{"id":2833,"date":"2019-01-01T09:20:11","date_gmt":"2019-01-01T09:20:11","guid":{"rendered":"https:\/\/www.kovair.com\/blog\/?p=2833"},"modified":"2019-01-01T09:20:11","modified_gmt":"2019-01-01T09:20:11","slug":"business-dilemma-of-machine-learning","status":"publish","type":"post","link":"https:\/\/www.kovair.com\/blogs\/business-dilemma-of-machine-learning\/","title":{"rendered":"The Business Dilemma of Machine Learning: Should you go the route of Self-Taught Machines?"},"content":{"rendered":"

\"The
\nDue to advancements in technology, machine learning has evolved from being a mere concept in science fiction to something that\u2019s very tangible. Now a widely used process, the medium is being used across a variety of segments to achieve tasks more efficiently.<\/p>\n

Whether you employ smart AI solutions in your enterprise actively or if you use some software that adapts to the needs of your small business, machine learning is at play to help you with your day-to-day processes.<\/p>\n

But not all applications of the technology are alike. To speak of, there are two basic types\u00a0of machine learning \u2014 supervised and unsupervised.<\/p>\n

Users also need to make sure that they are choosing the best fit between these two options to get the most out of these technologies.<\/p>\n

Supervised and Unsupervised Learning \u2013 What Does it Mean?<\/h2>\n

Supervised learning is when you define a set of process labels for your model to learn and process data. It then follows the trail of your provided data set against your pre-defined learning mechanism and provides you the end result with data labeled in a manner as you had taught it to classify initially.<\/p>\n

It is most widely used in classification models, where the input of data has to follow a mapped output. But it can also be used in regression models, where continuous output is expected with mapped input.<\/p>\n

In accordance with its name, supervised learning takes place when a data scientist or an IoT trained person \u201cfeeds\u201d information to the learning model. The information provided acts as an instruction to the data model, which is later modified with continuous supervision. It is similar to teaching students in a university. They are capable of performing complex processes, but they need to be guided first to achieve that feat. The performance is then compared to how effectively they followed the pre-defined learning process.<\/p>\n

Unsupervised learning on the other hand, does not require a data scientist to chaperone the model after its initial development. This is a more abstract but complex version of machine learning, where the model has to identify data, determine characteristics, and process labels on its own. It does not need a set of pre-labeled guidelines and it has to learn to make its own way through the dataset.<\/p>\n

The end result could be provided in a variety of settings, but it is not to be compared to a label. This model is applicable in scenarios where data scientists cannot or do not want to identify pre-defined labels for the output in order to have analyses from a different perspective.<\/p>\n

Unsupervised learning model is machine learning in an advanced form, where the machine teaches itself. The same dataset can be processed in a variety of views<\/u>, including but not limited to anomaly detection, grouping of same objects together (clustering), and identifying similar data objects by association.<\/p>\n

Which is Used More by Businesses?<\/h2>\n

Supervised learning is widely popular among a host of businesses due to the outcome and performance being subject to assessment on the pre-defined labels. The more complex unsupervised learning is applied when a dataset has to be approached via a number of perspectives rather than a pre-defined approach.<\/p>\n

The usefulness of each learning model depends on the type of business and the kind of assistance that you need. A few use cases for each of these learning models are mentioned below:<\/p>\n

Individual Examples of Supervised Learning<\/h2>\n