Machine Learning has its roots in statistical analysis. Many of its fundamental concepts were already well understood for more than a century. While statistics itself has seen a very gradual adoption as a viable tool for solving real-world problems, Machine Learning as an engineering discipline also had a rough false start in the late 80s, giving the entire field of Artificial Intelligence a bad name. As it turns out, the reason Machine Learning as a specialty and statistics as an application discipline failed to deliver at first is less an indication of fundamental flaws and more a consequence of two market conditions: not enough data and not enough computing power. Both of these factors were to be erased within a few short decades.
Today, the Big Data juggernaut is rapidly revolutionizing the IT infrastructure landscape and proven Machine Learning techniques are reshaping business practices in almost every nameable industry. Pundits in the Blogosphere are quick to declare Data Science the sexiest job on Earth.