So you think you don't have enough data to do Machine Learning

Ask a beginner why ML is so difficult and you will most likely get an answer in the lines of'the math behind is really complicated' or'I don't fully understand what all those layers do'. While that is obviously true and certainly interpreting ML models is a muddy subject, the truth is that ML is difficult because more often than not the data we have cannot live up to the complexity of our models. This is a very common issue in practice and since your models are only as good as your data is, I have gathered some of the most relevant guidelines to be used when you face shortage of data. This is something everyone working with data has wondered at some point. Unfortunately, there is no set of fixed rules that will give you a direct answer and you can only resort to guidelines and experience.