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Machine Learning: Classification

Beginner English SpecializationFREE
SkillsClassificationLogistic regressionDecision treesBoostingMachine learningModel evaluation

Is this course right for you?

Our take
The classification instalment of the University of Washington's ML Specialization, again with Emily Fox and Carlos Guestrin.

Good for: A solid conceptual grounding in classification methods.

Skip if: You want current, job-ready tooling from the start.

It works through logistic regression, decision trees, boosting and more, with the same strong conceptual grounding the specialisation is known for — you come away understanding why these methods work, not just how to call them. The catch is the one that runs through the whole specialisation: it's a 2016 course taught on the proprietary GraphLab/Turi Create library rather than the scikit-learn or PyTorch you'd use on the job today.

That makes it a course to choose for the ideas rather than the code. If you want current tooling from the first line, look elsewhere; if you want to genuinely understand classification, it still delivers. Free to audit on Coursera, certificate on a subscription (about $49/month) or Coursera Plus with financial aid. The methods it explains remain foundational (as of 2026).

Comparison · LBS

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About this course

Machine Learning: Classification is the classification-focused course of the University of Washington's ML Specialization. Emily Fox and Carlos Guestrin work through logistic regression, decision trees, boosting, and handling issues like missing data and class imbalance — applied to case studies such as sentiment analysis and loan default prediction.

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Instructor

EF
Emily Fox
Coursera instructor

Emily Fox and Carlos Guestrin of the University of Washington bring research depth and unusually clear teaching to the specialization. Guestrin in particular is often praised by learners for making complex ideas approachable.

Frequently asked questions

Partly — the same caution as its sibling courses. Part of the University of Washington's Machine Learning specialization, it is conceptually solid but older, and historically used the GraphLab/Turi toolkit rather than the now-standard scikit-learn. The classification concepts — logistic regression, decision trees, and so on — remain valid and valuable; it is the specific tooling that is dated, so consider applying the ideas in scikit-learn.

It helps. It is designed to follow the regression course in the University of Washington specialization, building on that grounding and assuming comfortable Python plus basic ML maths. You could take it fairly independently if you already have those foundations, but the sequence is intended, so arriving without the earlier material or the prerequisites makes it a steeper climb.

Supervised classification: predicting categories rather than continuous values, using methods like logistic regression, decision trees, and ensemble techniques, plus handling issues like overfitting and imbalanced data. It focuses on building and evaluating models that assign items to classes — spam or not, churn or not — which is a very common real-world machine-learning task.

Yes, you can audit it on Coursera and watch the lectures at no cost. The graded programming assignments and the certificate require a subscription, with financial aid available if you apply. Given the dated tooling, applying the concepts in scikit-learn yourself may serve you better, so auditing for the ideas is a sensible approach.

As a single course within the specialization, it typically runs about six weeks at a few hours a week, more if the concepts are new. It is hands-on, so the time goes into building and evaluating classification models, with the full University of Washington specialization a considerably longer commitment if you continue through it.
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