Women In Machine Learning: Katie Malone Udacity

For resources, the single best thing you can do is find people who can challenge you and make you think. These can be collaborators that you work with in "real life," or folks online (say, for example, contributing to open source projects). I've also found that the projects that turn out the best for me are the ones that I find most interesting or exciting, so I've grown to put a lot of effort into reading about many different things so I can find out what seems most cool or fun and then go after that--at first it felt a little backward, like instead I should be reading up to find out what I "should" be excited about and then letting that guide my choices, but I've found that thinking about it instead from the perspective of "what makes me excited, and let's think of a way to apply machine learning or data science to that" is way more fun for me. That's not really a resource, sorry, but I think it's important. For resources, I love online courses (like Udacity of course, but there are lots of good ones out there), podcasts (I have to say that, since I host one as a side project–Linear Digressions), and there are some excellent blogs out there too.