Machine learning has populated the technological sphere and within AI communities as it continues to grow in importance in managing high volumes of data. Related to AI, machine learning was developed as a learning mechanism that imitates the human mind—data and algorithms improve machines with time—gradually allowing the technology to become experts in their domain.
Whilst machine learning held little water a few decades ago, it’s now growing part of an increasingly important AI function: big data. As organisations continue to rely on technology, they consequently handle larger volumes of data, and with-it need to make effective business decisions. Data science, a growing IT job in Malta and internationally, is a particular role that manages such data quantities. Data scientists apply rigorous statistical approaches to data and algorithms and train these as they develop. The result is oftentimes impressive, whereby insights produced from such data provides for impactful organisational results.
A simple explanation of machine learning
Algorithms constructed by machine learning find patterns in data. Data itself can own several meanings, including numerical data, words, images, and so forth. Its digital storage allows for algorithms to function, processing a lot of applications we use today. Large systems we’re all familiar with such as Google search engines and recommendations on YouTube all function through machine learning, and our consistent use of these platforms allows these systems to grow smarter.
Whatever platforms and systems we use that learns from our actions continuously collects data on our behalf. Machine learning feeds on user actions that factors into their algorithms. This can mean what we click on is important, what we react to, videos we like to watch—all contribute to a more powerful algorithm. This is because in turn, we receive personalised recommendations based on our actions. Machine learning algorithms, with your data, make educated assumptions of our interests.
Machine learning, deep learning, and neural networks
Like many AI technologies that grow rapidly, we need to remain up to date with current terminologies that best represents what we’re seeing in these IT jobs. It also helps teams made up of software developers, for example, communicate more effectively using the correct jargon. When exploring the topic of machine learning, you cannot escape learning its related counterparts: deep learning and neural networks. Essentially, all three elements are fields of AI technologies, but deep learning is a sub-field of machine learning, and neural networks is a sub-field of deep learning.
The key differences that lie between these technologies is how the algorithm learns. Deep learning is able to manage higher volumes of data than machine learning, as it operates largely by automation. Where traditional machine learning requires human intervention to some extent in generating data, deep learning does not. This is because in some IT jobs in Malta and abroad, humans input information that teaches data to learn initially and manages its structure throughout.
Contrarily, deep learning doesn’t require humans to process its data, making it a more sophisticated and scalable option for numerous use cases. As neural networks form a sub-field of deep learning, it boasts the same advantage in not needing human intervention. Deep learning and neural networks are often credited as the main advancers of AI technologies for being able to speed up system processing. Common examples that reap the benefits of such advancements include natural language processing and speech recognition software, among others.
Lastly, neural networks are made up of three main layers: the input layer, hidden layers, and an output layer. Together, these make up a node layer, which can be numerous to form a network. Like neurons in the brain, these nodes connect and communicate according to certain inputs of data, where these connections continue to assimilate across neural networks.
Deep learning suggests a high volume of these networks exist and grow more sophisticated with little time. Machine learning is transferred to deep learning when more than three layers in a system are involved, as these create larger networks by nature. Less than three layers comprise a basic neural network, and is not considered a deep learning algorithm.
How machines learn
So how does machine learning actually work in practice? Machines learn in four ways: supervised, unsupervised, semi-supervised, and reinforcement learning. Supervised learning makes up the bulk of machine learning and perhaps is the most simplistic form of learning: data is labelled to inform machines what patterns they should scout for. This is how most recommendations systems work, in establishing patterns of video content you enjoy watching for example, machines are able to construct well-versed guesses for the following content you may be interested in.
For unsupervised learning, data is not labelled, so machines explore any patterns that arise in an algorithm. Their poorer sophistication over supervised learning mechanisms leaves this kind of learning less favourable among software developer jobs, as it means there are lesser applications for it. That being said, unsupervised learning is growing in interest for the field of cybersecurity.
Between supervised and unsupervised learning lies semi-supervised learning, as it makes use of both labelled and unlabelled data. In training, machines use small sets of labelled data for classification purposes, but then uses larger unlabelled data sets for feature extraction. This form of learning is namely used for circumstances where there isn’t enough labelled data to inform and train a supervised learning algorithm.
Lastly, reinforcement learning works by trial-and-error principles in order to complete an assigned objective. It works through a reward system in that machines receive either rewards or penalties during its learning. This means that a machine continues to learn until it reaches its objective. In this way, its learning behaviour is binary: it either helps reach an objective or hinders its progress.
How machines work
Whilst machine learning can grow into a complicated process, its main learning system can be divided into three main steps:
Making decisions: with machine learning, algorithms make decisions informed by labelled or unlabelled input data. They make predictions and classifications that results in an educated data pattern.
Error functions: these assist the prediction process of an algorithm model. As machines learn and grow their data, error functions can compare between previously made decisions to improve future outcomes.
Optimisation: data models in training improve as they connect data points, so a model’s structure can adjust to accommodate the discrepancy between known examples and model estimations. This will inform the algorithm to evaluate its operations to optimise effectively in performing more accurately.
Whilst this particular article serves to introduce and explore the workings of machine learning, further discussions in IT jobs in Malta and elsewhere revolve around its numerous applications and subsequent challenges. As technology continues to advance, so too does our emerging concerns and potential arising solutions. Remain up to date with our blog's page to learn more about emerging technologies and AI, and look out for an upcoming article that details common challenges of machine learning found today.
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