For context, I am a cell biologist with experience of using / acquiring images with a microscope but with little prior use of image analysis methods. I was interested in this course as my project is moving in a direction where I thought the ability to quantify and correlate features of images might become useful, and this is certainly a good course to get started on image analysis.
The course is beginner-friendly, walks the learner through the basics of image analysis / image acquisition, followed by an introduction to image analysis and the various things to bear in mind. As images are quite often affected by noise, there is quite an in-depth discussion on the types of noise and now their impact can be minimised which I thought was helpful. Later in the course, more specific methods for image analysis are discussed, with supporting practicals.
The instructors particularly Tony and Andrew are incredibly involved in the discussion - a trait that I generally appreciate of MOOC educators. From the discussion threads it became apparent that many others were, like me, taking this course with specific objectives in mind and quite often the questions were asked at the level of detail exceeding the scope of this introductory course. Oftentimes the instructors participated in the discussion nonetheless, providing helpful links to other resources elsewhere on the web.
I recommend this course for learners looking to understand how image analysis works, the possible limitations and how to potentially overcome them (to a certain degree that is), and a general overview of the tools available to users today. This course is not a hands-on "how do I solve this problem with image analysis" one per-se (although there are practicals, it felt to me like they were there more to illustrate a concept rather than to get learners well-versed with the packages) but would put the learner in a more informed position to try other documented and publicly available tools out there.