In the field of data and artificial intelligence, the term “learning” refers to the ability of an algorithm to improve its predictions through experience. In practice, the algorithm observes a dataset, extracts patterns, and builds a model that it can then apply to new data. The goal is not just to process existing information, but to create a general rule capable of adapting to future cases.

For businesses, learning is essential because it turns raw data into actionable decisions: forecasting demand, detecting fraud, segmenting customers, or anticipating risk.

Supervised vs unsupervised

Read the complete guide : https://docs.eaqbe.com/machine_learning/supervised_&_unsupervised

There are two main families of learning. Supervised learning involves training the algorithm with data for which the expected outcome is already known. We provide “complete” examples, with explanatory variables on one side and the target outcome on the other. The algorithm learns to reproduce this relationship and then applies it to new cases.

Unsupervised learning, by contrast, has no target variable. The algorithm explores the data without any guidance and seeks to uncover hidden structures: profile groupings, latent trends, or atypical observations. Each time it is applied to new data, it starts from scratch to generate a new model.

The difference can be summarized as follows: supervised learning answers the question “what will happen?”, while unsupervised learning answers “what can I learn from what I observe?”. In both cases, the objective for the business is greater visibility and anticipation, but the use cases differ depending on the data and business needs.

The supervised learning process

Supervised learning is the most commonly used approach in predictive data projects. It follows a series of steps that transform raw data into a usable model.

The first step is data selection. Explanatory variables describe the observed characteristics (age, income, purchase frequency…), while the target variable corresponds to the expected outcome (purchase or not, amount of spending, duration of loyalty…). This preparation assumes the data has been cleaned and organized to ensure quality and relevance.

The second step is to split the data into two sets. The first, called the training set, is used to “teach” the algorithm. The second, called the test set, is kept aside to later verify the model’s ability to generalize what it has learned. This split is essential to avoid the model simply memorizing the training cases without being able to adapt to new ones.

The third step is the actual learning phase. A statistical or algorithmic technique is applied to the training set. The algorithm builds a relationship between explanatory variables and the target variable. For example, in regression, it determines how factors such as size or location influence the price of a property. The result is a model that can be applied to new data.

A validation step follows immediately. This involves comparing the model’s predictions with actual outcomes on the training data. If the error is too small, the model may have memorized the training data, leading to overfitting. If the error is too large, the model has missed key patterns, resulting in underfitting. The right balance is a model that performs well but not perfectly on training data, a sign it can generalize.

The final step is testing on new data that has been kept aside. This phase is applied to records the model has never seen during training but for which the target variable is known. We can therefore directly compare predictions with actual results and assess robustness in near-real conditions. If the test performance is close to the validation performance, the model has learned a generalizable rule and can provide reliable results on new data.