Definition
Machine
Learning, usually referred to as ML, is a branch of artificial intelligence
(AI) that centers on developing computer algorithms that learn and improve from
data and experience. In other words, machine learning helps computers to learn
from data and make decisions or predictions on their own without any
programming.
Unlike in traditional programming where a computer is provided step-by-step instruction to perform a task, in machine learning, the computer is shown data examples and allowed to figure out how to accomplish the task based on those examples [1].
For example, if we want a computer to recognize which language someone is speaking, we don't provide it with specific instructions on what Yoruba or Igbo sounds like. Rather, we feed it thousands of audio recordings of people speaking Hausa, Yoruba, Igbo, Pidgin, and other languages and let the machine learning algorithm figure out the common patterns in pronunciation, tone, and vocabulary that distinguish each language. With time, as the algorithm analyzes more speech samples, it becomes better at language identification, even when speakers use unfamiliar regional accents it has never heard before.
Origin
Machine learning
originated in the 1940s, when researchers started working on basic pattern
recognition and the development of the first neural networks. This can be seen
in the case of Walter Pitts and Warren McCulloch, when in 1943 they developed
the first mathematical model of a neural network, establishing the foundation
for modern neural networks and eventually distributed machine learning systems.
Pioneers like
Donald Hebb, Alan Turing, and Arthur Samuel, while not the sole originators,
significantly advanced the evolution of machine learning. Hebb’s work on neuron
communication, Turing’s test for artificial intelligence, and Samuel’s coining
of the term “machine learning” all contributed to the burgeoning field of
artificial intelligence (AI) and laid the foundation for the numerous machine
learning algorithms we know today.
Recently,
Machine Learning has advanced dramatically through major breakthroughs such as DeepMind’s
AlphaGo victory in 2016, GPT-4 and Claude, transforming AI capabilities and
potentials across industries [3].
Context and Usage
Machine learning
is the most popular branch of AI technology in use today. Some of the most
common applications of machine learning which most of us have experienced in
our everyday activities are as follows:
- Autonomous cars and driver assistance enhancing all-round vehicle safety using features such as blind-spot detection and automatic braking.
- Speech recognition software that allows you to convert voice memos into text.
- Recommendation systems such as Amazon, Spotify and Netflix that suggest products, songs, or television shows tailored to your preferences.
- A bank’s fraud detection services automatically flagging suspicious transactions.
Why it Matters
Machine learning is crucial for understanding the ever-growing volume of data generated by modern societies. ML handles tasks beyond human ability to execute at scale, such as processing the huge quantities of data generated daily by digital devices. The ability to extract patterns and insights from vast data sets has become a strategic asset in fields like banking and scientific discovery. Also, the abundance of data humans create can be used to continuously improve and re-fine ML models, driving rapid progress in ML. Most leading companies today, such as Meta, Google and Uber, integrate ML into their operations to inform decision-making and improve efficiency [4].
Related Learning Approaches
- Machine Intelligence: Broad term for computer systems exhibiting intelligent behavior and problem-solving capabilities
- Reinforcement Learning: Learning approach where agents learn through trial and error using rewards and penalties
- Reinforcement Learning from Human Feedback (RLHF): Training method that uses human preferences to guide reinforcement learning
- Similarity Learning: Machine learning approach that teaches models to measure similarity between objects
- Singularity: Hypothetical point when AI surpasses human intelligence across all domains
In Practice
The on-demand music streaming service Spotify is a good example of a real-life case study of machine learning in practice. Machine learning algorithms associate your preferences with other listeners who have a similar musical taste, enabling Spotify to make decisions about which new songs or artists to recommend to you. Many services that provide automated recommendations, utilize this technique, which is referred to as AI [5].
References
- Crabtree, M. (2024). What is Machine Learning? Definition, Types, Tools & More
- Geeksforgeeks. (2025). What is Machine Learning?
- Akkio. (2024). History of Machine Learning: How We Got Here
- Craig, L., Tucci, L. (2024). What is machine learning? Guide, definition and examples
- Firican, G. (2025). The history of Machine Learning