Practical Machine Learning And The Data Behind It
The popular new buzzword, Machine Learning (ML) is often confused with Artificial Intelligence (AI). However, they are two entirely separate entities. You probably have a good idea of what Artificial Intelligence is but how much do know about Machine Learning? Not a lot? Well, let us tell you everything about practical machine learning including what is it and how it is helping businesses.
Understanding Machine Learning
Contrary to popular belief, Machine learning is nothing new, rather it is a concept that is making a comeback. Over the past few years, the interest in machine learning has increased greatly. The comeback is driven by the tremendous amounts of data being emitted and captured by sensors across the globe. With ever-increasing data, combined with cheap storage and incredibly low computational costs, new and faster ways of analyzing this data – beyond human capability are required. So, what is machine learning and how does it work?
Computers programs learn, change, develop and grow on their own when they come across new data. By converting the analytical model building into an automated process, machine learning—a data analysis method, allows computers to find insightful information without being programmed to do so. In fact, you’ve already experienced firsthand some notable applications of machine learning. These examples include friend recommendations on Facebook, Google’s self-driving car, offers recommendations from Amazon, Netflix movie recommendations and Cyber fraud detection.
How Machine Learning is Helping Businesses
Today, machine learning is a playing a vital role in the business world. According to a report by McKinsey—a worldwide management consulting firm, innovation and technological advancement in the future will be driven by machine learning. Today, there is much hype around machine learning in the business world and there’s a good reason for it. The following are some of the ways machine learning is helping businesses run more efficiently and profitably.
Better Understanding of Sentiments
One thing that machine learning excels at is analyzing sentiment. Incoming messages and the underlying sentiment can be determined to be positive, negative or neutral. Many organizations today are relying on machine learning to make decisions related to the customers. For example, a standard operating procedure (SOP) for many businesses today is using machine learning for social media listening.
Generate More Valuable Content
The average piece of user-generated content (UGC) is dreadful, to say the least. Often, the content is filled with typos, misspellings and flat-out wrong information across the board. Without requiring a human to tag each piece of content, machine learning identifies the best and worst user-generated content (UGC) to filter out the bad bubble and up the good.
As seen above, the use of machine learning can benefit businesses of all types and sizes. But this is only the beginning and we are still very much in the infancy stage. This is the reason many businesses today are using ML tools to automate their decision processes. This includes the increased use of Matlab, Python and R—programming languages that provide powerful sets of features for applying machine learning algorithms, in business operations.
It is predicted that by the year 2025, artificial intelligence (AI) will become a market worth hundred billion dollars, with machine learning having a sizeable share. The total amount spent on machine learning in 2016 was five billion, and this number is expected to increase greatly in the coming years. One can clearly see why!