Coursera - Machine Learning Foundations: A Case Study Approach Review

Machine Learning Foundations: A Case Study Approach is a 6-week introductory machine learning course offered by the University of Washington on Coursera. It is the first course in a 5-part Machine Learning specialization. The course provides a broad overview of key areas in machine learning, including regression, classification, clustering , recommender systems and deep learning, using short programming case studies as examples. The course assumes basic Python programming skills and it uses a software package called GraphLab that requires a 64-bit operating system running Python 2.7. Grades are based on periodic comprehension quizzes and short programming assignments.


The course covers a broad range of machine learning topics at a high level with the promise of drilling down into the details in future courses in the specialization. The lecturers have good chemistry, but they tend to get distracted when they are on screen together. The video and slide quality are very good and although the delivery is a little rough around the edges at times, the lectures are informative. The machine learning methods covered aren’t necessarily treated as complete black boxes, but the course intentionally avoids getting too deep into the details, putting the emphasis on conceptual understanding.

The weekly labs are contained in short IPython Notebooks—interactive text and code documents rendered in a web browser—that illustrate some simple models in GraphLab. The labs themselves are easy and don’t require much coding other than calling various built in GraphLab functions. The hardest part about the class is getting your programming environment set up in the first place. If you don’t have a new version of 64-bit Python 2.7, you can’t run GraphLab. It is relatively easy to get set up if you can use the recommended Anaconda Python distribution, but getting things set up manually on an existing Python installation may prove troublesome. The instructors provided some 

workarounds for doing the course without GraphLab or using GraphLab on Amazon’s cloud computing service; I wouldn’t take the course without getting GraphLab working in some form. Many students decried the use of a non-open source package for an open class; I think it is useful to be exposed to new tools and GraphLab seems cleaner than Python’s popular scikit-learn package. In this sort of course, the focus should be one concepts rather than syntax.


Machine Learning Foundations: A Case Study Approach achieves its goal of introducing machine learning at a high level without rushing or trying to cram too much into any particular week. What the professors lack in terms of polish they make up for with enthusiasm. Compatibility and setup issues will be a roadblock for some, but overcoming them is worth it.


I give Machine Learning Foundations: A Case Study Approach 4.5 out of 5 stars: Great.