What Is Machine Learning?

An exploration of the field and its applications

Robot calculating information given. Image provided by Getty and Forbes

Machine learning (ML) is a topic that has intrigued many for a long time. First invented by Arthur Samuel, ML refers to the intelligence a robot can process from information given. The use of ML can be categorized in two ways: to process information literally and to classify products.

ML has tremendous use in businesses; take, for example, a scanning processor. When you “feed” the machine information, such as pictures of the famed novel, “The Hobbit,” and then show the machine a picture of a different book, it will classify the book as “non-hobbit.” ML identifies close features of items and uses that information to differentiate materials.

ML has three processes: a decision process, an error function, and a model optimization process. In a decision process, ML algorithms make a classification or, sometimes, a prediction. Based on the input, the algorithm will estimate the similarity or extrapolate a pattern in the input to other data. An error function evaluates the classification of the model. This process involves rechecking if the process was correct and assessing the accuracy of the model. Lastly, the model optimization process handles weights that are adjusted to reduce the inconsistency between the known data and the imputed data. The algorithm will repeat this process, eventually updating information until correct data accuracy forms.

Furthermore, there are three types of ML: supervised, unsupervised and semi-supervised. Supervised ML defines datasets to train algorithms to predict outcomes correctly. When data is fed to the model, it self-adjusts to correct the data using comparisons. Unsupervised ML discovers hidden patterns without human intervention. It reduces the number of features and data needed to process objects more proficiently. Lastly, semi-supervised ML uses a smaller labeled dataset to classify larger datasets by extracting these smaller parts from a wider dataset to learn even better. It helps lessen the cost since it can process multiple data by just having bits of small datasets.

Many applications and businesses utilize ML to classify materials from each other and enable machines to view and process things as humans do. Although ML’s development is astonishing thus far, it will continue evolving, becoming more efficient, refined and accurate.