When it comes to big data or even data science in itself, we often think of machine learning. The power of machine learning was discovered in the late twentieth century and is now becoming extremely important in many industries. But what is machine learning and why is it useful? Simply put, ML, or machine learning, is an approach in which computers, specifically software, can optimize the process of predicting trends or outcomes and adapt through experience. It is a subset of Artificial intelligence, which is giving human intelligence to computers; ML is becoming more and more useful because it allows us to delegate tasks that are often difficult to accomplish without some sort of smart algorithm. Given that machine learning isn’t anything new, it seems as if we need it more than ever in a era where convenience, automation and intelligence of machines are everything.

Since a majority of scientific endeavors involve data collection and analysis through various scientific methodologies, it would be useful to do more with this valuable data. With ML this is certainly possible; even by using the most basic available frameworks, you are able to establish a new dimension to your research or perhaps further investigate areas that were never before feasible. For example, we are able to apply ML to genomics for gene sequence prediction or even cancer prognosis through computer vision algorithms. As expected the future of ML is very promising and masses are immersing themselves into this “new” technology. Existing frameworks exist allowing for users of all skill levels to take part. Google’s machine learning framework, TensorFlow, is one of many frameworks that exist in the machine learning community and it isn’t restricted to just computer science; these are being adapted to healthcare, the scientific community, and more.

If you’re new to all of this, there isn’t much to be worried about. It’s never too late to pick up ML and many can attest to the fact that what is accomplished now is only the beginning and its limits applications are virtually endless. To further explicate upon the various ML techniques would be a fruitless endeavor but there are comprehensive online tutorials, MOOC’s (massive open online courses), and thorough documentation designated for one to have a solid foundation before delving into larger scale applications. However, frameworks and existing algorithms are only of use if one is knowledgeable in certain programming languages. From a personal standpoint, machine learning was not something I was concerned about until I had to use an open source framework to accompany my research as a mechanism that would help optimize results and findings. Now, myself and the rest of the world are investigating its possibilities, more out of inquiry than anything. It further bridges the gap between computer science and the rest of the world and will serve many purposes in the time to come. Be on the look out.

<Timothy J. Park Fairmont Preparatory Academy 12th grade>