Machine learning uses algorithms that allow a computer to find information and data trends without being explicitly told where to look.
Machine learning is a type of artificial intelligence that essentially allows a computer to “think for itself,” learning from what it finds, identifying patterns and changing its approach on the basis of its learning.
It might sound like science fiction, but machine learning has actually been around for a number of years. The phrase was first coined in 1959 by Arthur Samuel, who defined it as “the subfield of computer science that gives computers the ability to learn without being explicitly programmed.” However, it was not until the digital age that the true value of machine learning really came into its own.
Valuable to your business
Machine learning is of huge relevance to businesses in the 21st century due to the enormous volume and variety of data that is now available, from internet search analysis, online purchasing trends, social media likes, online reviews and a whole host of other sources. At the same time, computer processors are more powerful than ever, yet within the reach of any budget.
When you look at it that way, it is clear to see why today is known as the information age. All these factors combine to allow us to analyse complex data in large volumes, and deliver fast, accurate, large scale results.
From this, your business can gain invaluable insights and predictions, to guide and inform better decision-making, faster than ever.
Machine learning brings a level of speed and complexity to the table that could not have been dreamt of a few years ago.
Types of machine learning
There are a growing number of machine learning methods and algorithms in use, and these grow more varied and complex every day, but in simple terms, they fall into three basic categories:
Supervised learning is perhaps the most common. Here, an algorithm learns to predict an output, based on various input variables. It is called “supervised” because the algorithm is working over set target variables. It can iterate over training data until it achieves an acceptable level of success, almost like studying to pass an exam. A good example of supervised learning is in fraud detection, where the algorithm learns from a set of fraudulent and genuine records to predict frauds in future.
Unsupervised learning still has the input data, but the algorithm is left to find the output data for itself. The objective is to learn about the underlying structure of the data and identify patterns and trends. Unlike supervised learning, there are no “right answers” here. Unsupervised learning can, for example, be used to examine consumer behaviour and identify market segments.
Reinforcement learning provides what is known to mathematicians and statisticians as a Markov decision process. This is when the algorithm learns to react or make decisions based on what it finds. It is essentially learning through interactions with its environment. The algorithm takes a trial-and-error approach and selects each new action on the basis of its past experiences.
The scale and complexity of today’s data means that the importance of machine learning for business will grow exponentially, in every field imaginable, from credit rating to automated transport and from market analysis to predicting equipment failures.
How can machine learning help drive your business forward, to stay ahead in the 21st century and beyond?