Machine learning is something that’s being discussed more by companies in a wide selection of industries in recent years. Thanks to the many benefits that it could offer businesses, machine learning is now being considered as a tool to complete procedures that could previously only be conducted by humans. In this article, we’re going to discuss the basics of machine learning and try to answer some of the most commonly asked questions about these developments.
What is machine learning?
Before going any further, it’s important to clarify what exactly machine learning is. Machine learning is essentially a process of teaching computer systems to make accurate predictions with data. These decisions and predictions were previously only made by humans, but now machine learning could be used to answer common queries online or with technology. For example, it can help to decide whether captions could be added to a video online or decipher whether an email is spam or not. The difference between machine learning and regular computer software is that a human isn’t required to write code to help the system make these decisions. Instead, it uses a machine learning model to teach the system to decide between various data points and label data accordingly.
The difference between machine learning and AI?
One of the most commonly asked questions in regards to machine learning is whether it’s just AI under a different name. In fact, machine learning is considered to be a form of AI, and it’s just one solution that’s become incredibly popular in recent years. AI has been around for many decades and is a wider concept that aims to create intelligent machines. AI hopes to replace human thinking eventually and recreate our behaviours in the future, whereas machine learning is simply a subset of AI which allows machines to learn from data without programming.
Machine learning algorithms
While thousands of machine learning algorithms are developed each year, each one will have three main components.
- Firstly, representation ensures that the system knows how to represent knowledge. This could include a set of rules, graphs, or model ensembles.
- Evaluation is then used to evaluate hypotheses and should consider accuracy, likelihood, cost, margin, and other similar elements.
- Optimisation is the final part of the algorithm and is the way in which programs are generated.
All three of these elements are present within the algorithm. From there, you’ll find they can be trained by using one of four popular methods. While supervised and unsupervised machine learning are the most common options today, you’ll find a couple of other solutions are being used more in recent years.
Supervised and unsupervised machine learning
When discussing machine learning, you’ll generally find it’s split into two categories. Supervised learning works to teach machines by example. The system will be exposed to a wide range of data and given many examples of how to distinguish between these data sets. Of course, for this to be successful in the long term, systems may need to be shown millions of pieces of data in order to learn how to complete a task properly in the future.
On the other hand, unsupervised learning works with algorithms that identify patterns within a set of data. It works to spot similarities within data sets and then split them into categories. It doesn’t try and single out a specific result, but it will instead find groups that can be placed together or single pieces of data that stand out from the rest.
An option that’s now becoming increasingly used by companies adopting machine learning is semi-supervised learning. This mixes elements of the two main approaches and relies on a small amount of labelled data and a larger amount of unlabelled data. Through this mixture, the system can be trained, and it will learn how to label the unlabelled data. It can then be trained to complete work based on the newly labelled data and the original labelled data.
Reinforcement learning is another option that companies are using, and it works to offer rewards from certain actions. This is a very ambitious learning style, but it can be an excellent way to improve machine learning.
Who could benefit?
Businesses and industries of all types could benefit from machine learning in the future. While it’s not used throughout every industry yet, there are some areas where it’s regularly being implemented to speed up processes. When searching the internet, you’ll find that search results are often created using machine learning, as they’ll rank pages based on where you are most likely to click. E-commerce sites also use machine learning, and they can detect whether a transaction is fraudulent or not. Social media uses machine learning to find information on relationships and preferences and make suggestions to users on their accounts.
Away from the web, finance companies are using machine data to decide who receives various offers and to establish the risks involved in investments. Robotics commonly use machine learning to understand new environments, such as with self-driving cars. While these may seem like futuristic devices to many people, machine learning makes these vehicles a safe and efficient tool for drivers today. Finally, space exploration uses machine learning for radio astronomy and space probes. As you can see, so many industries and businesses can benefit from this technology, which is why it’s receiving so much attention currently.
Machine learning offers a wide range of benefits to companies and can help ensure you are targeting the right audience and improving the customer experience. You may notice this technology used on apps such as Instagram, Facebook, and Netflix, and it helps to offer you a more intuitive user experience that’s tailored to your preferences.
Regardless of what industry you are in, it’s likely machine learning will play a huge role in future years when it comes to automating processes that would otherwise take up endless hours of work for employees. By understanding the way in which machine learning works and its applications, you can decide where and when it can be used to enhance your work in the future.