How machine learning is different from conventional programming language?

The term machine learning is not new and it has even become a buzzword for modern technology. On a daily basis, we’re all using machine learning from simple Google maps and Google assistants to complex self-driving cars and automatic language translation. This modern programming approach has revolutionized almost every sector including IT, finance, cybersecurity, and business.

Although both machine learning and conventional programming language are separate categories under the programming language category. Conventional programming language on the other hand has been around for quite some time. 

Machine learning and conventional programming language are two different approaches to computer programming that yields different outcomes or expectations. 

By definition, Machine Learning is a field of computer science that gives computers the ability to learn without being explicitly programmed. Machine learning teaches computers the ability to solve problems and perform complex tasks on their own. In most situations, problems solved using machine learning are based on the computer’s learning experience for which they wouldn’t have been solved by conventional programming languages. Such problems can be face recognition, driving, and diseases diagnosis. With conventional programming language, on the other hand, the behavior of the computer is coded by first creating a suitable algorithm that follows predesigned sets of rules.

Also, machine learning depends on real-life data-driven approaches that involve learning and modeling while conventional programming relies on programming logic. In other words, machine learning depends on a rather different form of augmented analytics where input and output data are fed into algorithms. The algorithms then create the program. On the contrary, conventional programming languages involve manually creating programs by providing input data. The computer then generates an output based on programming logic. For instance, you can easily predict consumer behavior through trained machine learning algorithms.

Another major difference between machine learning and conventional programming language is the accuracy of predictions. Conventional programming language depends on algorithms within a variety of input parameters. Machine learning on the other hand collects data based on past events (historical data) which build a model that is capable of adapting independently to new sets of data to produce reliable and repeatable results. This kind of self-learning models cannot be built with conventional programming languages. This is where machine learning becomes important for business owners. Machine learning allows you to effectively utilize your data to streamline business operations, reduce error, and improve accuracy. With machine learning, you can easily breakdown the silo and coordinate your workflow through greater efficiency, more synchronization, and less tunnel vision provided by machine learning. 

Additionally, with conventional programming languages, programmers are restricted within the parameters they can think of. Hence, accurate predictions depend on the thousands of parameters they can add and which must also be done with high accuracy. It is near impossible to build algorithms that use all parameters in a reasonable way and to the highest level of accuracy. However, with machine learning, there are no restrictions on the number of data sets and models that can be generated since the built models are capable of learning independently. As long as you have enough processor power and memory, you can use as many input parameters and data sets as you see fit and you would generate reliable and repeatable outputs.

At Charter Global, we help organizations gain better control of their consumer data with machine learning so they can market their products smarter and scale faster than their competition.