Machine Learning With Go

Gathering and Organizing Data

Polls have shown that 90% or more of a data scientist's time is spent gathering data, organizing it, and cleaning it, not training/tuning their sophisticated machine learning models. Why is this? Isn't the machine learning part the fun part? Why do we need to care so much about the state of our data? Firstly, without data, our machine learning models can't learn. This might seem obvious. However, we need to realize that part of the strength of the models that we build is in the data that we feed them. As the common phrase goes, garbage in, garbage out. We need to make sure that we gather relevant, clean data to power our machine learning models, such that they can operate on the data as expected and produce valuable ...