What does machine learning mean?

The term machine learning (abbreviated ML) refers to the capability of a machine to improve its own performance. It does so by using a statistical model to make decisions and incorporating the result of each new trial into that model. In essence, the machine is programmed to learn through trial and error.

Where did machine learning come from?

The term was conceived in the 1950s—about the same time scientists begin to use artificial intelligence (AI) for the simulation of intelligent behavior in computers. Although contemporaneous, the two technologies are notably different: whereas AI generally refers to the capability of a machine to carry out tasks as a human would, ML specifically denotes a computer application used to process and learn from data, as exhibited in a self-driving car’s ability to detect and process nearby objects. Other common applications of machine learning involve internet search personalization, fraud protection, and identity security—all of which require a machine to learn particular behaviors.



How is machine learning used?

A more agile variety of machine learning, which identifies complex, nonlinear patterns in large data sets and makes it possible to create more accurate risk models nearly in real time, is beginning to be used in these types of applications. You’ve already run across this type of machine learning in other environments, such as your email application’s spam recognition algorithm, Amazon‘s product recommendations and the suggestions you get on Netflix. Now very similar technology is being deployed to combat card fraud.

 

Types of machine learning

  1. Supervised
  2. Unsupervised

 

What is Supervised Machine Learning?

In Supervised learning, you train the machine using data which is well “labeled.” It means some data is already tagged with the correct answer. It can be compared to learning which takes place in the presence of a supervisor or a teacher.

A supervised learning algorithm learns from labeled training data, helps you to predict outcomes for unforeseen data. Successfully building, scaling, and deploying accurate supervised machine learning Data science model takes time and technical expertise from a team of highly skilled data scientists. Moreover, Data scientist must rebuild models to make sure the insights given remains true until its data changes.



What is Unsupervised Learning?

Unsupervised learning is a machine learning technique, where you do not need to supervise the model. Instead, you need to allow the model to work on its own to discover information. It mainly deals with the unlabelled data.

Unsupervised learning algorithms allow you to perform more complex processing tasks compared to supervised learning. Although, unsupervised learning can be more unpredictable compared with other natural learning deep learning and reinforcement learning methods.

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