Machine learning is defined as the science of enabling computers to act and learn like human being do which is done by autonomously improving learning by feeding them with data and information in the form of observations from real life interactions. It is the application of artificial intelligence that provides these computer systems with the ability to learn and pick up information which improves from experience without the need to explicitly program. Simply defined, machine learning is the development of computer programs that access data and consequently use it to learn on their own.

Machine learning enables analysis of massive quantities of data. While it generally delivers faster, more accurate results in order to identify profitable opportunities or dangerous risks, it also requires additional time and resources to train it properly. Combining machine learning with AI and cognitive technologies can make it even more effective in processing large volumes of information.

How Companies Are Using Machine Learning

In today’s business world, there is a rise in big data, therefore companies are using machine learning to tackle areas like:

• Computational Finance for algorithmic trading and credit scoring
• Computer vision and image processing for face recognition, object detection, and motion detection
• Computational biology in medicine for DNA sequencing, tumor detection, and drug discovery
• Automotive manufacturing and aerospace for predictive maintenance
• Natural language processing for voice recognition applications

Common Machine Learning Applications for Businesses

Prominent companies are now relying on machine learning algorithms to understand their customers and make full use of revenue opportunities. The most common machine learning applications are:

1. Customer Lifetime Value Modeling

Customer lifetime value models are among the most important for eCommerce business to employ. That’s because they can be used to identify, understand, and retain your company’s most valuable customers, whether that means the biggest spenders, the most loyal advocates of your brand, or both. These models predict the future revenue that an individual customer will bring to your business in a given period.

2. Customer Segmentation

Rather than relying on a marketer’s intuition to separate customers into groups for campaigns, data scientists can use clustering and classification algorithms to group customers into personas based on specific variations among them. These personas account for customer differences across multiple dimensions such as demographics, browsing behavior, and affinity

3. Churn Modeling

Customer churn modeling can help you identify which of your customers are likely to stop engaging with your business and why. The results of a churn model can range from churn risk scores for individual customers to drivers of churn ranked by importance. These outputs are essential components of an algorithmic retention strategy because they help optimize discount offers, email campaigns, or other targeted marketing initiatives that keep your high-value customers’ buying.

4. Image Classification

Image classification uses machine learning algorithms to assign a label from a fixed set of categories to an image that’s inputted. It has a wide range of business applications including modeling 3D construction plans based on 2D designs, social media photo tagging, informing medical diagnoses, and more. Deep learning methods such as neural networks are often used for image classification because they can most effectively identify relevant features of an image in the presence of potential complications like the variation in the point of view, illumination, scale, or volume of clutter in the image.

In this day and age, machine learning is everywhere you go so even though you do not know that you are using machine learning, you are. After going through this article, you will now know exactly how to use it and use it to the advantage of your business or organization.