Unsupervised Learning, Recommenders, Reinforcement Learning
DeepLearning.AI · View on Coursera ↗
Taught by Andrew Ng, Aarti Bagul, Geoff Ladwig, Eddy Shyu
Data ScienceComputer Science
About the course
In the third course of the Machine Learning Specialization, you will: • Use unsupervised learning techniques for unsupervised learning: including clustering and anomaly detection. • Build recommender systems with a collaborative filtering approach and a content-based deep learning method. • Build a deep reinforcement learning model. The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. In this beginner-friendly
The dissent
The most-upvoted critical review (8 votes, 3.0★).
“While the material presented in this course was very interesting, and Andrew Ng was delightful, there was SO MUCH repetition, redundancy, and basic (yet unnecessary) math that the content could be condensed to about half the length, and anything that falls into "don't worry about it" category could be made optional. Personally, I would have preferred having to worry about it - yes, people should expect to learn and use math in these technical courses.”
Coursera's 4.9 comes from 5,754 ratings. Of those, 0.5% are 1–2★.
Starisk reads the reviews learners voted most helpful, and weights each by the square root of its votes: a review with 80 upvotes counts about five times one with 2. Unvoted reviews are not skipped out of laziness — by this model they carry no evidence that other students endorsed them.
That measures what the most-endorsed reviews say, not “true quality.” The rating distribution above is the whole population, so you can see the sample next to it.
Reviews read on 23 Oct 2026.Read them on Coursera ↗