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What is machine learning? – A beginner’s guide

What is machine learning, and why is it so useful? We take a detailed look at what this technology is used for, the kinds of jobs that use it, and the skills you’ll need to get started with it.

What is machine learning header

Machine learning is an increasingly common concept. Along with terms like deep learning and artificial intelligence, it’s a buzz word that finds its way into science and technology news. But what is machine learning? And what is it used for? We take a look at everything you need to know about the basics of this fascinating technology. 

As well as exploring a machine learning definition, we’ll also look at some of the different types currently in use and what their applications are. We’ll also look at some of the careers that use machine learning and some of the skills you’ll need to get started. 

Machine learning definition 

Let’s start at the very beginning with a definition of machine learning. If you quickly search for the phrase, ‘what is machine learning?’ you’ll get a whole host of different results and definitions. From the simple to the complex, there are many ways to define machine learning. This is a testament to how much of a unique, broad, and technical concept it is. 

What’s more, there are many other associated terms that you need to grasp to understand the core of machine learning. We’ve outlined some key definitions below:

Machine learning 

We can think of machine learning as the science of getting computers to learn automatically. It’s a form of artificial intelligence (AI) that allows computers to act like humans, and improve their learning as they encounter more data. 

With machine learning, computers can learn to make decisions and predictions without being directly programmed to do so. The process uses algorithms to build models that can then be applied to a whole host of different purposes. 


In the simplest terms, an algorithm is a set of instructions that a computer needs to follow to complete a particular task. In relation to machine learning, algorithms analyse input data to predict output values within an acceptable range

As these algorithms receive new data, they ‘learn’ to optimise their processes, meaning they improve performance and become more intelligent. As we’ll see, there are four main types used in machine learning: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.

Artificial intelligence 

Artificial intelligence (AI) is a branch of computer science that focuses on developing computers and machines that can perform tasks that usually require human intelligence. Such software systems operate in an intentional, intelligent, and adaptive manner.

AI systems often use real-time data and inputs to respond to situations and make decisions. They can analyse huge amounts of information in very short spaces of time. Machine learning is just one of the subsets of artificial intelligence. 

Deep learning 

Deep learning is a field of machine learning. It focuses on creating algorithms that are inspired by the brain. These artificial neural networks, as they’re known, are based on the structure and function of the brain. 

In the same way that humans learn from experience, deep learning models also repeatedly adjust their performance to make improvements. This type of machine learning is generally used for tasks that require some form of thought. We’ll explore deep learning vs machine learning in a separate article.

The basics of machine learning 

Now that we have a machine learning definition in place, let’s look at some of the very basics of this fascinating field. To keep things accessible for all, we’ll not delve too deeply into the mechanics behind the concept. However, we will include useful links and courses to more detailed reading where appropriate. 

A good place to start is with an explanation of how machine learning algorithms work. There is a fairly famous quote that does exactly that. It comes from Tom Mitchell, an American professor and expert in machine learning. He explains the machine learning meaning in the following terms: 

‘A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.’

Let’s break that down a little more with an example. Let’s say you want a machine learning program to predict weather patterns in a particular area (task T). You can feed past weather pattern data (experience E) through your algorithm. If your algorithm is successful in learning, it will be able to more accurately predict weather patterns (performance measure P). 

Of course, there isn’t just one type of algorithm you can use to apply to everything. In reality, there are thousands of highly specialised tools and programs developed for all kinds of real-world problems. 

The main types of machine learning algorithms 

Now that we’ve answered ‘what is machine learning?’ in basic terms, let’s take a look at some of the different types of machine learning algorithms. As we’ve mentioned, there is a whole host of different ones out there. Here, we’ll cover some of the machine learning basics. 

Many problems that ML sets out to solve require a bespoke approach. As such, the types of instructions needed for each will be very different. However, there are generally four main categories that these algorithms fall into: 

Supervised learning 

With this method of machine learning, you train the algorithm using a labelled set of data to learn from. So, there are already some known answers, and it can determine whether new data matches it. As it produces results, it can evaluate them based on information you’ve already provided. The more data you give it initially, the more it knows about unseen data. 

Unsupervised learning 

In this type of machine learning algorithm, the programme is trained with data that isn’t labelled. It doesn’t know what the data represents. Instead, the computer detects patterns, finds rules within it, and summarises where there are relationships in the data. 

Semi-supervised learning

As you might expect, this type of algorithm uses elements of both of the above. The data you provide to teach the machine will have some labels, which is used to help process larger sets of unlabelled data. 

Reinforcement learning 

This method of machine learning is focused on continuous learning and reward using unlabelled data. A useful way of thinking about this concept is with video games. If a computer wins a game, it receives positive feedback.

It can then continue refining the moves it takes to win the game to become more effective. Often, this means replaying it many thousands or millions of times and getting feedback on each. 

What is machine learning used for? 

You probably come into contact with machine learning algorithms on a daily basis without realising it. What’s more, we’ve only just started to scratch the surface of what machine learning and deep learning can do.

If you’re wondering what machine learning is used for, we’ve highlighted just a few the creative ways you might encounter the technology: 

  • Automation. Perhaps the most high-profile machine learning use is in the automation of tasks humans usually perform. The ability for a computer to think and act without being programmed has incredible potential. 
  • Recommendations. Based on previous input data, machine learning can recommend products and services that users or customers might like. This is perhaps one of the most common forms of machine learning you’ll see in your day-to-day life.  
  • Insights. Machine learning algorithms can process and analyse huge sets of data. Often used in the field of big data, such insights can help businesses understand their customers and healthcare professionals understand their patients. 
  • Detection. The way that machine learning works makes it ideal for spotting anomalies in patterns. As algorithms learn what ‘normal’ is, they become more adept at detecting when things go wrong. 

These are just a small sample of the types of areas where machine learning is being used. Excitingly, it’s a field that’s still relatively young. As computing power increases and algorithms become more complex, we’ll see many more uses for machine learning. 

Examples of machine learning 

To give a clearer picture of how machine learning is being used today, let’s explore some real-life instances of the technology at work. Some of these machine learning examples are ones you may have encountered directly, while others may impact you in ways you’ve never noticed. 

Search engines

Search engines like Google use machine learning in a variety of different ways. By watching how users respond to the results displayed when you make a search, algorithms can refine which pages are displayed. The Google RankBrain algorithm assesses what users might be looking for when they make a search. 

Understanding this type of algorithm plays an essential part in things like Search Engine Optimisation (SEO) and other forms of digital marketing. It also means you get useful, relevant and high-quality results when you search online.  

Speech recognition

Virtual personal assistants have been around for a while now. With services like Siri, Alexa, and Google Now, you can ask questions, set reminders, and even control various elements of your home. All of these use speech recognition and language analysis powered by machine learning. 

By using deep learning algorithms and neural networks, these digital assistants can perform a whole host of functions. Often, the more data they gather from people speaking, the more accurate they become. 

Fraud detection 

As many of our financial services move to digital platforms, the risk of fraud and scams increases. To combat such issues, machine learning algorithms have been devised. These programs work on large data sets to find correlations in user behaviour that could lead to fraud. They look at wide-scale patterns to identify anything out of the ordinary. 

A good example of machine learning at work in reducing fraud is Danske Bank. Previously, their labour-intensive means of examining fraud created 1,200 false positives a day. They were only detecting 40% of fraud. By introducing a deep learning solution, they saved time, reduced false positives by 60%, and increased true positives by 50%. 

Medical diagnosis 

Another field that is producing massive amounts of data is that of healthcare. Individual patients, as well as groups of people, are creating information about diagnostics, treatments, and conditions. These big data sets can help build predictive models on a range of illnesses and their treatments. 

IBM’s Watson for Genomics, for example, uses AI and ML to allow clinicians to provide personalised care to cancer patients. This type of precise approach to medicine can mean more effective treatments for more people. As far as machine learning applications go, this is one of the most valuable. 

Customer support

Compared to some of the other uses for machine learning on the list, this might seem a little mundane. However, it’s still a great example of ML in action. By using algorithms to assess interactions between customers and companies, it’s possible to create things like chatbots and virtual assistants. 

These services respond to queries and simulate real conversations, improving customer experience. They can help to ensure clients receive the help they need while saving organisations time and money. Plus, the more data the chatbot or assistant receives, the more accurately it can help customers. 

What types of careers and jobs use ML? 

If you’re intrigued by what you’ve read about the subject, you might wonder what jobs you can get with machine learning knowledge. As you might expect given the scope of the field, there are many opportunities out there. We’ve picked out a few notable ones below. 

Machine learning engineer

As you might expect, machine learning engineers are at the heart of this fascinating field. They’re the people who create the algorithms and programs that allow computers to learn. If you’re wondering how to become a machine learning engineer, you can find more info on our machine learning career advice page. 

Data scientist

The field of data science focuses on discovering and exploring patterns within data. This insight is then used to help businesses and organisations make decisions and overcome obstacles. Central to the role of data scientist is an understanding of machine learning algorithms. They are used to process large amounts of data and draw conclusions. 

Software developer 

Many of the concepts of machine learning also apply to the role of software developer or software engineer. Both positions focus on using programming languages and creating models and algorithms to solve problems. An understanding of machine learning can certainly help with software development. 

Business intelligence analyst 

Machine learning and artificial intelligence can (and likely will) play a significant part in business intelligence (BI). As a role focused on understanding patterns, anomalies, and opportunities, BI analysts can use ML to gain real-time insights. They can also produce more accurate forecasts that improve automatically as they assess more data. 

What skills do I need to get started with machine learning? 

If you’re interested in a career in machine learning, you might be wondering about some of the skills and knowledge you’ll need to get started. Usually, you’ll need at least a bachelor’s degree and some relevant experience to secure a machine learning job. However, there are many skills you can work on to start building your knowledge. 

We’ve picked out a selection of hard and soft skills that can help you get into machine learning. These are the types of expertise that employers would expect to see from anyone wanting to work in the industry. 

Computer science and programming 

Top of the list of skills needed for machine learning is programming and computer science. As well as understanding how algorithms work and how to create them, you’ll also want to know a few programming languages. For machine learning, Python is a helpful place to start, and languages like R, Java, and C++ are useful. 

Maths and statistics 

Many machine learning models are based on probability and statistics. Having an understanding of these concepts is essential as you learn about the applications of ML. Similarly, you’ll need a high level of mathematical skill to work with complex algorithms. 

Data modelling and analysis 

A central part of many machine learning jobs is data analysis. Being able to model and evaluate large sets of information is vital. As we saw in our machine learning and deep learning definition, data is at the heart of creating and improving ML algorithms. 


Like many similar emerging technologies, the machine learning industry is rapidly changing. Being able to adapt with these changes is essential if you want to work in the sector. What’s more, individual roles are likely to be quite dynamic, meaning you’ll have to think on your feet to assess new situations. 


Whether you’re collaborating with people from different disciplines and backgrounds or explaining your findings to non-experts, communication skills are machine learning essentials. You’ll need to understand and be understood, particularly in the often fast-paced environments you’ll be working in. 


Ultimately, machine learning is about solving problems, whether directly or indirectly. Knowing the right problems to solve and taking a methodical and considered approach are highly valuable assets for your machine learning career. 

Getting started with machine learning 

If you want to learn machine learning and some of its core principles, there are various ways you can get started. We’ve picked out some of the resources we have that can improve your knowledge in a variety of relevant areas. As well as some machine learning courses for beginners, we also have some more in-depth learning opportunities. 

Final thoughts 

So, what is machine learning? Clearly, it’s a fascinating, valuable, and developing field of technology. By teaching computers to learn and improve by themselves, we can continue to expand the possibilities offered by artificial intelligence and deep learning. 

If you’re thinking about learning machine learning, there are several ways you can build your knowledge and experience. By working on various hard and soft skills, you can work your way to developing your expertise.

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