_:n5159789c55ea409db5930be90e7fa45axb54
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"(09/2019)\nOverall the courses in the specialization are great and provide great introduction to these topics, as well as practical experience. Many topics are explained clearly, with valuable field practitioners insight, and you are given quizzes and code-exercises that help deepen the understanding of how to implement the concepts in the videos. I would recommend to take them after the initial Andrew Ng ML course by Stanford, unless you have prior background in this topic.\n<text />There are a few shortbacks:\n1 - the video editing is poor and sloppy. Its not too bad, but it’s sometimes can be a bit annoying.\n2 - most of the exercises are too easy, and are almost copy-paste. I need to go over them and create variations of them in-order to strengthen my practical skills. Some exercises are quite challenging though (especially in course 4 and 5), and I need to go over them just to really nail them down, as things scale up quickly. Course 3 has no exercises as its more theoretical. Some exercises have bugs - so make sure to look at the discussion board for tips (the final exercise has a huge bug that was super annoying).\n3 - there are no summary readings - you have to (re)watch the videos in order to check something, which is annoying. This is partially solved because the exercises themselves usually hold a lot of (textual) summary, with equations.\n4 - the 3rd course was a bit less interesting in my opinion, but I did learn some stuff from it. So in the end it’s worth it. Not sure I would have taken it as a stand-alone course, though.\n5 - Slide graphics and Andrew handwriting could be improved. \n6 - the online Coursera Jupyter notebook environment was a bit slow, and sometimes get stuck. \n<text />Again overall - highly recommended\n<text />"
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