How machine learning can support data analytics endeavours?

For nearly a decade, data has been the primary resource for companies to gain a competitive edge in the fiercely merciless marketplace. It is very important to be right the first time, because in today’s market scenario second chances are very rare. Hence the urgency to integrate analytical models in the business framework. Businesses are using data to understand their customers better, which in turn helps them create better marketing strategies. A data driven approach helps a company personalize their services towards the clients so as to keep them loyal. These things are achieved through the methodical analyses of data and by using the results to make better business decisions. Machine learning and artificial intelligence are fields that have kept growing through the economic downfall. So, if you want to be a part of it, start by finding the best machine learning course for yourself.

Where does machine learning enter?

The amount of data that companies work today is enormous. It is difficult to create analytical models that can analyze the data in a certain way that will find answers to specific questions. This is where machine learning comes into play. Machine learning programs can learn from data without much human participation. Therefore machine learning algorithms can be deployed to sift through huge data sets. These algorithms can be used to recognize patterns in the data. Moreover machine learning can be used to develop analytical models based on certain problems.

Supervised and unsupervised learning

The basic principle of machine learning is quite simple. A machine is trained to recognize data and organize it according to patterns. The training process can be supervised or unsupervised. Let us see what that means.

Supervised learning

A machine learning programme is trained with labeled data in this format. For instance, in case of an image recognition system the program will be trained with labeled images. So that when the algorithm is exposed to various images it can recognize the images that are similar to the training data and categorize them as such.

Unsupervised learning

In this case the machine is not trained with labeled or marked data. It is just exposed with a lot of data and the programme recognizes data with similar features. So, with this approach, in the case of an image recognition system the machine will not be able to categorize a certain kind of image. However, it will be able to recognize all the images with similar features and put them in one box.

Analytics and machine learning

The description of two popular forms of machine learning should show the picture already. If a machine learning algorithm can recognize patterns in data be it structured or unstructured, half the job is done. In a business that deals with a huge amount of consumer data, machine learning algorithms can be applied for both predictive and prescriptive analysis. It can help a company categorize the potential leads; recognize hash falls in product design; or create a marketing strategy that engages the widest range of potential consumers. With machine learning the enormous amount of data becomes an advantage.