Whether or not you have an interest in computing, technology or data science, you will have undoubtedly heard the term “machine learning” in the news or day-to-day conversation throughout recent years.
So, what exactly is it – and how does it affect everyday lives across the globe?
In this article, we’ll explore the world of machine learning, look into some of its applications and consider its future.
Table of Contents
What is machine learning?
In basic terms, “machine learning” describes the collection and assimilation of data by a machine. The data is then used to automatically inform the machine’s future “behavior” in a manner that constitutes artificial intelligence (AI) without requiring a third party to program it.
This is why the terms “machine learning” and “automation” are often used interchangeably.
How is machine learning applied and utilized?
There is currently a huge range of applications possible for machine learning – from the everyday to the extremely specialist.
From search engine algorithms that help to determine the results you see and the order in which they appear to the spam filter in your email, to chatbots on business websites, to personalized content on social media, many areas of the internet are now powered by machine learning.
Even appliances and devices like Roombas and smart speakers make use of machine learning, as do apps that feature facial or voice recognition. Then, of course, there’s the swiftly growing field of self-driving technology for vehicles.
The approach is usually employed to streamline tasks, improve ease of use and personalize specific processes, programs or interfaces.
Developing your knowledge of machine learning
An understanding of the very basics of machine learning is undoubtedly helpful in today’s developing world, as it will help to keep you up to speed with the latest advances and make your adoption of new devices and technologies quicker and easier.
However, gaining in-depth knowledge may prove a brilliant move for your future career– particularly if you’re planning on going into a field such as computer or data science, web development, robotics or any other sector involving machinery or tech.
The extent to which you pursue an education in this field may have a significant impact on your future employability within these disciplines.
Of course, there are innumerable articles and academic papers available online to help introduce you to some fairly advanced information – not to mention plenty of videos and step-by-step walkthrough guides.
However, in order to be right on the cutting edge of new technological approaches such as machine learning and AI, enjoy extensive access to leading academic texts and mentorship, and achieve an exceptionally valuable qualification at the end of it all, you may well decide to pursue a university degree.
Of course, the greater your understanding of machine learning – and the stronger your level of qualification in this field – the more impressive your résumé will look to employers seeking team members to work on new and exciting innovations across the globe.
There are plenty of BSc courses in computing and similar subjects available globally. Once you have suitable credentials from the institution of your choice, you may decide to go even further.
Not only will a Master’s in Computer Science from an institution such as Baylor University enable you to hone your data scientist skills to a world-class level, but it will also make you superbly employable in a number of fascinating and lucrative fields relating to machine learning and AI.
Whatever the extent of your interest in machine learning and whatever you intend to do with it, it will undoubtedly stand you in good stead for years to come, as it seems clear that the future of this exciting resource will be rich and full of opportunity.
What does the future of machine learning look like?
As machine learning already has a huge number of applications and applies to so many different technologies, there is almost no end to where it might go next.
The AI specialists at Dayone Tech predict that quantum computing will continue to play a large role in the furtherment of machine learning.
Quantum computing is a resource whereby various multi-state operations may be run simultaneously – which, in the case of machine learning, means faster data processing and swifter, even more accurate outcomes.
Of course, there is always scope for advances in machine learning’s current applications, from the improved accuracy of online searches to detailed image and voice recognition and personalization. Of course, in the grand scheme of things, it may not be long before self-driving cars outnumber manually operated vehicles.
Not only will developments of this kind make day to day activities more convenient for users in general, but they will also provide vital resources for pressing global causes, including teaching and learning, civil defense, accessibility for people with particular health conditions or impairments and everything between.
Machine learning is, of course, a very complex subject – and this article merely brushes the surface of its significance and intricacies.
It is a rich and fascinating field that offers a whole spectrum of opportunities to change and improve the way we approach the most basic of tasks, and the related sphere of artificial intelligence is no less engaging and promising.
At the time of writing, we can certainly make predictions regarding the directions in which machine learning will move as the coming years roll by – but advances are happening so swiftly and comprehensively that it is almost impossible to foretell where machine learning will take us further down the line.