@bitL's comment of it being "practical, not theoretical" is pretty spot-on. I'm currently enrolled in and taking the course; I've also taken Udacity's CS373 cou

@bitL's comment of it being "practical, not theoretical" is pretty spot-on. I'm currently enrolled in and taking the course; I've also taken Udacity's CS373 course (when it was first offered in 2012) - and early courses that formed the foundation of Udacity and Coursera (ie - the Stanford-sponsored AI Class and ML Class, respectively). Again - what I noted in another comment - if you aren't comfortable with coding (Python and C/C++ in the nanodegree course), or linear algebra - you might have a tough time. You'll also likely want to brush-up on your stats/probability knowledge (though this hasn't come up yet - not like the CS373 course, or the AI and ML Classes). Finally, while you won't absolutely (well - not that I've seen yet) need super-detailed knowledge of what a derivative is or how it is derived (?) - that bit of Calculus seems to be important in machine learning (it is something I struggle with). Ultimately, give it a shot and have some fun; so far, I have enjoyed everything (just finished the lesson and lab for the Keras framework last night). Don't get too hung-up on the "due dates" for projects; ultimately the main due-date is the end of the term (you have to have everything completed by then to not have to retake the term). Be sure to use the forums, your mentor, the Slack channels, and the facebook page to get help and inspiration. Oh - and you are able to publish your solutions and code around, but if you decide or need to use code provided by someone else (or you use someone else's solution, or ideas from a paper, etc) - YOU NEED TO DOCUMENT THIS in the project rubric (otherwise it will be considered plagiarism which, if found out, may get you kicked from the course) - so be sure to credit your sources! Finally - if you enjoy the course - be sure to check out Udacity's CS373 course, their "Intro to Artificial Intelligence" course (both of those I believe are free?), and Coursera's Machine Learning course (also free). There are also a ton of other great online resources and books out there; let me know if you need or want suggestions!