Machine learning (ML) starts with data – the raw material that fuels every model. Whether it’s images, text, sensor streams or financial records, the quality, diversity and preprocessing of that data shape what the algorithm can eventually learn. Cleaning, normalisation and feature engineering turn noisy inputs into meaningful signals that the system can recognise.
Once the data is ready, the algorithm needs a way to represent the underlying patterns. This is where architectures like neural networks, decision trees or gradient‑boosted machines come in. Each architecture encodes knowledge differently: deep nets stack layers to capture hierarchical features, while tree‑based methods split the input space into rule‑like regions. The choice of model dictates how the system “sees” and “decides” .
Training is the learning phase where the model adjusts its internal parameters to minimise prediction error. Through iterative optimisation—often gradient descent—the algorithm refines weights, learns from mistakes and gradually improves accuracy. Regularisation techniques, learning‑rate schedules and validation strategies keep the model from overfitting, ensuring it generalises to unseen data.
Finally, the trained model is deployed to make predictions, classify new inputs or generate content. In computer vision it may detect objects, in NLP it can translate languages, and in creative domains it can compose music or design art. Continuous monitoring and fine‑tuning keep the system relevant as data distributions shift, closing the loop from raw input back to actionable insight.
