Implementing Machine Learning for our organization, is it important? Check and understand first what Machine Learning is and how its implementation can increase your competitiveness!

Today, artificial intelligence (AI) technology is developing rapidly. Not many people know that artificial intelligence consists of several fields, one of which is Machine Learning. This Machine Learning (ML) technology is one of the most interesting areas of AI, where machines that can learn are like humans.

Machine Learning (ML) is a machine designed to learn on its own without direction from the user. Machine Learning is built on top of other disciplines such as statistics, mathematics, and data mining so that machines can learn by analyzing data without reprogramming.

In this case, Machine Learning has the ability to retrieve existing data with its own commands. ML can also check existing and accepted data to perform a specific task. The tasks that ML can perform are also very diverse, depending on what it learns.

The term Machine Learning was first used in the 1920s by some mathematicians such as Adrien Marie Legendre, Thomas Bayes, and Andrey Markov when explaining the basics of Machine Learning and its concepts. 

A well-known example of an ML application is Deep Blue, developed by IBM in 1996. Deep Blue is Machine Learning designed to learn and play chess. Deep Blue is also tested by playing chess with professional chess masters and Deep Blue winning chess matches against humans.

Today, Machine Learning technology has grown more and more rapidly and helps people in many fields. Even today, ML applications in everyday life are already easy to find. For example, when you unlock your phone using the face scanner feature. Your phone has applied Machine Learning to learn your face. The ads you see on social media are also the result of ML processing that delivers ads according to your personal taste.

How can ML learn? ML can learn and analyze data based on data provided at the beginning of development and when ML is already in use. ML works according to the technique or method used during development. What does the technique look like? Check out the following reviews:

-Supervised learning

Machine Learning to manage information already in data by labeling data with specific labels. It is hoped that this method can provide the output objectives to be  achieved by comparing previous learning experiences.

- Unsupervised learning

Unsupervised learning techniques are usually applied to data that does not contain information that can be applied directly. It is hoped that this technique will help find hidden structures and patterns in unlabeled data. Slightly different from supervised learning, there is no previous data reference.

How Machine Learning works in practice depends on the learning techniques and methods you use in ML. But basically, the principles of how Machine Learning works are the same: data collection, data exploration, model or method selection, training with the selected model, and evaluation of ML results.

Machine Learning continues to learn as long as you continue to use it. Just like Facebook's photo facial recognition feature, it recognizes facial patterns from the tags you entered when posting a photo. ML uses information as a learning medium from people who have tagged photos.

So don't be surprised if Machine Learning is used frequently, its accuracy will be better than the first one. This is because Machine Learning has learned a lot over time from your usage. Similar to Facebook's facial recognition feature, the more people who use this feature to tag people in their photos, the more accurately that person will be recognized.

Are you ready to implement Machine Learning for your business partner? Consult with Telkom DWS!