Big Data Makes for Exceptional Machine Learning
As a new year kicks off, the words “big data” continue to be spoken in multiple industries all over the world. 2015 was a tremendous year for big data analytics as more businesses and organizations than ever before adopted data science in order to grasp some of the benefits it provides. In much the same way, 2016 look to continue that trend, with more in-roads being made for big data in areas such as finances, health care, manufacturing, and even sports organizations. At the same time, machine learning is quickly catching on. Both big data and machine learning are often mentioned in the same breath, yet the two are not synonymous. Machine learning can be performed without the massive and complex data sets people often think of when referring to big data. Likewise, big data analytics doesn’t necessarily involve machine learning techniques. While the difference should be noted, if the goal of an organization is to extract useful and actionable information from the data they collect, machine learning will likely be in the cards. And if they want to use machine learning to the fullest extent, even taking it to an exceptional level, big data will be needed.
The details behind how machine learning works usually require a mind best suited for data science. Put in simple terms, machine learning involves processing data for the purposes of learning a specific task. How the solution is reached is mainly based on the algorithms followed, but it’s particularly important to note that no actual programming is involved in the process. By following the algorithms, the computer basically finds the best way to reach a desired outcome. That means that two different machines may actually end up finding different answers to the same problem. This of course could lead to some exciting possibilities and interesting applications, some of which are already being employed by businesses. Take Google for instance and their Google Translate function. Through massive data sets collected from input by users, machine learning algorithms find the best ways to translate from one language to another. Perhaps a gifted computer programmer could have created code for that purpose, but the code would have been extremely long, incredibly complicated, and likely filled with errors. Machine learning simply works best in this situation, and that concept is quickly spread to other functions that businesses and organizations encounter.
There’s good reason that machine learning has gained so much interest in such a short period of time: it works. That’s actually putting it lightly. Machine learning works, in the words of Quanta Magazine’s Ingrid Daubechies, “spectacularly well.” Classic and traditional techniques can certainly get the job done, but machine learning is well the results are fascinatingly effective. This effectiveness even extends to the likes of deep learning and neural networks, what many experts are predicting are the precursors to true artificial intelligence. In fact, the incredible results shown from machine learning have even baffled mathematicians in terms of reaching an adequate explanation. The effectiveness of the algorithms used definitely have a part to play in machine learning’s reputation, but some of the results can be credited to the use and intersection of big data analytics. If anything, it’s big data that turns machine learning into something really revolutionary.