What is machine learning? Definition and examples

Machine Learning image 1
Machine learning focuses on the development of software technology. The software teaches machines to improve on their own when exposed to new data.

Machine learning is an artificial intelligence application that gives ‘smart’ machines the ability to learn and improve automatically. They improve from experience, even though computer scientists had not programmed them explicitly for certain tasks.

The term is all about developing software technology that lets machines access data and then use it to learn by themselves. In other words, learn without human intervention.

Humans have the ability to learn by experience. Machines with artificial intelligence can do the same.

Machine learning is also the scientific study of statistical models and algorithms that machines use to carry out a task effectively without receiving explicit instructions. They rely on inference and patterns instead.

Emerj.com has the following definition of the term:

“Machine Learning is the science of getting computers to learn and act like humans do, and improve their learning over time in autonomous fashion, by feeding them data and information in the form of observations and real-world interactions.”

A neural network – a set of algorithms that has been modeled after the human brain, is an example of machine learning.

What are AI and machine learning?

AI stands for artificial intelligence. AI includes software technologies that make machines such as computers and robots think like us (humans). It also makes them behave like us.

Humans have natural intelligence. ‘Smart’ machines, on the other hand, have artificial intelligence.

Artificial intelligence is rapidly becoming more common in our homes, business, and production processes.

We often mention the ability to learn without human help when we describe AI. We are referring to machine learning, which is part of AI.

Machine and human learning – simple example

If we see a pattern, we can sometimes use our intelligence to make conclusions. For example, look at this quiz:

  • 2 – 20
  • 5 – 50
  • 10 – 100
  • 100 – 1,000
  • 500 – ?

After seeing a pattern, i.e., each time we multiply the first number by 10, we come to the answer ‘5,000.’ With machine learning, we are trying to teach machines that kind of behavior. In other words, to learn from experience.

According to Becoming Human – Artificial Intelligence Magazine:

“Machine learning algorithms use computational methods to ‘learn’ information directly from data without relying on a predetermined equation as a model.”

“The algorithms adaptively improve their performance as the number of samples available for learning increases.”

A team process

According to American multinational IBM, machine learning allows us to learn continually from data. This helps us, for example, to predict the future.

Powerful sets of models and algorithms are being used across most industries. They improve processes and help us gain insights into patterns and anomalies within data.

However, it is not a solitary endeavor. It is a team process that requires business analysts, data scientists, and data engineers. It also requires business leaders. They all work together.

IBM says:

“The power of machine learning requires a collaboration, so the focus is on solving business problems.”

Video – Machine Learning in 5 Minutes