Machine learning

Often, machine learning and predictive analytics are mistaken or interchanged for other. Yet they are not quite the same. Here is why.

Artificial Intelligence (AI) has been trending in headlines for quite some time for all exciting reasons. While it is not a new buzzword in the technical nor business world, it is successfully transforming industries around the globe. To date, enterprises, firms and start-ups are racing to adopt AI in their business culture. This emerging technology has blessed us with improved computing and analysis of data, cloud-based services and many more. The applications are so vast that, business leaders might find themselves caught up in confusion on what to implement for their business practices and get maximized ROI.

Well, as per the most preferred options, machine learning and predictive analytics are used to cater to such needs. Thanks to them, companies can extract relevant insights about their clients, market and businesses with a fraction of operational costs. Although they are both centered on effectual data processing, machine learning (ML) and predictive analytics are sometimes used interchangeably. Predictive analysis works on the lines of machine learning, yet they are different terms with varied potential.


What is Machine Learning?

Machine Learning is an AI methodology where algorithms are given data and asked to process it without predetermined rules. This allows the machine learning models to make assumptions, test them and learn autonomously, without being explicitly programmed. It is accomplished by feeding the model with data and information in the form of observations and real-world interactions. E.g. Machine learning is used for understanding the difference between spam, malicious comments, and positive comments on Reddit by studying a given set data of comments existing on the social community discussion page.

There are two types of machine learning: supervised and unsupervised.

Supervised or Assisted machine learning requires an operator to feed pre-defined patterns, known behaviors, and inputs from human operators to help models learn more accurately. It helps the machine model comprehend the kind of output desired and allows the operator to gain control of the process. On the other hand, unsupervised or unassisted machine learning depends on the machine’s ability to identify those patterns and behaviors from data streams as no training data is provided. One instance of its application is employing it for intelligent profiling to find parallels between a restaurant chain’s most valuable customers.


What is Predictive Learning?

Predictive Analytics, whereas, refers to the process of analyzing historical data,as well as existing external data to find patterns and behaviors. Although an advanced form of AI analytics, it existed much before the birth of AI. Mathematician, Alan Turing harnessed it to decode encrypted German messages (Enigma Code) during World War II.

It also automates forecasting with substantial accuracy so that business firms can focus on other crucial daily tasks. However, since the patterns remain the same in most cases, predictive analytics is more static and less adaptive than machine learning. Therefore, any change to the analysis model or parameters must be done manually by data scientists. Its common adopters are banks and Fintech industries. There these analytics tools are used to detect and reduce fraud, determine market risk, identify prospects, and more.


Application in businesses

One cannot possibly decide which of the two is the better option for business; as their use cases are not the same. For example, one of the business applications of machine learning is to measure real-time employee satisfaction while predictive analytics is better suited for fields like marketing campaign optimization. Strategies based on predictive analysis can empower brands to identify, engage, and secure suitable markets for their services and products, and boost efficiency and ROI of marketing campaigns. This is possible as here analysis is focused on data streams that require specific pre-defined parameters. The software can display foresight on KPIs, which includes revenue, churn rate, conversion rate, and other metrics.

As mentioned earlier, it is an indispensable asset in Fintech and banking sectors. It is also used to gain insight into their customers’ buying habits.

Machine learning is competent in scanning business assets to locate security risks and origins of possible threats, thereby playing a significant role in cyber-security. They further help in increasing the value of user-generated content (UGC) by skimming out the bad, spamming, and hate content. Also, by observing and understanding customer behavior, it can determine the success of an advertisement’s performance and speed up product discovery.



Apart from their apparent difference, both these branches of AI hold immense and impressive possibilities. They can be adjusted to match a project’s scale, and accordingly include tools that align most in achieving the project goals.Companies must act quickly, lest they risk being trampled by their rivals who have already implemented them. Also, it is important to remember that all predictive analytics methods are not part of machine learning.