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Jon Gauthier


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\-- What was your prior experience in the field? None. (Web developer since 2006.) \-- What did you learn? The Stanford ML course teaches a set of immediately applicable machine learning algorithms, from linear regression to feedforward neural networks. Professor Ng consistently includes in his lectures notes on the implementation of the content presented. He is straightforward about the caveats of the methods described in the course, and spends an entire section of the course enumerating the various ways to diagnose which errors are affecting a given implementation / application and how to make the proper correction. \-- Did the course meet expectations? The course easily exceeded my expectations. The concepts in this course now serve as an entire new set of utilities on my toolbelt as a computer programmer. They have been enormously useful and have without a doubt added to my value as a programmer. \-- What didn't you like? The most difficult math that was fully covered in the course dealt with matrix algebra. Concepts with steps involving calculus or linear algebra were only briefly described. While an understanding of the mathematical underpinnings is not required to build a competent implementation of one of the ML algorithms taught, it would have been interesting to see more (potentially optional) lectures on the more technical mathematical support that ML depends upon.