COUniversity of Washington · on Coursera
Machine Learning: Clustering & Retrieval
Beginner English Professional CertificateFREE
What you'll learn
Implement nearest-neighbour retrieval for similarity search
Cluster data with k-means
Use mixture models and the EM algorithm
Apply latent Dirichlet allocation to documents
Evaluate clustering and retrieval quality
Code the methods in Python
This course includes
Yes
Certificate
Yes
Mobile access
English
Language
Comparison · LBS
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Instructor
EF
Emily Fox
Coursera instructor
— learners— courses — instructor rating
Emily Fox and Carlos Guestrin of the University of Washington round out their specialization here with clustering and retrieval. Their case-study style and clear explanations carry through to the unsupervised material.
Requirements
- Some Python programming
- Basic maths (linear algebra and statistics help)
Who this course is for
- Learners building practical ML skills
- Data scientists working with unlabelled data
- Anyone in the UW ML Specialization
About this provider
CO
Coursera
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Frequently asked questions
Retrieval finds the most similar items to a query (like nearest-neighbour search); clustering groups items into segments without labels. The course covers both, with k-means, mixture models, and LDA.
Some Python and basic maths. It's hands-on, so expect programming assignments.
You can audit the full course free on Coursera. A certificate requires a subscription.
It's approachable thanks to the clear lectures, but it assumes you're comfortable with Python and have seen basic ML concepts — it's not an absolute-first course.
No — it focuses on classic clustering and retrieval methods. For deep learning, DeepLearning.AI's courses are a better fit.