Machine learning follows a process of preparing data, training an algorithm and generating a machine learning model, and then making and refining predictions.

Preparing the data

Machine learning requires data that is analyzed, formatted and conditioned to build a machine learning model. Judith Hurwitz and Daniel Kirsch, authors of Machine Learning For Dummies, advise that machine learning requires the right set of data that can be applied to a learning process. Data preparation typically involves these tasks:

Training the algorithm

Machine learning uses the prepared data to train a machine learning algorithm. An algorithm is a computerized procedure or recipe. When the algorithm is trained on the data, a machine learning model is generated. Selecting the right algorithm is essential to applying machine learning successfully. Selection is largely influenced by the application and the data available. But there are some commonly used algorithms and applications:

Predicting and refining

Once the data is prepared and the algorithm trained, the machine learning model can make determinations or predictions about the data on its own. For example:

Consider a data set that has two basic values for cars: weight and speed. Values can be plotted on a graph that shows light cars tend to be fast and heavy cars tend to be slow.

When the machine learning model is provided with data about cars, it uses the algorithm to determine or predict whether a car will tend to be fast or slow, or light or heavy. It does this without explicit human intervention. And the more data provided, the more the model learns and improves the accuracy of its predictions.

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What is machine learning? | IBM