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University of Washington · on Coursera

Machine Learning: Classification

Beginner English SpecializationFREE
Our recommendation
The classification-focused course of the University of Washington's ML Specialization on Coursera. Emily Fox and Carlos Guestrin work through logistic regression, decision trees, boosting and more, with a strong conceptual grounding. As with the rest of the specialization, note it dates from 2016 and relies on older proprietary tooling.

Good for: A solid conceptual grounding in classification methods.

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

Skills you'll gain

ClassificationLogistic regressionDecision treesBoostingMachine learningModel evaluation

Is this course right for you?

A good fit if you…

You want to understand classification deeply
You like case-study learning
You accept the tooling is dated

Consider something else if you…

You want current industry tools from day one
You want a deep-learning-first course
You want a quick code tutorial

Requirements: Basic Python and some maths comfort; the regression course helps as a precursor.

Realistic time: Around 15–20 hours over a few weeks.

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.

What you'll learn

Build classifiers with logistic regression
Use decision trees and ensemble methods like boosting
Handle missing data and class imbalance
Evaluate classifiers with the right metrics
Apply classification to real case studies
Implement the methods in Python

This course includes

Yes
Certificate
Yes
Mobile access
English
Language

What it costs

You can audit the course free on Coursera. A certificate needs a Coursera subscription (about $49/month) or Coursera Plus, with financial aid available. It is part of the University of Washington Machine Learning Specialisation.

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Is the certificate recognised?

A University of Washington certificate on Coursera is a credible learning record. It is a course certificate rather than a professional qualification or degree.

Last updated

Created in 2016, this course teaches through the proprietary GraphLab/Turi Create library rather than today's standard tools like scikit-learn or PyTorch. The concepts remain sound, but the tooling is dated — factor that in if you want directly job-ready code.

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.

About this provider

CO
Coursera
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Frequently asked questions

The concepts hold up, but it is a 2016 course using the proprietary GraphLab/Turi Create library rather than scikit-learn or PyTorch.
Yes, you can audit it free; the certificate needs a Coursera subscription or Coursera Plus.
It helps — this is part of the same specialisation and assumes similar Python and maths comfort.
Logistic regression, decision trees, boosting and other classification methods, with strong conceptual teaching.
Around 15–20 hours over a few weeks.
Free
to audit
Enroll now