Machine Learning – mad science or a pragmatic process?

As the interest in data science, predictive analytics and machine learning has grown in direct correlation to the amount of data that is now being captured by everyone from start ups to enterprise organisations, endjin are spending increasing amounts of time working with businesses who are looking for deeper and more valuable insights into their data. As such, we've adopted a pragmatic approach to the machine learning process, based on a series of iterative experiments and relying on evidence-based decision making to answer the most important business questions. In this series of posts, we're going to look at what machine learning really is (and isn't), the experimentation process and some real examples of how and where we've put it to use. It's no exaggeration to say that the hype around machine learning has gone a bit crazy recently. Since the explosion of Big Data, the need to make sense of masses of digital information has not surprisingly increased, causing a wave of excitement around a new era of data science.