I know nothing about machine learning, so take the following with a pinch of salt. (I originally posted it as a comment and was encouraged to repost as an answer, so I guess it can't be terrible.)
You get more data by, well, collecting more data...
Simply using some algorithm to generate more data similar to the data you already have won't help, because it doesn't change the distribution of the data, so doesn't do anything to address the problem that your data might not be representative of reality. For example, suppose I make a crappy painting (I can't paint at all) and I ask ten people from my family and close friends if it's any good. They all say, "Yes, David, it's wonderful" because they don't want to hurt my feelings. If you advise me to ask more people's opinion, that's because you feel that I didn't get a proper variety of opinions. Just asking the same ten people again won't change anything: it'll just make me more confident in my bad data because now "twenty" people have told me that my crappy painting is wonderful.
Ultimately, the same problem applies to any algorithmic approach: the best it can do is to generate more data like the data you already have, but that can't possibly have higher quality. And note that any algorithmic approach is, essentially, "use machine learning to generate more data like the data I already have, and then use machine learning to do X with all that data" so it can't be any better than just using machine learning on the original dataset.