What is a Machine Learning?
A way for computers to learn from examples instead of being given exact rules — by finding patterns in labelled or unlabelled data.
Machine Learning explained in plain English
Machine learning is how computers learn from examples instead of being told exact rules. You show the system many inputs paired with correct answers, and it gradually figures out the patterns on its own.
Once trained, the model can apply what it learned to new situations it has never seen before.
Analogy
Machine learning is like learning to identify birds by looking at thousands of photographs with names attached, rather than memorising a field guide written by an expert. Eventually you start recognising patterns — shape, colour, size — without anyone spelling out every rule.
Example
Credit scoring, product recommendations, and medical risk prediction are everyday applications built on machine learning.
How is Machine Learning used?
Netflix learns what you might want to watch from your viewing history. Email apps learn which messages are spam from millions of labelled examples. Voice assistants improve at recognising your speech the more people use them.
Common misconceptions about Machine Learning
Machine learning is not magic — poor data, biased examples, or the wrong problem setup can produce models that look impressive in tests but fail in practice.
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- Artificial Intelligence
Any system that performs tasks requiring human judgment — understanding language, recognising images, making decisions, or solving problems — by finding patterns in data rather than thinking or feeling.
- Backpropagation
The process that tells a neural network which internal settings caused an error and how to adjust them, working backwards through layers.
- Classification
The task of sorting inputs into predefined categories — choosing a label rather than producing a number.
- Embedding
A numerical representation of text, images, or other data that captures semantic meaning.
- Fine-tuning
The process of further training a pre-trained AI model on specialised data to improve performance on specific tasks.
- Gradient Descent
The method by which a model gradually improves by making small adjustments after each mistake, moving toward better performance.
- Inference
The phase when a trained model is actually used — taking new input and producing a prediction or response.
- Neural Network
A layered system that processes information in stages, with each layer detecting slightly more complex patterns than the last.
- Overfitting
When a model memorises training examples too precisely and fails to generalise to new data.
- Supervised Learning
Training a system using examples where the correct answer is already known, so it learns to map inputs to labels.