What is machine learning? A beginner’s guide

It’s a buzzword in technology. You’ve likely heard of it more than once. Seen it in tech news headlines. Found it heavily laced within countless articles, opinions and blog posts. We’re talking about machine learaning.

Thing is, for all its use, it can be hard to pin down exactly what the term machine learning means. Some explanations differ, and some aren’t exactly written in plain English. So, let’s change that.

Here, we answer the question ‘what is machine learning’ and explore why it’s a term on everybody’s lips.

What is machine learning?

Machine learning (ML) is a subdivision of artificial intelligence (AI). It refers to the ability of machines to learn new information and develop problem-solving abilities.

Machine learning uses training data or data input to teach machines how to solve problems, answer questions and draw conclusions from source material. All without the need for human intervention.

ML takes the place of requiring explicit programming for each specific problem or new piece of information. Instead, computers use algorithms to learn how to do new things and make inferences on their own.

And the machine gets smarter over time. You continually feed it with data, and it continually parses, analyses and absorbs. As the computer gains experience, it improves its performance and becomes more intelligent.

So, what is machine learning? In a nutshell, it’s a science focused on teaching computers to act like the human brain by learning autonomously over time.

The types of machine learning

Understanding the answer to ‘what is machine learning’, means understanding how it works. There are two types of machine learning: supervised and unsupervised. Each type requires data input to learn from and uses algorithms to find an answer.

Supervised machine learning is great for recurring problems. The ones that we’ve already solved once, but that come up time and again. Supervised learning requires the potential outputs of the algorithm to already be known.

The training data in supervised learning consists of structured examples. These examples are labelled with the correct answers. With this bank of information, a machine can then learn to apply the steps in the examples to new data input.

Meanwhile, unsupervised machine learning is harder to implement. (And so not as widely used as supervised learning.) However, unsupervised machine learning opens the way to finding solutions to problems that human won’t or can’t tackle.

The training data used in unsupervised learning is not structured or organised for the machine. So, where supervised learning has only important and relevant examples, unsupervised learning leaves the machine to draw its own conclusions from a mess of data.

What is machine learning used for?

Machine learning can theoretically solve any problem in almost any industry. Here are just a few examples.

  • Business

Machine learning poses several uses in businesses. One example is within online customer service. With machine learning algorithms, a machine can learn to identify the sentiment behind the messages a customer sends (a process known as sentiment analysis). Machine learning also sometimes exists in customer service chatbots. Here, a machine learns how best to reply to customer queries.

  • Banking

Machine learning can enable computers to learn to detect potential cases of fraud across many different fields, such as in finance and banking. Plus, the more cases of fraud that are detected, the better the machine gets at identifying other fraudulent transactions.

  • Health

Machine learning is also able to support doctors by assisting with medical diagnosis. Machines can process substantially more information in a small amount of time than humans. So, machine learning allows doctors to harness this processing power to provide earlier, more accurate medical diagnosis.

A beginner’s guide

Of course, this is only a simple overview to the ‘what is machine learning’ question. Machine learning is a vast and intricate field. As a result, books could be (and are) written about its many approaches and applications.

However, if you don’t need to spend months reading research papers, simply think of ML as a branch of artificial intelligence that lets machines learn new skills and solve new problems.

Machine learning is a core part of the future of technology. And one day, machine learning could be assisting us across almost every industry, from healthcare, to business, to farming.