Machine Learning
Machine learning (ML) is a branch of computer science that enables computers to learn from data.
The learning process involves the preparation of data sets, the selection of appropriate models and
learning algorithms, training the model with the respective data sets through the learning algorithm,
and the validation of the model. The produced model , which can be regarded as an approximation model,
is then used to make data-driven decisions on unseen novel data.

There are various classes of ML algorithms that are categorized according to the way they work.
Examples of the ML algorithms adopted widely in applications are: clustering, decision trees, and artificial
neural networks. Many effective techniques have also been studied to construct the models that are more
reliable and more accurate. The tools include regularization and ensemble algorithms.

ML algorithms have been successfully applied to a variety of problems: object detection/recognition,
speech recognition, image retrieval, medical diagnosis, and bioinformatics. Based on the class of a problem,
e.g., classification or regression, an appropriate ML algorithm is chosen and used to build an approximation model.
ML algorithms can provide us with a good model when problems have enough data. The more data, the better performance.
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