What is Classification? – Lesson Preview
This lesson introduces one of the most essential concepts in machine learning: classification. You’ll learn what classification means, how it differs from other ML approaches like regression and clustering and why it’s foundational for automating decision-making in SEO and marketing.
Lazarina explains how classification models work using labelled data to sort inputs (like emails, web pages, or keywords) into meaningful categories such as “spam” or “not spam,” or “product page” vs “blog post.” You’ll explore the types of classification tasks, from binary and multi-class to multi-label and imbalanced models, and understand their use cases across industries.
Finally, the lesson walks you through the most common classification algorithms, including Logistic Regression, Decision Trees, Random Forests, SVMs, KNN and Naive Bayes, highlighting how each works and when to use them.
What you’ll learn (why it matters)
- Understand classification — because it’s a core ML task for automating content and keyword analysis.
- Differentiate between classification, regression, and clustering — to choose the right algorithm for your data.
- Identify types of classification tasks — to design models suited for binary, multi-class, or imbalanced data.
- Explore key ML algorithms — because knowing their strengths helps match the right one to your project.
- Recognize real-world applications — to connect ML theory with SEO and marketing use cases.
Key concepts (with mini-definitions)
- Classification — sorting labelled data into categories using predictive models.
- Regression — predicting continuous numerical outcomes instead of discrete categories.
- Supervised learning — ML trained on labelled data to make predictions.
- Binary classification — choosing between two outcomes (e.g., spam vs. not spam).
- Multi-class classification — assigning one label from several possible categories.
- Multi-label classification — assigning multiple labels to one instance.
- Imbalanced classification — handling uneven distributions across categories.
- Clustering — grouping unlabelled data based on similarity (unsupervised).
Tools mentioned
None explicitly mentioned.
Practice & readings
- Review the DataCamp article on “One-vs-One and One-vs-Rest” strategies (linked in the lesson).
- Suggested exercise: pick one of your SEO datasets (e.g., URLs or keywords) and label 50 entries by type, then identify which classification task it would fit (binary, multi-class, or multi-label).
Key insights & takeaways
- Classification is foundational to ML and drives predictive automation.
- Choosing the right algorithm depends on your data type and goal.
- Every algorithm (Logistic Regression, Decision Tree, etc.) has trade-offs in accuracy, bias, and interpretability.
- Bias in classification models can skew real-world outcomes, understanding it is essential.
- Knowing when to use classification vs. clustering prevents wasted effort and incorrect analysis.
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