In any field of life, people always wants to be associated with the latest, be it gadgets, appliances or the technology. Applying this to the world of information technology, we see organizations investing strategically in Cloud Computing and IoT (Internet of Things). To get an edge over competitors, organizations have started investing into resource pools to enable the support that would be required at the back end of IoT and Cloud implementations and into Artificial Intelligence and Machine Learning.
It’s hard to keep up with the pace of change in the data science and machine learning fields. And when you’re under pressure to deliver projects, learning new skills and technologies might be the last thing on your mind. But if you don’t have at least one eye on what you need to learn next you run the risk of falling behind. In turn this means you miss out on new solutions and new opportunities to drive change: you might miss the chance to do things differently.
When I work with companies and executive teams, I often find that there is some confusion about the differences and overlaps between data science, machine learning, and artificial intelligence. So, I thought it would be worth creating a quick and straightforward guide to these three terms, which are closely related, sometimes used interchangeably, but really convey different meanings.
Machine Learning is the core subarea of artificial intelligence. It makes computers get into a self-learning mode without explicit programming. When fed new data, these computers learn, grow, change, and develop by themselves.
Clustering is the machine learning technique. It is the task of dividing the data points into a number of groups such that data points in the same group more similar to the other data points in the same groups and dissimilar to other data points in the other group. It is basically a collection of objects based on similarity and dissimilarity between them. Clustering is a method of separate learning and it is a technique for statistical data analysis used in many fields.