Machine Learning (ML): what it is and how it works
Machine learning (ML) is a branch of artificial intelligence (AI) that focuses on building
algorithms and models capable of automatically learning from data. Unlike systems based on
explicit rules, ML methods recognise patterns and relationships within datasets and use that
information to make predictions or decisions about new instances.
The goal is to reduce the need for manual programming: instead of specifying step by step
what to do in every scenario, you give the algorithm solved examples (or a large volume of
raw data) and let it “discover” useful regularities on its own. This approach draws on
statistics, mathematics, and computer science and is crucial in today’s big-data era.
The roots of machine learning lie in several fields: cybernetics, information theory,
statistics, and, of course, artificial intelligence. As early as the 1950s researchers began
experimenting with algorithms that could “learn” from data. A historic example is
Frank Rosenblatt’s perceptron (1957), regarded as one of the first attempts to
emulate the behaviour of biological neurons.
Over the decades ML has seen cycles of intense enthusiasm—such as the expert-systems boom of the
1980s—followed by slowdowns known as “AI winters,” when funding and interest waned. Nevertheless,
the rise of the internet, the advent of faster hardware (notably GPUs), and the availability of
ever-larger datasets have fuelled a renaissance in machine learning, leading to state-of-the-art
techniques such as deep learning.
Theoretical foundations and generalisation
From a theoretical standpoint, machine learning rests on many concepts from mathematics and
statistics. A crucial principle is generalisation: a model trained on data must
learn rules that allow it to make accurate predictions on previously unseen inputs, avoiding
over-fitting.
If the system merely “memorises” its training samples (over-fitting), it cannot adapt to new
situations. To curb over-fitting and improve generalisation, practitioners use several
strategies, including:
Splitting the dataset into training, validation, and
test sets.
Applying regularisation techniques to keep models from becoming overly
complex.
Employing optimisation algorithms such as Stochastic Gradient Descent,
Adam, or RMSProp to iteratively adjust the model’s parameters and minimise
a loss function that measures prediction error.
Types of Learning: Supervised, Unsupervised, Reinforcement
In Machine Learning, three main families are generally distinguished:Supervised learning: the algorithm receives examples made of an input (e.g., an image of a dog) and an expected output (the label “dog”). The task is to find the function that best approximates the relationship between input and output. Classic cases include classification (spam vs. non-spam, disease detection, image categories) and regression (predicting a house price).Unsupervised learning: the data are unlabeled, and the model tries to uncover hidden patterns or internal structure. Techniques such as clustering (e.g., grouping users with similar tastes) or dimensionality reduction (PCA, t-SNE) help explore datasets for correlations and natural splits.Reinforcement learning: inspired by behavioural psychology, an agent interacts with an environment and receives rewards or penalties for its actions. The goal is to maximise cumulative reward, progressively improving its strategy. This approach is widely used in control systems, robotics and games (think of AlphaGo and AlphaZero).
Data Quality and the “Garbage In, Garbage Out” Principle
A decisive factor in Machine Learning is the quality of the data used to train models. Even before designing complex neural networks or sophisticated algorithms, curating and preparing the dataset is one of the most labor-intensive tasks. It is necessary to:
Clean the data to remove outliers or noisy records.
Handle missing values, for example by deleting incomplete rows or replacing them with plausible estimates.
Apply transformations or normalisation to make the data more homogeneous and suitable for the algorithm.
If necessary, engineer new features by combining existing variables or introducing external information.
The saying “garbage in, garbage out” highlights that if the data are unrepresentative or contain errors, even the most sophisticated model will deliver unreliable results.
Real-world applications and impact sectors
Machine learning now underpins many of the services and products we use every day:
Natural-language processing (NLP): machine translation, speech synthesis and recognition, chat-bots and virtual assistants, sentiment analysis.
Finance and insurance: credit-risk assessment, algorithmic trading, fraud detection in transactions.
Recommender systems: e-commerce and streaming platforms (Amazon, Netflix, Spotify) that suggest products or content tailored to the user.
Marketing and advertising: user profiling, ad-campaign targeting, trend and consumer-preference analysis.
In each of these areas ML automates processes, reduces costs, and improves prediction accuracy, making the management of large data volumes more efficient.
Deep learning and deep neural networks
In recent years the most spectacular progress has come from deep learning, a subset of ML that relies heavily on deep neural networks. These networks, built from many layers of artificial neurons, can extract hierarchical representations of data. For example, in a convolutional neural network (CNN) for image recognition, early layers detect basic edges and shapes, while deeper layers combine them to identify ever more complex objects.A major advantage of deep learning is that it reduces the need for manual feature engineering, because the models learn on their own which characteristics matter. However, deep learning often requires large data volumes and high computational resources, which can limit adoption in some contexts.
Limits and open challenges
Despite its power, machine learning poses several important challenges:
Interpretability (black-box problem): models—especially deep networks—are complex and hard to explain. Sensitive fields (healthcare, justice, finance) demand greater transparency about how decisions are made.
Bias and discrimination: if the data reflect social biases, the model inherits them, producing unfair or discriminatory results. Addressing this requires careful dataset curation and the development of fair algorithms.
Data-hungry algorithms: many modern methods need very large datasets. Where labelled data are scarce, training effective models becomes difficult.
Model maintenance: when the underlying data distribution changes over time (concept drift), models must be retrained or updated to stay accurate.
Research into few-shot learning tackles the shortage of labelled data, while transfer learning reuses knowledge learned in one domain for another. The field of Explainable AI (XAI) studies techniques that make models less opaque and more understandable.
The Future of Machine Learning
The evolution of Machine Learning is tightly linked to rising computational power and the availability of richer, more complex data. At the same time, ML is increasingly integrated with other fields such as robotics, computational biology, precision medicine and genomics. Even today, ML algorithms help discover new drugs, analyse genetic sequences, drive autonomous vehicles and optimise production processes.Moreover, the advent of specialised hardware (GPUs, TPUs, neuromorphic chips) and new computing paradigms (quantum, edge computing) could make ML models even more efficient and versatile. From a privacy and data-security perspective, federated learning promises to train models directly on users’ devices without centralising information, thus giving greater control over confidentiality.
Conclusions
In short, Machine Learning is a cornerstone of modern artificial intelligence: it turns data into knowledge to predict phenomena, support decision-making and automate complex tasks. Its strength lies in adapting to diverse contexts and improving performance as data volumes and computing power increase.Nonetheless, questions remain about transparency, fairness and ethical use, especially when algorithmic decisions affect millions of people. Ongoing research, advancing enabling technologies and ethical-regulatory reflection will keep shaping ML’s profound impact on society, the economy and our future.
FAQ
What is meant by machine learning?
Machine learning (ML) is a branch of artificial intelligence that lets systems “learn” from examples—that is, detect patterns in data—and improve their predictions or decisions without hard-coding rules for every specific case.
Is ChatGPT AI or ML?
ChatGPT is an artificial-intelligence application built on a language model trained with machine-learning techniques. In other words, it uses ML to learn, but the end product falls under the broader AI umbrella.
What are the four fundamental pillars of machine learning?
1) High-quality data; 2) Representation/feature engineering (how data are “described” to the model); 3) Algorithm / model (e.g., decision trees, neural networks); 4) Training and evaluation procedure, including the objective function to optimise.
What’s the difference between AI and machine learning?
AI is the general goal of getting computers to perform tasks that require human intelligence (reasoning, perception, interaction). Machine learning is a sub-discipline that reaches that goal by training mathematical models on data instead of hand-written rules.
What is the best language for machine learning?
Python is the de-facto standard thanks to mature libraries such as NumPy, pandas, scikit-learn, PyTorch and TensorFlow. R excels at statistical analysis, Julia targets native performance, while C++ or Java may be used in high-scale production.
What are the five types of machine learning?
Taxonomies vary, but the five most cited paradigms are: 1) Supervised learning; 2) Unsupervised learning; 3) Semi-supervised learning; 4) Reinforcement learning; 5) Self-supervised learning (or auto-encoding), increasingly common in generative models.
What is NLP in machine learning?
Natural Language Processing (NLP) is the ML/AI field that enables computers to understand and generate human language: text analysis, translation, sentiment analysis, chatbots, and so on.
What is AI in simple words?
AI is the ability of software to carry out tasks that normally require human intelligence—recognising images, speaking, planning, problem-solving—by learning from experience or applying smart rules.
What’s the difference between machine learning and deep learning?
Deep learning is a subset of machine learning that uses deep neural networks (many layers) to learn hierarchical data representations. All deep learning is ML, but not all ML relies on deep networks.
Author
Nicolò Caiti
I’ve made MarTech my career, focusing on artificial intelligence for digital marketing. In this blog I analyse how AI is transforming the sector—improving web performance, optimising digital strategies and speeding up everyone’s work. With years of experience in marketing automation and advanced customer-journey management, I share practical insights, case studies and best practices to help people harness AI’s potential in their roles.I hope you find the answers you’re looking for!
