What is machine learning?
Machine learning is the science of finding patterns and making predictions from data. This kind of learning is based on work in multivariate statistics, data mining, pattern recognition, and advanced/predictive analytics. Machine learning explores the construction and study of algorithms that can learn from data. To put it simply, machine learning is a form of artificial intelligence that actually works.
While machine learning is not new, there has been much progress made in the field, which is rapidly expanding and continually being divided into different sub-specialties. There is also a large amount of research going on today that is related to machine learning and neural networks.
We live in an era of “Big Data”. Every consumer’s action performed online today is being tracked by various sources. All of this data provides businesses with the opportunity to discover insights that can lead to better and faster decisions. The organizations that can quickly realize value from their data assets via advanced analytics such as machine learning will become the obvious leaders, while the rest will be left behind.
What are the benefits of machine learning?
• Machine learning solves problems that cannot be solved by numerical means alone.
• Machine learning methods are particularly effective in situations where deep and predictive insights need to be uncovered from data sets that are large, diverse, and fast changing. Across these types of data, machine learning easily outperforms traditional methods on accuracy, scale, and speed.
• Machine learning methods are scalable and can be used in analyzing potential customer churn across data from multiple sources, such as transactional, social media, and CRM.
• High performance machine learning can analyze all of a Big Data set rather than a sample of it. Being scalable not only allows predictive solutions based on complex algorithms to be more accurate, it also drives the importance of software’s speed to interpret the billions of rows and columns in real-time and to analyze live streaming data.
Example of machine learning at work
The following are some examples of machine learning over a Big Data stream:
• Detecting fraud in the millisecond it takes to swipe a credit card, machine learning rules not only on information associated with the transaction, such as value and location, but also by leveraging historical and social network data for accurate evaluation of potential fraud.
• Search engines like Google and Bing, Facebook and Apple’s photo tagging application and Gmail’s spam filtering
• When Amazon or Netflix recommends a book or a movie you would like.
• When Google predicts that you should leave now to get to your meeting on time.
• When Pandora magically creates your ideal playlist.
Computer scientist Andrew Ng, director of the Stanford Artificial Intelligence Lab says that machine learning is a step towards the “Artificial Intelligence (AI) dream of someday building machines as intelligent as you or I.”
With Big Data projected to drive enterprise IT spending to $242 billion according to Gartner, Big Data is here to stay, and as a result, more businesses are getting excited about machine learning. To many enterprise organizations Big Data represents a strategic asset. With advanced analytics and machine learning, companies can discover insights and generate predictive models to take advantage of all the data they are capturing.
This advanced analytics technology means that instead of looking into the past for generating reports, businesses can predict what will happen in the future based on analysis of their existing data. The value of machine learning is rooted in its ability to create accurate models to guide future actions and to discover patterns that we’ve never seen before.
Today it’s no longer about how to store your data; it’s about how to translate that data into real business value.