of data points, affecting costs and architecture.
Tools mentioned
None explicitly mentioned.
Practice & readings
- Review your current SEO data sources and classify them into text, numeric, image, time series, audio, or video.
- Check whether your datasets are labelled or unlabelled to identify potential ML approaches.
- Additional resource – ML Data Characteristics, Mapped to SEO Tasks, ML Tasks, and Possible Algorithms
Key insights & takeaways
- The type of data directly determines the ML models you can use.
- Text, numbers, images, and sequences unlock different SEO automation opportunities.
- Data format, scale, labels, and cleanliness are non-negotiable factors.
- Knowing your data helps you avoid mismatches and wasted effort.
- SEO consultants often have accessible data sources but may need cross-team collaboration.
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