Machine Learning (ML) definition: How it works, applications & more

Machine Learning (ML) is a subset of Artificial Intelligence (AI) that involves training algorithms to learn from and make decisions based on data. These systems improve their performance over time without being explicitly programmed, adapting to new information and patterns as they’re exposed to more data.

Machine Learning can be categorised into different types based on the learning system and the available data. The technology can be used to sort data, predict future events‌ and detect anomalies.

Machine Learning is already streamlining and personalising aspects of our daily lives.

For example, platforms like Netflix, Amazon and Spotify use Machine Learning to analyse user behaviour and preferences to recommend movies, products or music that you might like. Navigation apps also use Machine Learning to predict future traffic patterns and suggest the fastest routes.

These examples show how Machine Learning is always working behind the scenes as we go about our everyday lives.

This is enabled by algorithms, which are a set of step-by-step instructions that a machine can follow to achieve a certain goal or do a certain task. They're usually split into 3 main categories based on how they analyse data:

1 - Supervised learning (SL)

As the name implies, supervised learning involves algorithms that learn under human guidance or supervision. Supervised learning algorithms, such as linear regression, logistic regression, support vector machines (SVMs) and other classification algorithms, are commonly used for classification problems.

This means that data scientists label each data point with a correct output so the algorithm learns a mapping function. This allows it to predict labels for new, unseen data points. Supervised Machine Learning involves training algorithms on labelled datasets to predict outputs based on the provided training.

Consider a scenario where a data scientist is training a machine to differentiate between the many types of flowers.

Initially, the data scientist would provide some flower images, each labelled with the flower’s name, colour, shape and scent.

The machine then learns to identify each flower type through a process that involves data preprocessing, feature extraction and iterative adjustments to the model’s parameters based on the accuracy of its predictions against known outcomes.

Similar to a teacher-student relationship, data scientists train machines to make correct identifications based on unseen data.

2 - Unsupervised learning (UL)

Unlike supervised learning, where machines are trained to make a prediction or classification, in unsupervised learning, the machine is trained using unlabelled or raw data. Here, it explores insights, structures and patterns without any guidance or supervision.

Using techniques like the following, you can uncover hidden data, gain new insights and make better decisions:

• Clustering - grouping data points based on similarities into clusters
• Association - identifying relationships between variables
• Dimensionality reduction - reducing or removing irrelevant data to create a model with a lower number of variables

Nowadays, businesses collect data from their interactions with customers, such as purchase history, browsing habits and social media activity.

By identifying different groups of customers with similar behaviours or preferences, businesses can easily tailor their marketing strategies to each specific group.

3 - Reinforcement learning (RL)

Reinforcement learning (RL) mimics the trial-and-error learning process that human beings normally undergo. This means that the machine learns through experience and, over time, becomes capable of choosing actions that result in desirable outcomes.

The key components of reinforcement learning are the agent, environment, actions and reward signal:

• Agent - refers to the entity that makes decisions; it learns from the environment by interacting with it
• Environment - is the world through which the agent moves and performs actions
• Actions - the set of possible steps the agent can take in response to the environment
• Reward signal - provides feedback to the agent about the value of its actions, guiding the agent’s learning process by reinforcing behaviours that lead to desired outcomes

Language translation

Machine Learning models, particularly those using deep learning architectures like sequence-to-sequence models, have significantly improved the quality of automated language translation.

These models learn from bilingual text data to provide translations that are accurate and contextually appropriate.

Image recognition

This application is one of the most well-known uses of Machine Learning. Algorithms can identify objects, places, people, writing and actions in images. Image recognition has a range of applications, from security surveillance systems to medical imaging. Automatically tagging friends and places in social media photos is also possible.

Product recommendation

Machine Learning algorithms analyse your past behaviour and the behaviour of similar users to recommend products. This technology powers recommendation systems in ecommerce and streaming services like Amazon and Netflix, enhancing user experience.

Social media features

Machine Learning powers many features in social media platforms, from personalising what you see in your feed, to targeting ads, to detecting and filtering out spam. These algorithms improve user engagement by curating content that's most relevant to each user’s interests.

Chatbots

Machine Learning enables chatbots to understand user inputs and respond in a human-like way. These are used in customer service to provide users with instant responses to enquiries, improving efficiency and customer satisfaction.

Chatbots are becoming increasingly sophisticated, capable of handling complex queries and providing more accurate responses as they learn from past interactions.

Computer vision

Machine Learning algorithms are used to understand and process visual information. They can be used in healthcare, environmental science‌ and augmented reality.

Neural networks

These are used in Deep Learning applications to perform tasks such as image and speech recognition. They’re built and trained using libraries like Keras and TensorFlow.

Data quality and quantity

The performance of Machine Learning models relies on the quality and the volume of the data they're trained on. Inadequate, inaccurate or biased data can lead to models that perform poorly or produce biased outputs.

Lack of training data

In unsupervised machine Machine Learning, the lack of labelled data can make it challenging to discover underlying patterns and structures in the data. Insufficient training data can prevent models from learning effectively, leading to poor generalisation on new, unseen data.

Poor data quality

Issues such as missing values, inconsistent formats and noisy data can lead to inaccurate models that produce unreliable outputs.

Irrelevant features

Including features that don’t contribute to the predictive power of the model can reduce its performance, confuse the learning process and increase training time.

Model explainability and transparency

Many advanced machine learning models, particularly Deep Learning models, are often seen as “black boxes” due to their lack of transparency. This makes it difficult to understand how decisions are made.

Computational costs

Training sophisticated Machine Learning models requires substantial computational resources, which can be costly and energy-intensive.

Generalisation vs overfitting

Achieving a balance where models generalise well to new data rather than memorising their training data is a critical challenge.

Ethical and social implications

The deployment of Machine Learning systems in critical areas raises concerns about issues such as algorithmic bias, privacy and the potential for job displacement.

Security vulnerabilities

Machine Learning systems can be attacked in several ways, including data poisoning and model stealing.

Talent shortage

The complexity of the field and the rapid pace of technological development have led to a shortage of qualified professionals who can develop, deploy and maintain Machine Learning systems.