The course developed by Andrew Ng is quite interesting, going to the essentials in order student get the big picture and the essential tools for building the backbone of future ML applications. Of course, being confident with mathematics principles and notations will be helpful but most of the time, its not an issue if you have the minimal knowledge. What it lacks on Coursera is the next stage of this course where we could investigate further the technologies presented but in more technical way. Maybe we might see that in the future...Regarding course supports (videos, forums ...), they are of good quality and the fact Andrew used them by drawing on slides helps to have a better understanding. We could notice that there are few minor errors (eg: a j index which becomes i in J(theta) writing) and I think the technical slides on Back propagation could be improved if a dedicated slide to used mathematical notations / definitions. Sometimes, there are some errors which could induce some confusions. But these minors errors dont hide the impressive work done by Andrew.Regarding assessments, quizzes could be tricky if you dont got the spirit (not an exam habit in France) and coding exercises are well structured in order the student will focus on the core modules of the lesson and not on information flow. These exercises are inspiring if youre interesting in teaching and inspiring for Data Scientist Apprentices if you investigate the utils functions developed to support the exercise.Many thanks for this great course and I hope my two cents will help other people to attend itBruno
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